1 CHAPTER ONE INTRODUCTION Recently there ...

CHAPTER ONE INTRODUCTION Recently there has been an increasing interest in the phenomenon of climate change especially at the micro-level, as well as the associative issue of sustainability, indeed these issues have raised concern at the international level judging from the numerous gatherings and conventions in place such as Rio de Janeiro (1992), Kyoto (1997), Nairobi (2006), Copenhagen (2009), Mexico City (2010), and Bali (2012). The research community views knowledge on microtemperature change as important for addressing an environmental problem, whereby one must first understand the root causes and the mechanisms at play (United Nations Framework Convention on Climate Change, 2006). Despite the conviction, discussions at Lima (2014) had very little success in moving the agenda arrived at previously, indeed the only agreement reached was on “making progress in the next meeting”. At Paris 2015, parties agreed to holding the increase in the global average temperature to well below 2 degrees Celsius above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 degrees Celsius above preindustrial levels, recognizing that this would significantly reduce the risks and impacts of climate change (United Nations Framework Convention on Climate Change, 2015c, p.21). However, the Paris Agreement also required parties to the agreement to ratify, accept or approve by signature at the United Nations Headquarters in New York from 22nd April 2016 to 21st April 2017 (United Nations Framework Convention on Climate Change, 2015c, p.30). Developing economies such as the BRICS (A reference to the nations of Brazil, Russia, India, China and South Africa), need new energy sources to satisfy the hunger for fueling their growing economies and new requirements for their citizens (United Nations Framework Convention on Climate Change, 2015). The demand for fossil fuels (especially crude oil) and gases have been increasing, but 1

the supply from new sources and hitherto unprofitable supplies being tapped, has also been increasing, with the result that the fall in fuel prices has a negative effect for new consumers from lower income brackets, coming into the market. This trend is unsustainable and is leading to increased carbon dioxide and carbon monoxide emissions, and with devastating effects on the ozone layer, the higher crust of the earth’s atmosphere, given the increased greenhouse effect (Koenigsberger et al., 1973, p.37). In an effort to meet its industrialization and other targets, the BRICS Nations have maintained the status quo by continuing to use coal and other inefficient methods which exacerbate the above situation. Climate change seems to be inevitable and there is a growing interest in understanding urbanization problems such as urban heat islands, in turn has stimulated a detailed study of the effects of local climate change by researchers in the developed world who are looking for a regional formula for their cities (Oke, 1988). Increasingly, urbanization has become a challenging issue for the vast majority of African governments and planners. Although Africa remains the least urbanized continent it has lately displayed the fastest rate of urbanization in the world. At the moment, the African continent is experiencing the highest urban growth at the rate of 3.5 percent per year; this rate is expected to hold until 2050. On the down side, approximately 72 percent of urban residents in Sub-Saharan Africa live in slum-like conditions (African Population and Health Research Center, 2014, p.xvii). The absence of climate change studies in developing countries is restricting many of the decisions of made and applied by policy makers (Tayanc & Toros, 1997, p.Abstract) thus triggering a study of this nature. During the Lima (2014) Congress, for instance, most media houses claimed that 2014 had registered the highest increase in temperatures in recorded history. Some authorities may disagree on the unit of measurement, but most now agree that not only is the climate 2

changing, but also that the change is related to human activity (United Nations Climate Change Secretariat, 2015a). The negative effects on the earth’s overall climate balance is manifesting itself in the general increased temperature extremes, in a particular month, in one region, while another region may experience colder temperatures (Koenigsberger et al., 1973: 37). For instance, in 2014, Australia experienced increased incidences of forest fires due to the extreme dry weather that lasted for prolonged periods. Other countries such as Tokyo (Japan) and London (England) experienced periods of snow and ice in months that traditionally were expected to be milder (Aljazeera, 2014). The oceans and the arctic iceberg have also not been spared and marine life has been decimated due to the increased temperature and melting ice blocks which float downwards and affect the overall ocean currents (British Broadcasting Corporation, 2010). At the global level, issues such as carbon counts, depletion of the ozone layer and its associated harmful effects of cancer and other human afflictions, reduced vegetation cover, the build-up of carbon dioxide and carbon monoxide, other pollutants and dust particles, melting of the ice-caps on mountains and those at the Arctic and Antarctic poles, rising sea levels and changes in weather patterns, amongst many others, are cause for economic and health concerns in all nations of the world. Globalization and marginalization have resulted in developing nations, such as Kenya, being among the most adversely affected. Thus there is a need to study and put into place mitigation and prevention mechanisms at the local level to address such negative developments and their associated calamities (Shuckburg, 2007, p.6). Research into urban heat islands has indicated a definite relationship between the temperatures recorded in urban as compared to the rural areas, and related these to the key indicators of urban growth such as the relationship between temperature change and population growth (Makhoka & Shisanya, 2010). Kenya’s 3

population stands at 47,039,449, growing at a daily rate of 3,388 or one person born every five seconds (United Nations Statistics Division). Kenya is ranked thirtieth in the world in terms of population growth rate. Given the improved life expectancy and drop in infant mortality, the United Nations estimates that Kenya’s population will rise to 51.7 million by 2020 (Kenya Population Clock, 2016). Nairobi, the capital city of Kenya, has witnessed a massive growth in key urban indicators. Nairobi is currently the thirteenth largest city in Africa in terms of population, and the fourth largest based on infrastructure development and its area of cover (Kenya Laborum, 2016). Its population has increased from 120,000 when the first census was conducted in 1948 to 2,137, 570 in 1999 (Republic of Kenya, 1999), to 3,138,369 in 2009 (Kenya National Bureau of Statistics, 2009), and currently stands at 3.5 million residents in the City proper, with 6.54 million in the metropolitan (Kenya Population Clock, 2016). Nairobi grew at a rate of 5 percent per annum between 1969 to 1999, which represents one of the fastest city growth in Africa and is projected to grow even faster in the future (African Population and Health Research Center, 2014, p.1). Land surface temperature forms an important climate variable related to climate change and is an indicator of the energy balance at the surface given that land surface temperature is a key variable in the physics of the land surface process. A study using satellite images of Nairobi for the years 1986, 1995, 2002 and 2010 was used to derive land use land cover, normalized difference vegetation index and land surface temperature for the 24 year period of 24 years, as part of examining the dynamic effects of land use changes on land surface temperature (Mumina & Mundia, 2014, p.38). The study notes that urbanization is taking place with forest, plantations, shrubs, grassland and bare land giving way to built-up areas. The study suggests a negative correlation between the vegetation coverage and the land surface temperature hence indicating that a reduction in vegetation cover from bare land to 4

built-up areas will lead to increase in land surface temperatures (Mumina & Mundia, 2014, p.38). A manifestation of the temperature change includes the mists and fogs prevalent in the Nairobi’s upland climatic region and the change in rainfall patterns associated with the urbanization process. A study of atmospheric aerosols and the development of energy dispersive X-ray Fluorescence Spectrometer in Kenya, suggests that the visibility phenomenon is the most obvious human perception of air pollution and the agents of climate change (Gatari, 2006, p.11). A study of the urban heat island of Nairobi noted that the effect of temperature change on human health was an increase in incidences of heat stroke and mortality (Meffert, 1981). Another study on natural and man-made disasters and their effects on buildings noted that environmental degradation is often a factor in transforming a natural hazard or climate extreme into a disaster as related to climate induced disasters and ocean salinity, wind patterns and aspects of extreme weather including droughts, heavy rainfall, heat waves and the intensity of tropical cyclones on the wide spread changes in precipitation amounts (McDonald, 2003, p.16). An investigation of the positive influence of urbanization on the rainfall variability tower of the city of Nairobi identified an insignificant increase in the number of rainy days during 1969 to 1983 and further decrease during 1984 to 2008. However, the investigation observed a significant increase in the quantity of rainfall during the study period, attributed to anthropogenic activities in the city that increase the number of cloud condensation nuclei in the urban atmosphere necessitating rain formation (Ongoma Otieno & Onyango (2015, p.234). All these studies and indicators lend credence to a study of the air temperatures near the ground and built forms in the city of Nairobi. The aim of this study was to investigate the effect of urbanization and, principally, the urban built form on the micro-temperature change, in the study area. The findings of the study, 5

act as a valuable tool in future related studies attempting to seek possible solutions to the potential challenges posed specifically by temperature change at the local level and broadly, by environmental change at the global level, and further, to develop appropriate tools and measures for realizing sustainable built forms.

1.1 BACKGROUND OF STUDY This study embraced the ‘creating a research space’ model developed by Swales (1990), Kothari and Garg (2014, p.25) the five steps in defining a research problem in a systematic manner method by Rukwaro (2016, pp.16 – 17) and Mugenda (2011, p.133) problem statement presentation in the form of a principal proposition, interacting proposition, speculative proposition and the purpose and rationale of the study. The Swales Model recommended a first research move of establishing a territory, a second research move of establishing a niche and a third research move of occupying the niche. The properties of micro-temperature change are still not completely understood. Therefore, given the limited nature of the earth’s resources and the disparity of allocation, it behooves individual nations to negotiate carbon purchases in relation to natural vegetation cover and environmental audits. Such negotiations and agreements must result in the implementation and enforcement orders on climate regulation at national, regional and sub-regional levels for a meaningful addition at global level (United Nations Climate Change Secretariat, 2015a). Micro-temperature change forms a part of the broader discussion on climate and environment change, and further, forms a part of building science and thermal design taught in architectural schools. This is because temperature is the sensation that human beings associate with heat and spatial ambience (Allen Ed., 1985, p.774). Temperature can be conveniently measured with both analog and digital techniques and the documented evidence made available to relate it to thermal 6

comfort and its mode of comparison to a particular climatic region (Szokolay, 2011, pp.20 – 22). Some scholar’s assert that observations on the phenomena associated with micro-temperature change have either been misrepresented in terms of the relationship between built form and micro-temperature change (Shuckburg, 2007, pp.6 – 7; Littlefield Ed., 2008, pp.35.5 – 35.6 and Meffert, 1981, p.2), or that not enough research has been carried out to justify a value judgment on the degree of change or its causes. This study has interrogated these assertions in Chapter 2 (Literature Review). Several studies have suggested some aspects of the built form having an impact on the micro-temperature change, these studies include Gatari (2006, p.11) on aerosols and Shuckburg (2007, pp.6 -7) on land use change. The climate of an area and technological capability seem to have a positive long-term correlation with the architecture in place and in turn the built form, as depicted by the work of Capeluto (2002) on the hot and dry climate of Israel, Lam (2004) on the climate in China and Rosenlund (1995) on the desert climates. However, such a study area related to uplands climate of Nairobi is yet to be undertaken. While existing studies have clearly established that aerosols and other greenhouse gases have contributed to climate and general temperature change, they have not addressed the role of the built form, especially at the urban level and scale, on this change in climate. This study cites the work of Gatari (2006) on aerosols and the ‘Climate Change 2007’, the Fourth Assessment Report of the United Nations Intergovernmental Panel on Climate Change (Shuckburg, 2007) which offers an authoritative assessment of climate change implications for the tropics, where urbanization currently stands at approximately seventy percent in the industrial countries and varies within each of the less-developed countries. Nairobi’s built environment as the study region of concern is classified as tropical upland climate and corroborated meteorological data is available at the 7

Kenya Meteorological Department (1984, p.61, for the period including July 1984) and historically from the then East African Meteorological Department (1970) for the period 1959 to and including 1968. The Jomo Kenyatta International Airport is the largest airport in east and central Africa, and is located about 18 kilometers to the east of Nairobi Central Business District (Kenya Laborum, 2016). The Jomo Kenyatta International Airport Nairobi office of the Meteorological Department is the nearest meteorological station to the Komarock Estate study site and is listed as being on latitude 01o 18’South, longitude 36o 45’ East, with an altitude of 1798 metres above sea level. This study can now pose some basic questions such as, “why are temperatures at the micro-level higher in urban areas as compared to rural areas?” “By how much more are temperatures higher in urban areas compared to rural areas?” “What part does built form contribute to micro-temperature change?” The issue of the upland climate of the study area of Nairobi also has to be incorporated into the equation. The problem and challenges posed by micro-temperature change can be contextualized by demonstrating its manifestation, on the built form, in urban areas. In the early 1960’s, until the mid-90’s, the government concentrated on the provision of site and service schemes whereby citizens could purchase a plot or site and construct a house, with services such as water and electricity provided, and thereafter the property would be pursued as a single dwelling of either a single (villa) or double storey building, commonly referred to as a maisonette. Examples of such service schemes were: Dandora, Doonholm, Umoja, Buruburu and Komarock. This principle formed the thrust for similar schemes in other cities and towns of Kenya (Kimani & Musungu, 2010; Nairobi Urban Study Group, 1973 and Nairobi Urban Study Group, 1973a). There was a lag in this pace and mode of development until the elections of 2002, when the then National Alliance Rainbow Coalition (NARC) political party 8

won the election, with a clarion pledge to develop infrastructure , improve the living standards for all ; subsequently, the pledge was reaffirmed and enhanced by the 2013 government, top the current status of primarily flat, multiple occupancy and shared households, in order to meet the housing demands , reduce the urban sprawl and squatter settlements, hitherto associated with the growth of urban areas. In Kenya, 60 to 70 percent of the urban population lives in unplanned structures (African Population and Health Research Center, 2014, p.xvii), surviving on less than a dollar per day or Kenya Shillings 102 , based on current exchange rates (Kenya Bureau of Statistics, 1999 and Kenya Population Clock, 2016). The Architectural Association of Kenya estimates that only 30 percent of the structures in urban areas are designed and supervised by professionals thereby implying that 70 percent of the built forms are implemented by non-professionals, further, the Association estimates that the building industry is expected to construct 140,000 housing units, to add to the building stock per annum, for the next twenty years, if it is to meet the current housing deficit (Gakuru, 2006). These studies suggest an opportunity for structured neighbourhoods and the opportunity for research as suggested by the current study. The background of this study has established a research gap, based on the ongoing discussion on the effects, causes and implications of climate change. The study has established a gap in regard to the relationship between micro-temperature change and urban built forms. This study proceeds with presenting the problem statement, in order to close the gap.

1.2 PROBLEM STATEMENT Environmental and climatological considerations are used to influence the design and planning of built form, wherein, external air temperature affects

the

process of thermal dynamics and the internal comfort conditions (Hough, 1989, p.28). Urban temperatures are increasing (Littlefield Ed., 2008, pp.35.5 – 35.6) 9

through the phenomenon of climate change and the establishment of heat islands (Szokolay, 2011, p.75). However, the urban temperature has been generally increasing and as such the built form, by necessity, must adapt to these changes through the process of retrofitting, whereby, the existing structures are modified and there is transformation of the already built structures and neighbourhoods to suit the changing environment. This change has actually been carried out gradually by the users, through change of user applications and renovations an example of this is Komarock Estate. However, such changes are neither planned and by nature are not structured to take care of the temperature change. By using temperature data drawn from the predominantly open ground meteorological stations on climatic design, one notes that the built form in urban areas has failed to respond to the temperature changing environment (Environment & Urbanization, 2015, pp.163 – 164). With respect to planning of cities like Nairobi, the study notes that physical planners do not adequately take into account densification, as required by the physical planning regulations that are outlined in the planning handbook (Littlefield Ed., 2008 and Neufert & Neufert, 2000), Chapter 303 (Laws of Kenya, 1968) and Chapter 286 (Laws of Kenya, 2009) of the Land Planning Act. As a result of this failure to comply with planning guidelines and laws, the urban built form is not responsive to micro-climate, and hence temperature change. Casual observations of the built form have indicated that the building variables lack expressed ideas or concepts that link temperature change to structured neighbourhoods. For example, the non-structured neighbourhoods and structures have lacked meaningful and sustainable built form which would express design and planning strategies in a changing temperature environment. A pilot study established that two houses, with the same building style (villa), placed on different orientations, gave rise to different temperatures being 10

recorded in the open space adjoining the test cell, which was the living rooms. The question raised then is: what does one need to do in order to bring about that which would explain the cause and effect of temperature on sustainable built form? This study is an attempt to answer this question and to explore the urban climate change. Moreover the study focuses on the temperature changes close to the ground, i.e. at the spatial micro-level. The essence of the study is to identify the variables at work and to link such variables to sustainable development. Some basic questions can now be asked in relation to stating the research problem for the study; these questions include: “why and by how much more are temperatures at the micro-level higher in urban areas as compared to the rural areas?” And “what part does built form contribute to the temperature change?” Earlier studies on micro-temperature change have indicated that the built form lacked manifestations related to the changing temperature environment. These included the bio-climatic analysis of the climates of Kenya (Ebrahim Ed., 2010), thermal roof design for tropical provided housing (Ebrahim, 2008b), day-lighting performance of building elements (Ebrahim, 2008a), appropriate roofing and energy considerations for warm-humid climates (Ebrahim, 2008), sick-building syndrome and bioclimatic regional classification in Kenya (Ebrahim, 2011a), and diagnosing, remedial action and retrofitting techniques to sick building syndrome in upland climates (Ebrahim, 2011). There exists room for further research in the study area (Oliver, 1973, p.235). A study on micro-temperature change and urban built form crosses the boundaries of established disciplines and the modern environmental scientist needs a broad background in quite diverse disciplines to appreciate such a study. The aim of such a study is to facilitate comprehension of the relationships that exist and to promote a rational interpretation of climatic concepts as they relate to both natural and man-modified environments (Oliver, 1973, p.1).

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Again, it is evident that climate has a marked influence upon vegetation, but this is not a one-way relationship and it is equally evident that vegetation cover must influence climate. Such a reciprocal effect is evident at various levels of study where it is demonstrated that at the local level, vegetation has a marked impact upon the climatic regime at both the meso-level and micro-level (Oliver, 1973, p.164). Further, a house designed for a warm, dry area cannot be expected to function at greater efficiency, in a different climate. There is an obvious gap between house form and house function over wide areas (Oliver, 1973, p.228). Anyamba (2011, p.277) has asked the question, “Since architecture is always site specific can it be continent specific?” The proposed study posed a similar question “given that the built form for an estate and its manifestation on the micro-temperature change is site specific, can it be climatic region specific? Currently, there is no study that provides proper interpretation of urban built form in structured neighbourhoods such as Komarock Estate Nairobi and in particular the Infill B Phase of the estate (Environment & Urbanization, 2015, p.164). This study was an attempt to investigate why this is the case and further to examine how the built form can be harmonized with the changes in microtemperature.

1.3 RESEARCH QUESTIONS The study sought to answer the following research questions: i.

What are the urban built form variables which cause temperature change in the Komarock Estate study site?

ii. What influence do the significant urban built form variables have in contributing to the temperature change? iii. What impact do the urban built form design and planning strategies have on the temperature change? 12

1.4 OBJECTIVES OF THE RESEARCH The main objective of this study was to establish the influence of the urban built form on the micro-temperature change. It was therefore necessary to undertake the following tasks: i.

To identify urban built form variables causing temperature change in the Komarock Estate study site,

ii. To determine the influence of the significant urban built form variables in contribution to temperature change, and iii. To develop design and planning strategies in view of sustainable urban built form in a temperature changing environment.

1.5 JUSTIFICATION OF THE STUDY The major outcome of this study is that it provided planners and architects with planning and design models that integrate the emerging temperature change value system with the appropriate and sustainable urban built forms. This study was prompted by the need to provide the building science and architectural education lecturer with an appropriate prediction and remedial approach, addressing the impact of urban built forms on the micro-temperature change and which also meets the national and global standards. There is limited documentation on the relationship between urban built form variables and their relationship with the micro-temperature change, especially in structured neighbourhoods in a tropical upland climate such as the one prevalent in Komarock Estate Nairobi. In particular, there is lack of information on the significance of temperature control systems necessary and in the development of concepts concerning built form planning and design for a sustainable changing temperature environment. 13

The findings of this study are useful in providing identification of either misconceptions of theory or methodologies used by previous studies to solve a research problem, and thus, the compelling need to re-visit the problem and present a more credible position (Rukwaro, 2011, p.20). Existing literature on the causes and manifestations of the temperature change have not adequately addressed the issues which form the concerns of this study. This study thus elaborates on architectural and built form manifestation of temperature change in a tropical upland climate of an estate such as Komarock. A thorough review of literature on the study area, revealed that there were predictions; albeit, not exhaustively proven, with very little done on the particular subject of changing temperature and built form. working on a

Hooper (1975, pp.94 – 116)

Highland Zone and Koenigsberger et al. (1973, pp.229 – 233)

working on or shelter for tropical upland climates principally in rural areas, have carried out an assessment of climate and traditional shelter and made recommendations on material use, form and planning considerations. Givoni (1969, pp.278 – 343) provides general principles of design and choice of materials that takes into account buildings, the climate and the performance of building elements in those climates. None of these scholars however, have expanded on the issues of urban built form and its influence on the temperature change. This study aimed to fill this literature gap.

1.6 SIGNIFICANCE OF THE STUDY This study emphasized that the players in the built form arena have to incorporate the emerging temperature change value system into planning and design guidelines, if the latter is to be appropriate and sustainable. These value systems are based on predictive and remedial measures and are yet to be incorporated into legislation, building and planning control. Architectural training must be in tandem with technological advancement and the needs of the country’s socio-economic, 14

political and environmental changes. However, the training of architects has failed to define the flow of architectural knowledge between research, training and practice. It should be understood that the practical skills are derivatives of the real world, where the dynamics of ecology, economics, social and political parameters are key to the end products of architecture (Rukwaro, 2011, p.314). A study of this nature can guide architects on building materials and the manifestation of temperature change on such materials and further, how temperature change influences built form. In addition, this study will guide students of climatic design to understand the dynamics of temperature change in relation to the built form. There exists a problem of ineffective flow of architectural knowledge, which stems from the fact that architectural studies undertaken at the universities are not filtering back to practicing architects (Rukwaro, 2011, p.315). Research enhances theory development by formulating concepts and generalizing phenomenon and validating existing theories. This study therefore theorizes that urban built forms have to be sustainably designed and developed, based on their impact on the environment and specifically on the micro-temperature. The findings of the study provide the building science lecturer at university level, with appropriate tools and teaching materials to appreciate the impact of architectural choices. Typically, the dissemination of research findings consists of purpose, goal-oriented communication of information or knowledge that is specific and potentially useable. This, bridges the gap between research carried out by the academics, and the future practitioners who eventually implement the projects. Consequently, the stakeholders in architectural education will benefit from the study in that the findings elaborated, provide a strategy for addressing the conflicting issues, various design outcomes and possible design choices and thus paves way for architects and planners to make informed decisions.

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1.7 RESEARCH ASSUMPTIONS The purpose of these assumptions was to assist in determining how and why the phenomenon of micro-temperature change is related to urban built form, in the case of structured sections as is the case in Nairobi’s Komarock Estate and in particular in neighborhoods such as Infill B Estate. The result of the study contributes to understanding, identifying and determining the significant building and open space variables that cause temperature change and, eventually to suggest planning and design guidelines that realize sustainable urban environments. In effect, these variables are examined against the background of how they are translated into design and planning strategies (Rukwaro, 1997, pp.7 – 8). The study delineated four assumptions as listed below: i.

That factors of temperature change are interpretable into the urban built form in structured neighbourhoods;

ii. Urban built forms have variables which can be used to measure temperature change; iii. Temperature change is inevitable, in view of e world climatic fluctuations and there is need to develop sustainable urban built form; and iv. It is assumed that to measure the significant changes in micro-temperature, statistical elements must be recorded and analyzed using quantitative methods and graphic techniques.

1.8 SCOPE AND LIMITATIONS OF THE STUDY The physical scope of the study on the relationship between microtemperature change and urban built form is limited to the geographic urban setting of Nairobi, located on the main road from Mombasa on the Kenyan coast to the Uganda border town of Malaba. Nairobi is the capital city of Kenya; Figure 1.1 illustrates the position of Nairobi in relation to the other cities and towns of Kenya. 16

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Nairobi is approximately one degree south of the Equator, 37 degrees on the longitude line, and generally experiences a highland climate (Hooper, 1975, pp.94 116). The Nairobi County comprises three main districts: the Central Business District, Nairobi Core Nairobi and the Nairobi Metropolitan Region as indicated in Figure 1.2. Most of the structured neighbourhoods such as Buru Buru, Kayole and Komarock were built mainly towards the Eastern t of the city. A ring road encircles the Nairobi Metropolitan Region, with Komarock Estate being accessed from the Eastern Bypass, and onto the Kangundo Road. Due to time and financial constraints posed by the academic process, this study limited itself to a case study of a structured neighbourhood comprising two hundred and forty plots, mainly concentrating in an area called the Infill B Phase, within the Komarock area. Figure 1.3 indicates the location of Komarock Estate in relation to the Nairobi Central Business District and the Jomo Kenyatta International Airport. The Nairobi Metropolitan Region is reported to have an area of thirty two thousand square kilometers (UNEP Global Resource Information Database, Commission for the Implementation of the Constitution, 2010). The meteorological station located at the Jomo Kenyatta International Airport was used to set the baseline temperature and to establish the change factor in the study. Figure 1.4 indicates the location plan and context of the Komarock Infill B Estate in relation to the neighbouring estates and infrastructure such as Komarock Phase One and the community facilities. The infill estate is flanked by the Kenya Power and Lighting Company Limited way leave, Kangundo Road (New Komarock Road) which leads to Matungulu Town and which falls within the Proposed Nairobi Metropolitan Region also flanks the other counties and towns. A comparative study with other neighbourhoods or other estates in Nairobi would have been beyond the scope of this study and forms the case for future research.

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Discussion of micro-temperature was limited to the air temperature nearer to the ground; calculated at approximately one and a half metres above the floor level, reflecting and comparing well with the data provided by meteorological stations (Koenigsberger et al., 1973, p.13). Benchmarking data was carried out through using meteorological data provided by the Jomo Kenyatta International Airport (Kenya Meteorological Department, 1984, p.61). No attempt was made to collect or collate temperature benchmarking data. Normal standards and indices for thermal comfort were accepted and applied. Air temperature provide a measure of thermal comfort and the other variables related to thermal radiation, air flow and relative humidity which relate to thermal comfort were dealt with in a subjective way, through using the Bioclimatic Chart, as generally advocated by Koenigsberger et al. (1973, pp.50 – 51), in the application of the lake climate of Kenya by Ebrahim Ed. (2010) and by use of the Hygrothermal Comfort Scale as used by Meffert (1980, p.16) in the study of Lamu. Design standards used in the study included the Kenya Bureau of Standards (KBS, 2007) and the International Organization of Standards (ISO, 1995, 1996 and 2001) which indicate the standards necessary for maintain the thermal comfort of a space (Koenigsberger et al., 1973, pp.47 – 51). Theoretical delimitations for the study included: the heat balance equation (Koenigsberger et al., 1973, pp.75 – 76), radiative excess temperature (Meffert, 1981, p.2) and sol-air temperature concept (Koenigsberger et al., 1973, p74). The universal climate change agreement used in the study was the United Nations Climate Change Secretariat (UNCCS: 2015a), rated by the Paris Climate Agreement. The study focused on a longitudinal research design whereby temperature data was collected using the observation method, the research tools used included observation sheets, checklists and tabulations from 30 sampled plots and 16

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sampled open spaces, roads or paths during the period 8th June 2013 to 19th September 2015. The present day digital tools are by their nature very general and therefore were deemed inappropriate for use in the current study. Therefore original tools and techniques had to be designed and tested prior for their use in the study, this is captured in Appendix 1 (Standard tabulated observation sheets), Appendix 2 (Tabulated data) and Appendix 3 (Table of drawings). Appendix 1 was referred to in the chapter on Research Methods, and especially in the section detailing data collection methods. Appendices 2 and 3 on tabulation of data and table of drawings were cited respectively, in the discussion, data analysis and presentation. The digital and analogue tools used in the study included: the design and development of Temperature Template for bioclimatic analysis in tropical countries (Ebrahim, 2010d), digital tool development in Kenya (Ebrahim, 2010c), Ebenergy Software (Ebrahim, 2010) and the design and development of Ebstats Software (Ebrahim, 2015). Other tools such as the Stata Software (Kiel, 2015), Excel (Gottfried, 2002) and the data loggers (Onset HOBO Data Loggers, 2007) used in the study, do exist. There was no intention or attempt to alter or develop the data analysis program or the measurement equipment. Nevertheless, some shortcomings of the tools used, in relation to either the quantity or quality were recognized and are described, with the hope that the authors or suppliers of such tools will address the shortcomings.

1.9 DEFINITION OF TERMINOLOGIES The following terminologies were used in the study: Baseline is a controlled measurement carried out before an experimental treatment and is mainly used for benchmarking of a research variable (University of South Carolina, 2014: Glossary of Research Terms). 22

Baseline climate is a concept used in the study to compare the diurnal and annual changes to the outside air temperature. Temperature readings were compared with those of the meteorological station within the region, which happened to be the meteorological station located at the Jomo Kenyatta International Airport. By using this station data, the study, in effect, cancelled out the effect of altitude, longitude and latitude variable in the equation. It also firmly lodged this climatic region as urban upland, with Nairobi as the base. Building is any structure for whatever purpose and of whatever materials constructed, and used for human habitation or for any other purpose (Singh & Singh, 2010, p.96). Building Code is a document that the Ministry of Local Government (Republic of Kenya: The Local Government Act, 2010) mandates the Nairobi County Council (NCC) to prepare, such as that of 1968 for Adoptive Building and Structures (Republic of Kenya: Building Code, 1976). The Building Code is currently under review and draft copies have been circulated to stakeholders in the building industry for comments and contributions prior to its adoption. Building height for a pitched roof is the vertical distance measured from the average level of the centre line of the adjoining street to the point where the external surface of the outer wall intersects the finished surface of the sloping roof (Singh & Singh, 2010, p.96). Building line is the line up to which the plinth of the building, adjoining a street, may lawfully extend and is also referred to as the building frontage or building setback (Singh & Singh, 2010, p.97). Built relates to man’s attempt to modify his environment to suit his basic needs and ideologies pertaining to comfort, perception and beliefs (Koenigsberger et al., 1973, p.41), and falls victim to the underlying design issues related to society, technology, environmental and in particular, the climate (Olgyay & Olgyay, 1963).

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Built forms are a result of designers and planners adhering to the building and construction process regulated by building and planning standards and legislation. Built form standards can be locally set or borrowed from internationally recognized institutions. Local standards are established by the Kenya Bureau of Standards (2007), while international standards can be obtained from the metric handbook for planning and design data (Littlefield Ed., 2008), Neufert architects’ data (Neufert & Neufert, 2000) and ISO Standards, amongst others. Cities are creations of man, arising from the necessity to suitably locate people and their activities, based on micro-climate and meso-climatic factors in the region (King’oriah, 2013, p.342). Climate is the integration in time of the physical states of the atmospheric environment, characteristic of a certain geographical location (Koenigsberger et al., 1973, p.3). At Komarock Estate the classification of climate entailed the averaging of regular weather data collected from sampled plots and open spaces in the form of air temperatures in order to determine a weekly mean and eventually, the monthly temperature data. Data loggers are the digital equivalents of the analogue thermometer, and were made available for the study by the Department of Architecture and Building Science (University of Nairobi) from the supplier, M/s Onset HOBO Data Loggers (2007). Detached house is a house that has open land around it. Discretionary powers is a reference to the power of some ministries of the central government in Kenya, to allow certain bodies to pass by-laws, prepare development plans and growth strategies which control development (Harvard University Graduate School of Design and University of Nairobi (2007), Erring & Ismail (1980), King’oriah (2013 and 1980), Nairobi Urban Study Group (1973) and

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(1973a), Thornton White et al. (1948), and Singh and Singh (2010, pp.4 – 54 and pp.95 – 133)). Flats have three to seven storeys and each floor may have two or more tenements. In Kenya, it is a requirement for flats with more than five floors to make provision for a lift. As such, for middle-class housing, most developers would restrict the height in order to comply with the law and as a cost-cutting measure. Floor area is the usable covered area of a building at any floor level (Singh & Singh, 2010, p.98). Form is related to shape, arrangement of parts and the visible aspect of an entity (Allen Ed., 1985, p.290), this is the physical manifestation related to the entity and in the case of the study the built form shapes, sizes, colour, dimensions, densities, composition, attributes and general logistics. Habitable residential room is a room occupied or designed for occupancy by one or more persons as study, living, sleeping, eating, kitchen, area etc., and does not include corridors, toilets, etc. (Singh & Singh, 2010, p.101). Legislation in Kenya is either the acts of parliament or annexed bylaws set by county governments and related and approved by various ministries on behalf of the central government or the state. According to the Commission for the Implementation of the Constitution (2010) the following building and planning laws of Kenya would be relevant and in use for building and land development: the National Land Commission Act (2012), the Land Act (2012), Land Consolidation Regulations (1989), the Building Code (1968), the Housing Bill (2009), Environment Management Act (1999), Environmental Impact Assessment and Audit Regulation (2003), Constitution of Kenya (2010), Local Government Act (1986), and Kenya Urban Areas Bill (2011). Macro relates to large or large-scale (Allen Ed., 1985, p.440).

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Macroclimate relates to the climate described for the region, and is information published by the nearest meteorological observatory – i.e. climate for the region, settlement or planning level (Koenigsberger et al., 1973, p.31). Maisonette is a double-storeyed house which formed the bulk of building types prevalent at Komarock Infill B Estate. Figure 1.5 indicates plot details for maisonette. Meso relates to middle or intermediate (Allen Ed., 1985, p.461). Meso-climate would relate to the part of the estate climate or building level (Koenigsberger et al., 1973, p.31). Micro relates to small (Allen Ed., 1985, p.463). Microclimate which is sometimes referred to site climate can imply any deviation from the climate of a larger area; every city, town or village and even precinct in a town may have its own climate – i.e. plot climate or building element level (Koenigsberger et al., 1973, p.31). Designers are interested specifically in those aspects of climate which affect human comfort and the use of buildings, which includes averages, changes and extremes of temperature, the temperature differences between day and night (diurnal range) amongst other climatic considerations. Micro-temperature as related to the context of the study was the measurement of the air temperature, and the data collected at one and a half metres from the ground. The data loggers were stationed in an open space immediately next to the living room, office or shop of the plot or open space to be measured, in order to reflect and incorporate the methods and techniques used by the local meteorological station. Recorded outside air temperature (To), were processed to a single figure hereafter called the dependent variable micro-temperature change for that plot or open space in degree Celsius (oC).

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Micro-temperature change can be equated to outside air temperature (To) and the change (Δ) of that temperature. Thus outside air temperature change (ΔTo) or simply (ΔT) will denote changes to outside temperatures. Micro-temperature can be that aspect of temperature related to microclimate (Koenigsberger et al., 1973, p.13). Neighbourhood is related to a district or vicinity (Allen Ed., 1985, p.482) and lends well of the concept of the city image and its elements advocated by Lynch (1960). Within this neighbourhood buildings and structures of diverse styles, classifications and logistics would exist. The study used Singh and Singh’s (2010) guiding principle of town planning such as green belt, housing, public building, recreation centres, road system, transport facilities and zoning. Their starting point being housing and residential buildings, and here, they include detached houses, semi-detached houses, row of houses, apartments or flats and skyscrapers (Singh & Singh, 2010, p.6, p.27 and pp.96 – 103). Occupancy is also known as the use group, it represents the main purpose for which a building or part of a building is used or intended to be used (Singh & Singh, 2010, p.99). Residential occupancy is one class of building in Kenya that one would need to get a change of user license in order to change the usage. The usage can also be of a single nature or of multiple uses. Open space is the integral part of the plot, left open to the sky line (Singh & Singh, 2010, p.98). Figure 1.6 indicates open ground details. Plinth area is the built-up covered area measured at the floor level (Singh & Singh, 2010, p.98). Plot also called the site is a piece of land enclosed by definite boundaries (Singh & Singh, 2010, p.99). Plots with two sides adjoining and with intersecting streets are called a corner plot. Row of houses has two walls in common with adjacent house also known as party walls. 28

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Room height is the vertical distance measured from the finished floor surface to the finished ceiling surface (Singh & Singh, 2010, p.97). Rural land usage is classified as a function of the physical environment, which comprises farmland, forests, national parks and unused land such as deserts, scrub-land, etc. (King’oriah, 2013, p.297). Semi-detached houses have one wall in common with the adjacent house. Skyscrapers are buildings of more than seven storeys and cannot be allowed by the zoning laws in operation within Komarock Infill B Estate. Structure and structured refers to the manner in which a thing is constructed, supporting framework or essential parts (Allen Ed., 1985, p.746). Structured neighbourhood in a sense implies a planned environment that is complying with the relevant laws of the land. Structured and planned buildings especially in relation to a town would consist of an arrangement of different components or units in such a manner that the town attains the significance of a living organism (Singh & Singh, 2010, p.4 and p.10), according to the master plan of the town (Kimani & Musungu, 2010) and further in the case of planned buildings in Kenya, comply with various laws and acts of parliament (Laws of Kenya, 2012, 2010, 2009 and 1968, and Republic of Kenya, 2014, 2012, 2012a, 2010 and 1976). Surrogate refers to a deputy, especially of a bishop or substitute (Allen Ed., 1985, p.757), and is a set of observable attributes, characteristics or behavior that can be used to represent an abstract concept, variable or idea (Mugenda & Mugenda, 2012, p.321). Surrogates were used in the study because it was difficult for the researcher to agree on a working definition of certain variables related to the built form such as the building type, building classification, etc. Also, built form was a combination of several other concepts. For example, building type as a variable can only meaningfully be defined reference to a combination of several measureable attribute such as building height in metres. 30

Temperature is the degree or intensity of heat of a body in relation to others, especially as read from a thermometer or perceived by touch (Allen Ed., 1985, p.774). Tropical upland climate are mountainous regions and plateau of more than 900 to 1200 metres above sea-level that experience climates, between the two twenty degree Celsius isotherms. Nairobi is an example of a city falling within this climatic region (Koenigsberger et al., 1973, p.30). Hooper (1975, p.96) refers to this region as Highland Zone. The nature of the upland climate is in many ways similar to the composite or monsoon climates, with its distinct rainy seasons. It is dominated by strong solar radiation, often with moderate to cool air temperatures. Even in the warmest part of the year, the air temperatures rarely exceed thirty degree Celsius. However, the diurnal variation can be as much as twenty degree Celsius. There is a marked reduction in temperatures in upland climates that are further away from the Equator. Humidity is not excessive and there is an almost constant never very strong air movement (Koenigsberger et al., 1973, p.229). Urban is the opposite of rural and relates to living or situated in a town or city (Allen Ed., 1985, p.832). Urban areas in the East African context have populations exceeding two thousand and are regarded urban as long as they perform the functions of administration and protection, social services, communication and transport, commerce, industry and power (King’oriah, 2013, p.298). This study identifies the geographical setting in order to explore the phenomenon of urban climate change on the Komarock Infill B Estate in the city of Nairobi. Urban built form is an acronym of three verbs, with governments and United Nations Agencies providing a distinction of rural and urban areas based on logistics of population, it is associated with the challenges posed by rural-urban migration, urban growth, densities and patterns (Mugenda & Mugenda, 2012, p.341 – 342: Urban ecology and urbanization). Villa is a single-storeyed house. Figure 1.7 indicates plot details for a Villa. 31

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Weather is the momentary state of the atmospheric environment at a certain location (Koenigsberger et al., 1973, p.3).

1.10 ORGANIZATION OF THE STUDY The thesis is divided into six chapters in line with the theme and essence of the study, which was: understanding and interpretation of the relationship between the independent variables of urban built form and the dependent variable of microtemperature change. Each chapter is designed to reflect on the objectives of the study and the study questions, the arguments and presentation of methods and tools, concepts, principles, theory and its application with a general flow from the initial impression to drawing of results, findings, summations and linkage to the next chapter. The subheadings address those concepts, principles, flow of ideas and the essence of the chapter, in a logical order. Chapter one, the introduction provides a background of the study, problem statement, research questions, objectives of the research, justification and significance of the study, research assumptions, the scope and limitations of the study, definition of terminologies and organization of the study. Chapter two is the review of related literature, has sub-headings discussing urban built form and temperature change, significance of urban built form variables and temperature change, sustainable urban built form and microclimate, application of design and planning strategies in a temperature changing environment, theoretical framework, conceptual framework, conceptual definition of variables, operational definition of variables, conceptual model and hypothesis of the study. Chapter three on research methods has sub-headings that discuss the research design and methodological framework, longitudinal research design, data sources, research tools, observation method, sampling design, plot attributes, planning and design attitudes, sampling technique, batching design and cluster sampling, collection, processing and preparation of data, data analysis and methods, 33

data analysis techniques used in the study, graphic representation of data used in the study, use of digital statistical analytical tools in the study, data tests and analysis conducted in the study, summarized analytical framework and a reflection on the research methods. Chapter four on results has sub-headings that discuss sampling plot attributes, sampling planning and design attitudes, subjects, urban built form and impact on micro-temperature change, the prevalence of urban built form factors, statistically inferred urban built form causes of micro-temperature change, statistically inferred urban built form factors provoking micro-temperature change, types and prevalence of urban built form intervention on micro-temperature change, prompts and barriers on sustainable urban built form in a temperature changing environment and a reflection on the results. Chapter five on synthesis and interpretation of findings has sub-headings that discuss urban built form variability and trend, micro-temperature change variability and trend, urban built form and micro-temperature change relationship, and a reflection of the findings. Chapter six on conclusion and recommendations has sub-headings that discuss the purpose of the study, summary of the findings, results of hypothesis testing, philosophy statement, limitations of the findings, conclusion, implications of the study in practice and theory, recommendations and suggested areas for further research.

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CHAPTER TWO REVIEW OF RELATED LITERATURE The first objective of the study was to identify urban built form variables causing temperature change in the Komarock Estate study site. In order to achieve this objective, the study reviewed related literature in relation to urban built form and temperature change. In addressing the second research objective, the study reviewed related literature on the significance of urban built form variables and temperature change, sustainable urban built form and microclimate. In addressing the third research objective, the literature reviewed was in relation to the application of design and planning strategies in a temperature changing environment. Chapter two, the review of related literature, proceeds with a review of literature and the development of the theoretical framework, conceptual framework, conceptual definition of variables, operational definition of variables, conceptual model and hypothesis of the study on the relationship between micro-temperature change and the urban built form variables.

2.1 URBAN BUILT FORM AND TEMPERATURE CHANGE In order to identify the built form variables that have an influence on temperature change, one has to first understand and review literature related to the role that climatic design plays in the planning and design of built forms. Climate, as a modifying factor, plays an important aspect in the form-generating forces and has major effects in the forms that human beings may wish to create for themselves (Rapoport, 1969, p.83). In this case, built form is dependent on climate and in effect the temperature regime of a place. A relationship exists between the climate and temperature variable with the built form variables associated with the building type, open spaces and commercial activity. Research work carried out in 1927, in the temperate climates, using rudimentary equipment, revealed that the total transformation of natural landscape into houses, streets, squares, great public buildings, skyscrapers and industrial 35

installations had brought about changes of climate in the large cities (Geiger, 1975, p.489). The basic reason for the differences found in city climates in temperate regions was as a result of the alteration of the heat and water budgets, atmospheric pollution, green areas and hard landscape, chemically active solid and gaseous products (Geiger, 1975, p.489). Building variables identified were hard materials use and the size of the buildings, while open space variables were shading coefficient, hard landscape and size of the open spaces. Urban climates differ from those of rural areas and the magnitudes of the differences can be quite large at times, depending on weather conditions, urban thermos physical and geometrical characteristics, and anthropogenic moisture and heat sources present in the area, as based on studies of urban climates and heat islands in various cities in the United States of America (Taha, 1997, pp.99 -103). Weather condition is aggregated over time to form the climate of a region. Urban thermo physical characteristics can be expressed in the three root words: urban, thermo and physical characteristics. Urban is a reference related to living or situated in a town or city (Allen Ed., 1985, p.832). Thermo is related to heat (Allen Ed., 1985, p.780) and by implication, temperature. Physical pertains to the laws of nature and physics (Allen Ed., 1985, p.554), and characteristics, relates to typical or distinctive features or quality. Thus, urban thermo physical characteristics would suggest the application of physics in the understanding of a city or towns thermal features. The geometrical characteristics of a place are made up of geo, which relates to the earth (Allen Ed., 1985, p.308), while metrical involves measurement (Allen Ed., 1985, p.462). Geometrical characteristics would suggest the earth’s distinctive measurement, and by implication, the scale of the earth’s surface. Urban pollutant concentrations can be ten times higher than those of the clean atmosphere and air temperatures can, on the average, be two degrees Celsius higher. The simulations suggest that reasonable increases in urban albedo can achieve a decrease of up to two degrees Celsius in air temperature, and with 36

extreme increases in albedo, localized decreases in air temperature, under some circumstances, can reach four degrees Celsius (Taha, 1997, pp.99 -103). A heat island can occur on range of scales. Heat islands can manifest around a single building, a small vegetative canopy, or a large portion of a city. Heat islands may be beneficial or detrimental to the urban dweller and energy user (Taha, 1997, pp.99 -103). The causes and effects of urban climates and heat islands are diverse and their interactions complex. Results from meteorological simulations suggest that cities can feasibly reverse heat islands and offset their impacts on energy use simply by increasing the albedo of roofing and paving materials and reforesting urban areas. The effects of anthropogenic heating, however, seem to be relatively small. The simulations indicate that the impact of anthropogenic heating may be important in urban centres but be of negligible importance in residential and commercial areas (Taha, 1997, pp.99 -103). Anthropogenic moisture and other pollutants originates from human activity such as the burning of fossil fuels rather than processes such as respiration and decay on nature and heat sources present in the area (Montello & Sutton, 2013, p.14 and p.17). The actual impact of urban climates and heat islands, on the inhabitants of these physical environments, depends on the characteristics of the local climate and its manifestations on built forms, to ameliorate the effects of the climate change. Generally speaking, low and mid-latitude heat islands are unwanted because they contribute to cooling loads, thermal discomfort, and air pollution whereas high latitude heat islands are less of a problem because they can reduce heating energy requirements (Taha, 1997, pp.99 -103). Field monitoring data and meteorological simulations indicate that changes in surface albedo and vegetation cover can be effective in modifying the nearsurface climate (Taha (1997, pp.99 -103). Identified built form variables were surface albedo, evapotranspiration from vegetation, and anthropogenic heating from mobile and stationary sources as causes of urban climate change. 37

2.2 SIGNIFICANCE OF URBAN BUILT FORM VARIABLES AND TEMPERATURE CHANGE Climate change records have been kept by international organization. The Intergovernmental Panel on Climate Change (IPCC) was set up in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP) as a forum for states to tackle the issues surrounding climate change. IPCC initial findings included rise in global surface temperatures, up by 0.75 degrees Celsius since the twentieth century. Rapid rises were noted in the last fifty years. Noted dates were 1998, 2002, 2003, 2004, 2005, etc. with increases of global average air and ocean temperatures, widespread melting of snow and ice, rising global mean sea level, and atmospheric concentration of carbon dioxide and methane ,over the last 10,000 years (Shuckburg, 2007, p.6). International conventions recommend micro-temperature change thresholds that nations should maintain. Studies in the temperate climates have used computers which predicted an increase of 0.15 to 0.3 degrees Celsius compared to 0.2 degrees Celsius observed per decade. Beyond the next two decades, the predictions of future climate change depends on the assumptions made in terms of future greenhouse gas emissions and indicate a bleak future equivalent to the great ice age experienced 120,000 years ago (Shuckburg, 2007, pp.6 – 7). Climate change records have also been kept by special unit setup by various universities. The Climatic Research Unit at University of East Anglia, Norwich recorded and simulated history by using seventeen sets of data to build up a record of average summer temperature for both hemispheres from 1000 to 1991, of the 17 sets, 10 were for the northern hemisphere, and five sets were for Sweden, Siberia, Alberta and Idaho. Ice core data was two sets for Greenland and one set for Spitzbergen. Average temperatures were higher in 1000 and 1998, with the highest recorded temperatures being experienced in Russia and Alaska. Temperatures of

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over forty five degrees Celsius were recorded in Central Australia, the Gulf States and Sudan (McDonald, 2003, p.102). Temperature changes experienced in urban areas were attributed to causes which include: the destruction of eco-balance through deforestation, urbanization and desertification, unchecked emissions of greenhouse gases, man-made materials and processes requiring high-energy consumption, human climate or environmental interventions, harmful effects of new products, harmful by-products of manufacturing processes through radiation and pollution, and the failure of production, transportation and technological systems (McDonald, 2003, p.16 and p.109). Climate change records assist in the articulation theories of temperature change, within the context of urbanization, and, extracting the significant variables at play as advances in theory. Recommended variables here include: the Sun’s position relative to the building in regards to the solar altitude and azimuth, site orientation and slope, existing obstructions on site, the potential for overshadowing from obstructions outside the site boundary, grouping and orientation of buildings, road layout and services distribution, glazing types and façade design, nature of internal spaces into which solar radiation penetrates, insulation capacity and resistivity of materials (McDonald, 2003). Land surface temperature seems to be related to the urbanization process. Indeed, increase in the urbanization process lead to the replacement of natural surfaces and a continuous increase in artificial land use local cover in form of roads, buildings and other anthropogenic surfaces, making it impervious (Mumina & Mundia, 2014, p.41). The continuous and enormous changes of the land use local cover, and the urban sprawl, encroachment and destruction of the ecosystem in the urban green space has led to the increase in land surface temperature intensity, thereby suggesting the impact of the built forms on the temperature change (Mumina & Mundia, 2014, p.41). 39

Temperature change seems to be related to population growth. Given the estimation that the world’s population living in urban areas is likely to increase significantly over the coming years, and further, the belief that the highest growth occurs in the developing world; it is most likely that the problem in urban areas will be an increase in surface temperature as a result of continuous alteration and conversion of previously pervious surfaces to impervious surfaces. The changes will cause environmental impacts with air pollution being a factor, contributing to global warming increasing the surface temperature (Mumina & Mundia, 2014, p.41). Other temperature changes affect the absorption of solar radiation, evaporation rates, surface temperature, the storage of heat and wind turbulence; all of these conditions fit within the urban island heat phenomenon (Mumina & Mundia, 2014, p.41). The findings on temperature change may assist s planners and architects to make informed decisions on the design of sustainable built form. Of particular importance to the county government of Nairobi, is the ability to take action by drafting policies to further control the land use local cover changes so as to minimize and reduce their impacts, thus mitigating the urban micro-climates (Mumina & Mundia, 2014). The recommended action is the introduction of green building as well as the adoption of measures that ensure the continuous preservation of Nairobi’s green corridors and space (Mumina & Mundia, 2014). Table 1.1 (Appendix 1: Standard tabulated observation sheets) indicates a list of micro-temperature change, urban built form and other variables, identified as significant in the review of related literature.

2.3 SUSTAINABLE URBAN BUILT FORM AND MICROCLIMATE A review of literature related to the relationship between sustainable urban built form and microclimate would assist the study to interrogate the relationship between urban built form and micro-temperature change and in the determination of 40

the influence of significant urban built form variables that contribute to the temperature change. Temperature change seems to be associated with environmental degradation. Environmental studies, which emerged formally as a discipline in the 1960’s is concerned with the impact of human activity on the environment. Studies suggest that human activity is responsible for the evident pollution of air, land and water, the continued destruction of wildlife habitats around the world, and the concomitant loss of biodiversity (Montello & Sutton, 2013, pp.14 – 15). Research is required on those aspects of built forms which contribute to environmental pollution, reduced green areas and the sustainable mix of hard and soft landscapes. Public interest in environmental degradation, and concern about its impacts on nature, and human health have increased, leading to governments in developed countries enacting legislation to regulate human actions that have adverse consequence on the natural environment. In Kenya, the Environment Management Act of 1999 and Environmental Impact Assessment and Audit Regulation of 2003 require an environmental impact assessment to be carried out whenever individuals or developers want to modify land cover and use (Commission for the Implementation of the Constitution, 2010). Legislation and regulatory mechanisms therefore ensure that research findings permeate into practice. Advocacy in environmental studies would reduce the impact of temperature change. Research should not only teach how the world works, it should help citizens to reduce detrimental impacts on the environment (including plants and other animals) and increase beneficial impacts (Montello & Sutton, 2013, p.15). All stakeholders in the climate change stage need to play their part in ensuring a sustainable future for all.

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2.4 APPLICATION OF DESIGN AND PLANNING STRATEGIES IN A TEMPERATURE CHANGING ENVIRONMENT In order to develop design and planning strategies, in view of sustainable urban built form in a temperature changing environment, literature was reviewed and related to the application and impact of design and planning strategies in different settings. The review of literature on the application of climate change legislation provided lessons on the relationship between built from and the temperature changing environment. Studies on understanding occupant heating practices in dwellings in the United Kingdom, evaluated the impact of the 2008 Climate Change Act, the 2010 Green Deal and the World Health Organization (WHO) which suggest that dwellings be heated at indoor temperatures of 21 degrees Celsius in the living room and 18 degrees Celsius in bedroom spaces. The studies carried out for 290 households in Leicester (UK) elaborated on how heating households at the requisite indoor temperatures promoted the health and wellbeing of the occupants of buildings (Kane et al., 2011). All the studies reported that households were heated at lower than the recommended temperatures of 21 degrees Celsius. The Climate Change Act committed the UK Government to reduce carbon dioxide emissions by 80 percent, of the 1990 levels by 2050, while the Green Deal ensured that householders were given a loan to make energy efficient improvements to their properties and were expected to make repayments using money saved from lower energy bill (Kane et al., 2011, pp.1 – 7). A symbiotic relationship can exist between built forms and temperature change. Reducing the energy used for space heating is a challenge as it is related to the technical performance of the building and its heating systems, as well as the behavior of occupants. A study, to relate house type to indoor temperature (Kane et al., 2011, pp.1 – 7, used data loggers to monitor air temperature every hour between July 2009 and March 2010 on building types which included detached, flat, semi42

detached, mid-terrace and end-terrace. According to the study, the building type of the built form variable seems to have a relationship to temperature change. Temperature profiles indicated that on average, flats had higher indoor temperatures than other house types. Further analysis is required of this data set in order to fully address why these properties have low temperatures during periods of occupation (Kane et al., 2011, pp.1 – 7). An earlier study of 224 English dwellings, conducted between 22nd July and 31st August 2008, revealed that overall, living rooms had an average temperature of 21.4 degrees Celsius, bedrooms

had

an average temperature of 21.5 degrees

Celsius and the average external air temperatures over the period was 15.3 degrees Celsius (Firth & Wright, 2008). The study above, found that : purpose-built flats and end terraces had the highest average summer temperatures and the greatest potential for overheating, post 1990 dwellings had the highest average temperatures and were more likely to overheat, average summer temperatures in dwellings were highest in the evenings (17:15 to 23:15) and lowest in the morning (06:45 – 09:00). A discussion ensued on the relationship between internal and external temperatures, with the emphasis that that further study on the analysis of the two was necessary (Firth & Wright, 2008, p.1).

2.5 THEORETICAL FRAMEWORK The Theoretical framework for the study on the relationship between microtemperature change and urban built form was a collection of interrelated concepts on what form does the structure take, and to determine what to measure, and what statistical relationships to look for (Rukwaro, 2016, p.25). A theory or theoretical framework is a structure for explaining phenomenon; it states the constructs and the laws that inter-relate these constructs to each other. A construct is a concept, abstraction or idea drawn from the specific, while a framework refers to the main structure or skeleton that not only gives form and shape to the whole system, but 43

also supports and holds together all the other elements in a logical configuration (Mugenda, 2011, p.11 and p.34). The study presents the theory explaining why the problem under study exists , it starts-off with the theoretical framework developed for the study , critically reviews the theories advanced by proponents who have handled a similar problem in order to develop a viable theoretical framework. Temperature and climate of a region has a profound impact on the design of sustainable built form. In essence, design temperature (TD) standards which are set by authorities such as the Kenya Bureau of Standards (KEBS) and International Organization of Standards (ISO) are necessary for maintaining the thermal comfort of a space (Koenigsberger et al. 1973, pp.47 - 51). Design temperatures are also used in the heat balance equation (Koenigsberger et al. 1973, pp.75 – 76) for an envelope as an element of built forms as follows: Qi + Qs ± Qc ± Qv ± Qr ± Qm – Qe = QTHB = 0

(Formula 2.1)

Where Qi is the incidental heat gain, Qs the solar heat gains, Qc the heat gain or loss through conduction, Qv the heat gain or loss through ventilation, Qr the heat gain or loss through radiation, Qm the heat gain or loss through mechanical means, Qe the heat loss through evaporation and QTHB the total heat balance. Incidental heat gains (Qi) can include gains from cars, humans, electric bulbs, animals or gains from the day-to-day usage of the external space and the building envelope. Solar gains (Qs) relate to the orientation of the built forms, shading devices, vegetation cover etc. Heat gain or loses through the process of conduction (Qc) is expected to be minimal due to contact during the day but it has a significant effect at night due to irradiation. Ventilation gains or losses (Qv) or effects of air currents depend on surface temperature and difference, pressure difference, wind current and stack effect, inlet outlet juxtapositions and passive systems. Radiation or irradiation gains or losses (Qr) due to the effect of differential temperatures or pressure, depends on the surface 44

temperatures in relation to distance or view of the measuring instrument or object. Evaporative losses (Qe) related to material use, wetness and vegetation cover for transpiration, respiration, carbon dioxide, oxygen levels and other emissions. Mechanical controls (Qm) will determine the capacity of mechanical heating or cooling, active systems and air conditioning requirements. The total heat balance (QTHB) is neutral when the heat gains and losses by the built form balance themselves off. As such, a built form designed for human comfort at a particular design temperature is expected to be sustainable in that temperature environment. However when the total heat balance goes up (QTHB↑), there would be gains due to say climate and more specifically temperature change (ΔT), and this would have a negative impact on the maintenance of heat through mechanical means (Qm). This is because more energy would need to be used to maintain comfort conditions in the form of air conditioning load. As the air temperatures rise

(To ↑) through the natural process of thermo-dynamics and

depending on the insulation and capacitive strategies employed by the designers of such built forms, the internal room temperatures (Ti) would most rise, given that other things are held constant and vice versa (Koenigsberger et al. 1973, pp.75 – 77). In a country with limited resources and many pressing needs, most inhabitants of such over-heated structures and built forms cannot afford the expense of cooling devices necessary for maintaining thermal comfort and as such, suffer from medical conditions related to thermal stress. Such negative conditions, prevalent in built forms, have come to be known as Sick Building Syndrome (SBS) and the remedial action on the built form to restore thermal comfort is the process of retrofitting the built forms by the designers and transformations by the users of these built forms. Research on diagnosing, remedial action and retrofitting techniques to the sick building syndrome in upland climates (Ebrahim, 2011) and sick building syndrome and bioclimatic regional classification in Kenya (Ebrahim, 2011a) have been carried out and the recommendations made are a case in point. 45

Identifying the main variables emphasized in the relationship and the theories related to the micro-temperature change and the built forms was the next step in developing the theoretical framework for the study. Temperature is measured in the shade and in meteorological organizations, usually in a ventilated box (Stevenson Screen) at a height of 1.2 to 1.8 metres above the ground level (Szokolay, 2011, p.29). Most of the published climatic data is collected from meteorological stations, usually located on an open site, often at airports (Szokolay, 2011, p.73). The climatic data for the study was sought from the East African Meteorological Department (EAMD: 1970) and the Kenya Meteorological Department (KMD: 1984) and in particular, the meteorological station at the Jomo Kenya International Airport (Kenya Meteorological Department, 1984, p.61) which is the nearest meteorological station to Komarock Infill B Estate. The climate of a given site may differ quite significantly, from that indicated by the available data from local meteorological stations (Szokolay, 2011, p.73). Climate and in particular the micro-temperature at the Komarock site may have differed quite significantly, from that indicated by the available data from the meteorological station, and is the thrust of this study. According to Szokolay, on-site measurements are impractical because nothing less than a year would suffice, and rarely is such time available for a project (Szokolay, 2011, p.73). Data was collected at the Komarock site between 8 th June 2013 and 19th September 2015. It is customary in architectural schools to obtain data from the nearest meteorological station and to exercise a qualitative judgment on how and in what way would the site climate differs from that indicated by the meteorological stations (Szokolay, 2011, p.73). The study used software (Ebrahim, 2010, 2010c and 2010d) to synchronize the data from the nearest meteorological station and the site conditions. 46

It was envisaged that by understanding the theory of temperature change, the e study would be able to identify and operationalize an assessment of the dependent variable. Temperature change (ΔT) and especially micro-temperature change in degree Celsius is the difference between the measured air temperature (To) and the temperature readings from the local meteorological station developed as the baseline temperature (TB). Micro-temperature change was calculated using the following formula: ΔT = To – TB (Formula 2.2) Research work has been undertaken on various aspects of built form and temperature change by Givoni (1969), Lenihan and Fletcher (1978), Baker (1987), Njue and Kimeu (2010), Meffert (1981) and Koenigsberger (et al., 1973). These studies therefore informed the study on the urban heat island of Nairobi suggesting that: urban development generally leads to a typical rise of temperature even under quite different topographic and climatic conditions, heat islands tend to increase with city growth, and as suggested is proportional to the logarithm of the size of population, heat islands in industrialized areas are strongest on working days, and that during hot spells, heat islands form areas with a marked rise in mortality , especially amongst the elderly people (Meffert, 1981, p.2). Urban heat island observations and expression in meteorological terms by the albedo (Meffert, 1981, p.2) and in building terms by the sol-air temperature concept, where radiative excess temperature is expressed by: I.α/f

(Formula 2.3)

Where I the incident radiation (W/m2), α is the absorbance factor (ratio) and f the surface conductance (W/m2 oC). The surface conductance is governed by the convectional and evaporative characteristics of the particular material and its exposure. Sol-air temperature is a temperature value which would create the same thermal effect as the incident radiation in question. Sol-air temperature concept 47

combines the effect of the heating effect of solar radiation incident on a building with the effect of warm air, and the sol-air temperature is added to the air temperature (Koenigsberger et al., 1973, p.74) as captured in the following equation: Ts =To + [(I x a)/fo]

(Formula 2.4)

o

Where Ts is the sol-air temperature ( C), To is the outside air temperature (oC), I is the radiation intensity (W/m2), a is the absorbance of the surface and fo is the outside surface conductance (W/m2 oC). Universal climate change agreements are used by regional and national standards bodies for developing micro-temperature change benchmarks and translating these into legislation by the respective governments. With 196 parties, the United Nations Framework Convention on Climate Change (UNFCCC: 2015a) has near universal membership and is the parent treaty of the 1997 Kyoto Protocol, which has been ratified by 192 of the parties (United Nations Climate Change Secretariat, 2015a, p.2). Part of the scope of the report required the review and consideration of strengthening the long-term global goal, referencing various matters presented by science, including in relation to the warming of 1.5 degrees Celsius above pre-industrial levels (UNFCCC: 2015, p.3). According to science, deep cuts in global greenhouse gas emissions are required, with a view to reducing global greenhouse gas emissions in order to hold the increase in global average temperature to below 2 degrees Celsius above preindustrial levels, parties therefore, should take urgent action to meet this long-term goal, consistent with science and on the basis of equity which would from henceforth be referred to as Limit for Global Warming (UNFCCC: 2015, p.3). the literature review also focused on the consideration of e strengthening the long-term global goal, referencing various matters presented by science, including in relation to a temperature rise of 1.5 degrees Celsius (UNFCCC: 2015, p.4). On the global average temperatures, reports revealed an increase of 0.85 degrees Celsius since 1880; this is a good approximation for pre-industrial levels, 48

while another reported that 14 of the last 15 years were the warmest on record, with 2014 being the hottest. More than 90 percent of the energy accumulated in the climate system between 1971 and 2010 has been absorbed by the oceans, leading to ocean warming. In terms of projections, the global average temperature change is likely to exceed 1.5 degrees Celsius by 2100, relative to pre-industrial levels (UNFCCC: 2015, pp.6 – 7), except in a situation whereby warming is likely not to exceed 2 degrees Celsius and will continue beyond 2100, the ‘business as usual’ development thinking share these characteristics, except that they are likely to exceed a warming of 2 degrees Celsius by 2100. Additionally, the reports suggest that temperature changes will exhibit both inter-annual and decadal variability and regional differences. For example, regarding temperature extremes, heatwaves are expected to occur with higher frequency and duration (UNFCCC: 2015, pp.6 – 7). If a temperature limit were exceeded, which is sometimes referred to as an overshoot, warming could be returned to that limit in the longer term if the carbon dioxide content of the atmosphere is later actively reduced. As far as the long-term aspects are concerned, an actual equilibrium in terms of temperature, notably of ocean waters, and sea level would only be reached after several centuries on a millennia time scale. To a large extent, anthropogenic climate change, including ocean acidification and many other impacts are irreversible, on at least a multicentury to millennial timescale (UNFCCC, 2015, pp.6 - 7). The Paris Climate Agreement, based on the 13th February 2015 agreement later signed in Geneva, was reached in Paris at the end of 2015 and will come into effect in 2020. The negotiating text covers the substantive content of the new agreement including mitigation, adaptation, finance, technology and capacitybuilding (UNCCS: 2015a, p.1). After the above literature review, the study proceeded to develop the theoretical framework for the study into the relationship between micro-temperature change and urban built form, here the study sought to operationalize the variables,

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and establishing the relationship between the variables in order to the study in generating new knowledge. The essence of planning and design is making choices between alternatives. This is not easy task, especially when it involves social and economic values. Even here, where the concern is with physical outcomes, the choices are not obvious. Indeed, the basic objectives and goals confronting those charged with designing for street climate may be to maximize shelter, to maximize the dispersion of pollutants, to maximize urban warmth and to maximize solar access (Oke, 1988, p.103). Urban climatology, as a predictive science, has the potential of translating research findings into a direct value in urban planning and design, street dimensions and building density. Quantitative guidelines on street geometry are associated with the almost infinite combination of different climatic contexts, urban geometries, climate variables and design objectives. Obviously there is no single solution, i.e. there is no universally optimum geometry. However, this should not stop those concerned with seeking general guidelines as long as they are flexible enough to cater to special needs and situations, urban canyon field studies, scale and mathematical modeling, shelter and urban geometry in air flow and natural ventilation in and around buildings, dispersion and urban geometry, and urban warmth and geometry (Oke, 1988, p.103). Studies on street design and urban canopy climate demonstrated that a number of useful relationships exist between the geometry and the microclimate of urban street canyons. The studies are potentially helpful in the establishment of guidelines governing street dimensions for urban designers. The relationships have the special merit of being quantitative and only depending on simple measures. Unfortunately there is basis for linking canyon climate characteristics to socioeconomic comfort or safety objectives. In many cases the choice of thresholds is arbitrary or largely subjective. There is a need to refine these through further research but there will always be an element of value judgment involved in setting the priorities and acceptable limits in a given city (Oke. 1988, p.111). 50

Open space variables that can be deduced from Oke (1988) are: orientation, height of adjoining buildings and width of open space derived from the basic geometric unit of open space (Figure 2.1), sky view angles derived from the urban cross-section showing sky view angles (Figure 2.2) and light angles derived from urban cross-section showing light angles (Figure 2.3). In a relatively recent study, following from Oke (1988) and others, of urban built form and climate change variables in Chennai Metropolitan Area (India), examined the thermal comfort conditions of six urban built forms (dense compact mid-rise urban and dispersed low-rise urban form) in relation to the urban geometry of the density of buildings, height to width ratio, sky view factor and green cover and vegetation. Using a computer model called RayMan to model the radiation fluxes in simple and complex urban environments, the comfort conditions of the outdoor urban spaces were analyzed in terms of air temperature and Physiological Equivalent Temperature (PET) (Rose, Horrison & Venkatachalam, 2011).

Figure 2.1: Basic geometric unit of open space. Source: Adapted from Oke (1988, p.105).

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Figure 2.2: Urban cross-section showing sky view angles. Source: Adapted from Oke (1988, p.108).

Figure 2.3: Urban cross-section showing light angle. Source: Adapted from Oke (1988, p.110). Air temperature and the thermal comfort trends in the residential neighbourhoods of the Chennai Metropolitan Area revealed that the nights were 52

comfortable. During the daytime, all the residential sites were uncomfortably hot with the Physiological Equivalent Temperature values well above the upper limit of the comfort zone. As the daytime comfort was found to have a significant correlation with the street geometry and percentage of urban built-up, the study indicates the significance of improving daytime comfort in residential areas, by stipulating appropriate urban built-form in the development regulations of the Chennai Metropolitan Area (Rose, Horrison & Venkatachalam, 2011, p.5). Thermal comfort analysis of the residential areas revealed, that the daytime comfort conditions can be improved significantly with an increase in the percentage of the built-up area and the height to width ratio. However, the increased aspect ratio and the urban built-up reduced the night time comfort conditions, and influences the energy demand for cooling during the nights. The height to width ratio influences the comfort conditions significantly when compared to the percentage of urban built-up area. Moreover, the study reveals that with increase in the height to width ratio, the daytime comfort increases and the night time comfort decreases. This indicates the need for arriving at an optimum height to width ratio, percentage of built-up area and improving the outdoor thermal comfort in the Chennai Metropolitan Area (Rose, Horrison & Venkatachalam, 2011, p.5). A study on the thermal comfort in outdoor urban spaces in primarily temperate climates attempted to understand the human variable, in order to achieve a better understanding of the richness of microclimatic characteristics in outdoor urban spaces, and the comfort implications for the people using them. The underlying hypothesis is that these conditions influence people’s behavior and usage of outdoor spaces (Nikolopoulou, Baker & Steemers, 2001). A purely physiological approach is inadequate in characterizing comfort conditions outdoors, and an understanding of the dynamic human parameter is necessary in designing spaces for public use. The thermal environment is indeed of prime importance; influencing people’s use of these spaces, but psychological adaptation such as available choice, environmental stimulation, thermal history, 53

memory effect, expectations, are also of great importance in that such spaces present few constraints (Nikolopoulou, Baker & Steemers, 2001). Table 1.2 (Appendix 1: Standard tabulated observation sheets) indicates a list of micro-temperature change, urban built form and other variables deemed influential following the review of related literature. The established theoretical framework in the study on the relationship between micro-temperature change and urban built form was used to link the study to existing theories (Design temperature standards and heat balance equation), hence enabling the study to have a basis for the justification and identification of study concepts (Sol-air temperature, thermal comfort and urban heat island concepts), the search for new data (On-site measurements, meteorological and baseline data), data analysis and interpretation of the data (Paris 2015). The reviewed theories enabled the study to explain why the studied situation exists, the variables at play (Micro-temperature change, urban built form variables related to densification and geometry), provide a general framework for data analysis and testing of hypothesis. The study proceeds to develop a conceptual framework.

2.6 CONCEPTUAL FRAMEWORK The Conceptual framework for the study was the concise description of the phenomenon under study with the relationship between micro-temperature change and urban built form being complemented through the graphic or visual depiction of the major variables of the study. An effective conceptual framework is an explanation of the constructs studied either graphically or in narrative form. The key factors, variables, and presumed relationships are conceptualized to provide direction in explanations of the relationship among interlinked concepts (Rukwaro, 2016, p.27). Figure 2.4 depicts the diagrammatic representation of the conceptual framework and the on objectives operationalized for the study at three levels. Level 54

1 of the conceptual framework diagram of the study attempted to operationalize the first research objective which read “to identify urban built form variables causing the temperature change in Komarock Estate through review of related literature “. The framework lists variable under the theme as: urban built form and temperature change, significances of urban built form variables and temperature change, sustainable urban built form and microclimate, and application of design and planning strategies in a temperature changing environment (Theme 1). Study of micro-temperature and urban built form relationship

Level 1 Operationalizing Objective 1: Identification of urban built form variables causing temperature change in Komarock Estate.

Reviewed Literature 1

Themes 1

Level 2 Operationalizing Objective 2: Determination of influence of significant urban built form variables in contribution to temperature change.

Reviewed Literature 2

Themes 2

Level 3 Operationalizing Objective 3: Development of design and planning strategies in view of sustainable urban built form in a temperature changing environment.

Reviewed Literature 3

Themes 3

Figure 2.4: Diagrammatic representation of conceptual framework on objectives operationalizing. Source: Author (2015).

A list of micro-temperature change, urban built form and other variables deemed significant was developed based on a review of related literature of work by 55

: Geiger (1975), Meffert (1981), Taha (1997), McDonald (2003), Shulkburg (2007), Firth and Wright (2008), Commission for the Implementation of the Constitution (2010), Kane et al. (2011), Montello and Sutton (2013), and Mumina and Mundia (2014). Level 2 of the conceptual framework diagram of the study attempted to operationalize the second research objective which was to determine the influence of the significant urban built form variables in contribution to the temperature change; this was done through review of related literature, designing a theoretical framework followed by a conceptual framework (Theme 2). A list of micro-temperature change, urban built form and other variables determined as influential form review of related literature , examined the work by Lynch (1960), Olgyay and Olgyay (1963), East African Meteorological Department (1970), Koenigsberger et al. (1973), Meffert (1981), Kenya Meteorological Department (1984), Oke (1988), Rosenlund (1995), Muneer (2000), Nikolopoulou, Baker and Steemers (2001), Capeluto (2002), McDonald (2003), Lam (2004), Gitari (2006), Shuckburg (2007), Ebrahim (2010, 2011 and 2011a), Szokolay (2011), Rose, Horrison and Venkatachalam (2011), United Nations Framework Convention on Climate Change (2015) and United Nations Climate Change Secretariat (2015a). Szokolay (2011, pp.73 – 74) lists local factors which influence the site climate such as topography, slope, orientation, exposure, elevation, hills or valleys at or near the site, ground surface, natural or man-made, its reflectance which is often referred to as albedo, permeability, soil temperature, paved areas and vegetation. Three dimensional objects, such as trees, tree-belts, fences, walls and buildings also may influence the wind, cast shadows on sites and may subdivide the areas into smaller distinguishable climatic niches. Level 3 of the conceptual framework diagram attempted to operationalize the third research objective which read “to develop design and planning strategies in view of sustainable urban built form in a temperature changing environment”. The 56

study reviewed related literature (3), and then developed a conceptual definition of variables, operational definition of the variables, the conceptual model and the hypothesis of the study (Theme 3). The Conceptual framework of the study discerns a deeper meaning of investigated idea and the data. Architects need to make a decision on the environmental design strategy based on the type of energy system of the site and the level of application of these systems earlier on in the design process. Figure 2.5 indicates the diagrammatic representation of the potential for climatic controls on building sites, available to architects. The diagram represents a typical plot of air temperature data for a normal day. It is based on the work of Koenigsberger et al. (1973, p.92) and modified by Meffert (1980, p.111). In the case of Koenigsberger et al. (1973), were concerned with the varying climatic factors and therefore placed these on the Y-axis. Their surrogate was the micro-temperature change in degree Celsius (oC), while the abscissa is the time in unit hours. The United Nations Climate Change Secretariat (UNCCS: 2015 and 2015a) recommends a figure of two degree Celsius as the upper limit for climate change. If this is the starting point for the design process, the architect would consider macroclimatic, meso-climatic, microclimatic and artificial climatic controls for regulating the internal and external environment of the built forms. Macroclimatic controls are used in the large scale level of planning and settlement design of the built forms and offer minimum intervention to the external environment. Mesoclimatic controls are intermediate level related to the building design, while microclimatic controls offer small scale at the elemental and structural scale interventions to build form design. Precisely controlled indoor climate can only be achieved by mechanical (active) controls, but this may not be the aim, and even if it is, with adequate structural controls, the task of mechanical controls is radically reduced to make the system more economical (Koenigsberger et al., 1973, p.92).

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Conceptual frameworks explore the possibility of adoption of models identified in the theoretical framework and the modifications that suit the study. Architects are taught to address issues of society, technology and environment. Climate is a subset of the broader environment whose consideration provides the basis of adequate shelter which Olgyay and Olgyay (1963) have called mature architecture. Figure 2.6 is a diagrammatic representation of the built form variables identified in the study, making an argument for mature architecture affecting the site climatic environment and micro-temperature change. This change may be positive or may have negative connotations. Conceptual frameworks of the study recognize that research is evolving and expressing its uniqueness in research methods, findings and contribution to knowledge. The study used planning controls in the sampling technique in order to achieve the first objective of the study which is to identify the urban built form variables which have an influence on the temperature change. The study used Lynch (1960) planning method to identify built form attributes related to the plot and open spaces associated with districts, nodes, edges, landmarks, roads and paths. Figure 2.7 shows the phenomenon under study through spatial variables. The following was a conceptual definition of plot attributes of the built form: Districts are medium-to-large sections of the city, conceived as having twodimensional extent, which the observer mentally enters inside of and which are recognizable as having some common, identifying character (Lynch, 1960). Nodes are strategic spots in a city which an observer can enter and which are intensive foci to and from which he is travelling. Nodes may be primarily junctions, places of a break in transportation, a crossing or convergence of paths, moments of shift from one structure to another (Lynch, 1960). Meeting points, convergence of paths, roads and axis, termination of points, places of rest such as courtyards etc. were identified as nodes within the Komarock Infill B Estate.

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Edges are the linear elements not used or considered as paths by the observer. They are the boundaries between two phases, linear breaks in continuity, shores, railroad cuts, edges of development, walls. They are lateral references rather than coordinate axis (Lynch, 1960). Landmarks are points of reference, but in this case, the observer does not enter within them, they are external and are usually a rather simple defined physical object (Lynch, 1960). Landmarks include specific buildings, signs, mountains and even distinctly observed districts in the site landscape. Paths are the channels along which the observer customarily, occasionally or potentially moves (Lynch, 1960). Paths may include the streets, walkways, transit lines, canals and railways which serve the city, and other spatial units that the viewer perceives. Axis may either be imaginary or physical. Axis helps to plan, describe and direct the viewer to a station and base. Axis as a planning system may take the form of lines, grids, curvilinear, rectilinear, parallel, axial, polar lines and arrayed (Lynch, 1960). The Conceptual framework of the study concludes by summarizing the concepts and constructs identified and presented, and the conceptual understanding of the study philosophy. In the next section, the study provides the conceptual definition of variables, operational definition of variables, conceptual model and hypothesis for the study.

2.7 CONCEPTUAL DEFINITION OF VARIABLES Conceptual definition of variables in the study of the relationship between micro-temperature change and urban built form commences with an explanation of the terms concept and construct. A concept is an image or symbolic representation of an abstracted idea, or a complex mental formulation of experience or meaning or 61

characteristics associated with certain events, objects, conditions, situations among others that have common characteristics beyond single observation. The concept is operationalized to create a measure for the constructs. Constructs are ideas or images conceived for a given study but they cannot be directly observed. A conviction of why things happen the way they do creates groups of concepts that form a construct (Rukwaro, 2016, p.27). The formulation of the conceptual framework of this study, established concepts and developed constructs which were deemed important indicators of relationships of different constructs under investigation. Constructs were measured in the study by first identifying their dimensions and developing the conceptual definitions of variables. Micro-temperature has been shown to be related to outside temperature (To). Under thermal design of buildings under steady state conditions (Koenigsberger et al., 1973, p.65 – 82) this outside temperature is normally averaged out and called the average temperature (TAve). Micro-temperature change is the product of subtracting the temperature data from meteorological stations (TMD) near the site from the micro-temperature. Komarock Infill B Estate depicts a structured neighborhood and is located in Nairobi which has an upland climate. Urban built forms in Komarock Infill B Estate have been shown to have built form variables related to surrogates that can be measured. Temperature can be expressed in other formats such as radiant temperature, surface temperature etc. However the study has limited itself to air temperature. The vision behind this approach is that an in depth study can be carried out on the effect of man’s construction and building activities, and its probable implication on the variable external air temperature (To) as it affects human thermal comfort and energy use. From normal and daily sun path observations and human physiology, it would seem that the scorching sun has an impact on the thermal sensation and temperature change in Komarock Estate built forms. By not adhering to the converse north-south orientation for perceived minimum impact, the overall temperature and specifically the micro-temperature of each building unit would be 62

affected. Thus an assessment of building orientation would give some vital clues on the extent of the damage, and on the contribution to the micro-temperature change. Apart from the magnitude value of the air temperature (TAir), the study also considered the maximum temperature (TMax), minimum temperature (TMin), average temperature (TAve), temperature distribution (Isotherms) and meteorological station temperature data. Metrological Station temperature data for the study were got from the East African Meteorological Department (EAMD, 1970) and Kenya Meteorological Department (KMD, 1984). The study used International Standards Organization (ISO), Kenya Bureau of Standards (KEBS, 2007) and bioclimatic standards (Koenigsberger et al. 1973) for assessing thermal comfort of the urban built forms. Studies on the determination of the effects of green spaces in Seoul (Korea) on urban heat distribution using satellite imagery concluded that urban heat distribution deviates considerably from a concentric heat island pattern, and further, that warm areas can be attributed to the presence of densely built-up commercial and industrial neighbouring sectors. In addition, spatial interaction existed within a range of about five to 10 km in the hot season. Moreover, spatial interaction in the vicinity of urban green spaces is shown to be stronger than in the core urban area (Choi, Lee & Byun, 2012, p.127). The temperature of green space is quite different from that of the urban area. The effects of temperature decrease due to urban green areas can extend to around four kilometres. The ratio of urban heat area to urban cooling area increases with distance from a green space boundary. The analyses indicated that urban green space plays an important role for mitigating urban heating in the central urban area (Choi, Lee & Byun, 2012, p.133). Operationalization of concepts in the study was carried out through classifying the concepts into several clearly defined dimensions and identifying the various indicators (surrogates) for each. Dimensions were specific groups of the concepts. Physical characteristics of the built form include volume, perimeter, 63

height, openings, texture, materials and specifications, which have an impact on the micro-temperature change. Modifications and transformations of the typical plans of structured neighbourhoods such as Komarock Estate, by the users, and the impact on the micro-temperature change can be monitored and the results compared to temperature standards set by Paris 2015. Urban planning variables which include plot ratios, ground coverage, sizes of street, density ratios and change of user permits would also have an impact on the temperature change. Legislature and thermal standards of urban built forms would also have an impact on the temperature change. Casual observation of the past, present and future directions of the built form and its manifestations gave inspiration and direction for the research. Urban built forms in Kenya are governed by local legislation like the building and planning act of parliament and by-laws which make provision for building design; Kenya Vision 2030 (Gakuru, 2006), which embraced millennium goals, Constitution of Kenya (Laws of Kenya, 2010), Building Code (Republic of Kenya, 1976) and Physical Planning Act Chapter 286 (Republic of Kenya, 2012). Planning and design attitudes of built form were used in the study to operationalize the concept of classification of urban built form through classifying the concept into measurable surrogates with precise dimensions that have an impact on the temperature change. Planning and designing a building does not involve only the building. There are other outside factors which affect it (Singh & Singh, 2010, p.1). Built form attitudes identified by the study were: building type, plot size, orientation, road proximity and building classification. Figure 2.8 shows the significant urban built form attitude method for plans, while Figure 2.9 indicates the sections. The following was a conceptual definition of planning and design attitudes of the built form:

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Building types in modern architecture translates into form follows function. This new school of thought in architecture weighs function more than appearance and advocates that the form must reflect function (Singh & Singh, 2010, p.3). Plot size and moreover the minimum size of the plot is one aspect of density zoning (Singh & Singh, 2010, p.22). Orientation, local climate and traditions of the people plus the site and its surroundings dictate the planning of built forms. Besides the natural elements such as sun, rain and wind also affect planning to some extent. The structures have to be oriented in a particular direction to make the best use of the natural elements of nature (Singh & Singh, 2010, p.2, pp.138 – 147 and pp.158 – 176). Road proximity as an aspect of town planning helps in achieving the best possible advantages of the situation of town with respect to its land and the surrounding environment (Singh & Singh, 2010, p.4, p.39 and p.44). Building classification of built forms are a house, detached house, semidetached house, row housing and “change of user” development (Singh & Singh, 2010, p.26 – 27 and p.39). A house is sometimes referred to as a dwelling which means a family unit. Detached houses have open land around it and have sufficient margins on the sides, front and rear. Semi-detached houses have one wall in common with the adjacent house. Row housing provides more of a residential density and may be single or double storey. Buildings which were classified as “change of use” were originally designed as a dwelling, but have since been changed to commercial (shops) and educational (school). Open ground are parks that serve as breathing spaces for urban areas. Other residential units may include apartments, flats and multi-storey units including skyscrapers (Singh & Singh. 2010, pp.26 - 27 and p.39). Open spaces can be classified according to the size of the park or open space and may be small, medium sized or large, while roads are captured in relevant statutory legislations and conventions.

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Table 1.3 (Appendix 1: Standard tabulated observation sheets) is a list of micro-temperature change, urban built form and other variables used in developing design and planning strategies in view of sustainable urban built forms in a temperature changing environment.

2.8 OPERATIONAL DEFINITION OF VARIABLES Operational definition is the explicit specification of a variable in such a way that its measurement is possible. It indicates how the variables are measured, what their measurements or values are and the units used to measure. A good operational definition should capture the variables’ measurements that are appropriate for testing hypothesis (Rukwaro, 2016, p.28). The study into the relationship between micro-temperature change and urban built form uses three types of operational definition of constructed, based in terms of operations performed to cause the phenomenon to occur, how the particular object or thing operates and, lastly, what an object or phenomenon looks like. Each technical term and its operational definition are explained briefly. The Review of theories established the structural dimension and surrogates (empirical indicators) of the studied phenomena which helps in the comprehension and expression of the conceptual framework. Thereafter, a matrix and diagrammatic representation of the studied phenomena, the structural dimension and empirical indicators of the phenomenon was developed. Table 1.4 (Appendix 1: Standard tabulated observation sheets) shows a matrix, while Figure 2.10 shows a diagrammatic representation of the structural dimension and empirical indicators of the phenomenon under study on the relationship between micro-temperature change and urban built form for Komarock Estate. The phenomena examined in the study on micro-temperature change included: the structural dimension inferred by temperature and empirical indicator (surrogate) captured by temperature change, temperature change for sample, 67

baseline temperature, outside air temperature, inside air temperature, average temperature, minimum temperature and maximum temperature.

Studied phenomena: Microtemperature change

Study of microtemperature change and urban built form

Studied phenomena: Urban built form (buildings)

Studied phenomena: Urban built form (open spaces)

Structured dimensions inferred: Temperature and baseline temperature.

Structured dimensions inferred: Buildings, building attributes, building attitudes, building planning regulations, building elementals, building type, building classification, orientation, plot size and road proximity.

Structured dimensions inferred: Open spaces, open space planning regulations, open space elemental and open space attitudes.

Figure 2.10: Diagrammatic representation of studied phenomena and the structural dimension inferred. Source: Author (2017). The studied phenomena of urban built form included: the structural dimension inferred by building and empirical indicator (surrogate) captured by building planning regulations (ground coverage and plot ratio), building regulations, building elemental, building attributes (district, node, edge, landmark, path and 68

axis) building attitudes (building type, plot size, building orientation, building classification and building road proximity). While the studied phenomena of urban built form included: the structural dimension inferred by open space and empirical indicator (surrogate) captured by open space planning regulations (hard landscape ratio, light angle and shading coefficient), open space elemental (open space size) and open space attitudes (open space size, open space orientation and open space road proximity).

2.9 CONCEPTUAL MODEL The Conceptual model that then study developed to facilitate the study of the relationship between micro-temperature change and urban built form (Figure 2.11) was the creation of a physical or computer analogy for some of the phenomenon. Modeling helps in estimating the relative magnitude of various factors involved in a phenomenon. A successful model it can be proved, accounts for unexpected behavior that has been observed, to predict certain behaviors, which can then be tested experimentally, and to demonstrate that a given theory cannot account for certain phenomenon (University of South Carolina Libraries, 2014: Glossary of research terms). Conceptual model of urban climates must point toward the interaction and interpenetration of the variables involved (Oliver, 1973, p.237). The entry point to the model, defines the work to be undertaken in the study of the relationship between the micro-temperature change and urban built form at Komarock Infill B Estate (Nairobi), it states the first research objective of the study which was to identify urban built form variables causing temperature change in Komarock Estate study site, the second was to determine the influence of the significant urban built form variables in contribution to temperature change and the third was to develop design and planning strategies in view of sustainable urban built form in a temperature changing environment. 69

MICROTEMPERATURE CHANGE AND URBAN BUILT FORM RELATIONSHIP

DEVELOPMENT OF DESIGN AND PLANNING STRATEGIES

Dependent variable: Micro-temperature change

IDENTIFICATION OF URBAN BUILT FORM VARIABLES

Independent variable: Urban built form (Buildings)

Independent variable: Urban built form (Open spaces)

DETERMINATION OF SIGNIFICANT URBAN BUILT FORM VARIABLES

Figure 2.11: Conceptual model to facilitate the study of the relationship between micro-temperature change and urban built form. Source: Author (2016). A cyclical maneuver seems to have been developed around the three research objectives of the study with the central movement being the process of 70

processing the collected data with regards to the dependent variable of microtemperature change and the independent variables related to the urban built form (building and open space) variables. Once the model had been motivated, urban built form variables causing temperature change in the study site were identified, and the process moved towards the determination of significance, thereafter developing design and planning strategies in view of sustainable urban built form in a temperature changing environment, and back to the entry point. The process was again repeated until all the other variables had been identified.

2.10 HYPOTHESIS OF THE STUDY The null hypothesis (H0) for the study was as follows: “Micro-temperature change has no relation to the urban built form.”

The alternative hypothesis (H1) would read as follows: “Micro-temperature change has a relation to the urban built form.”

The study of the relationship between micro-temperature change and urban built form variables formulated the hypothesis which was meant to be tested statistically in a negative way (null hypothesis). Such a statement of the null hypothesis would allow all chances that the undesirable event must happen, so that should the desired event take place despite such a conservative approach, one can then be in a position to confirm that event did not occur as a matter of mere chance (King’oriah, 2004, p.177). The undesired event in the study was that microtemperature change and urban built form were not related. The desired event in the study was that micro-temperature change was related to urban built form and especially with a positive relationship. 71

Use of the null hypothesis in the study allowed data related to the variables to be collected and the null hypothesis tested to establish or negate the relationship. The alternative hypothesis of the study was the alternative set of facts that are accepted or proven to be true, if the null hypothesis is rejected or proven not to be true or proven not to be applicable to a certain set of circumstances (King’oriah, 2004, p.177). In the study, micro-temperature change was the dependent variable and urban built form was the independent variable. Micro-temperature change was operationally defined and measured in degree Celsius. Urban built form was defined as buildings and open spaces. Both buildings and open spaces were operationally defined and measured using surrogates. Building surrogates, related to the urban built form variables were operationally defined and measured as building type and building heights in metres, plot size in square metres, orientation in degrees from the north, building classification and row of building widths in metres, road proximity in metres, ground coverage and plot ratio as percentages of the plot size. Open space surrogates, related to the urban built form variables, were operationally defined and measured as open space size in square metres, hard landscape ratio as a percentage of the open space size, orientation in degrees from the north, light angle in degrees from the vertical, road proximity in metres, shading coefficient as a percentage of the north and open space length in metres.

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CHAPTER THREE RESEARCH METHODS The research methods used in the study on the relationship between microtemperature change and urban built form were theoretical procedures and statistical approaches that it was envisaged would help the study to collect samples, data and find solutions to the problem. The study selected methods that were scientific because they relied on collected facts, measurements and observations and were not based on personal opinion. Research was carried out following a research procedure. The research procedure for the study followed a step-by-step activity which involved identifying the problem which was handled in chapter one of the study, reviewing related literature in chapter two, collecting and analyzing the data, as well as planning resources for the study. The procedures were described in sufficient detail to allow other researchers to replicate the method in similar studies (Rukwaro, 2016, p.34). Chapter three on research methods details the research design and methodological framework, longitudinal research design, data sources, research tools, observation method, sampling design, plot attributes, planning and design attitudes, sampling technique, batching design and cluster sampling, collection, processing and preparation of data, data analysis and methods, data analysis techniques used in the study, graphic representation of data used in the study, use of digital statistical analytical tools in the study, data tests and analysis conducted in the study, summarized analytical framework and reflections on the research methods.

3.1 RESEARCH DESIGN AND METHODOLOGICAL FRAMEWORK The Research design of the study was the strategic plan that sets out the broad outline and key features of the work to be undertaken in the research study (Mugenda & Mugenda, 2012, p.278). The study formulated the following research 73

questions. Question one read “What are the urban built form variables which cause temperature change in Komarock Estate?” Question two read “What influence do the significant urban built form variables have in contributing to the temperature change?” Question three read “What is the impact of temperature change on design and planning strategies for sustainable urban built form?” The italicized words are critical to the questions and give credence and direction to the research design for the study. The Methodological framework for the study commenced with a review of research designs from other studies.

RESEARCH DESIGN STUDIES Methodological framework for the study was the methods associated with carrying out the inquiry and the reasoning behind the choice of method employed by the study to collect, process and analyze the data, and methods employed for drawing results, synthesis and interpretation of findings, conclusions and recommendations (Mugenda, 2011, p.127). Four studies were scrutinized as a basis for making an informed decision on the research design to be employed in the study. Capeluto’s (2005 July) study on natural ventilation as a strategy for improving the thermal comfort in open spaces in the hot and dry climate of Israel used qualitative methods to analyze the winds. Lam (2005 July) studied the weather data analysis and design implications for different climatic zones of China. Muneer et al. (2000) studied the thermal acoustic, visual and solar performance of windows in buildings, and explored simulation methods using digital techniques. Rosenlund’s (1995) doctoral study on design for desert, an architect’s approach to passive climatisation in hot arid regions primarily in different locations of Tunisia, seemed to be the most apt for the study. Five methods of collecting data 74

were used in the Rosenlund (1995) study – i.e. field measurements, parametric modelling and studies, baseline cases, experimental construction and buildings, and longitudinal measurements and studies, the latter lies within the interest of this study.

LONGITUDINAL MEASUREMENTS AND STUDIES Measurement studies are normally longitudinal, i.e. they span a long period to include variations in the climate and to get data from different seasons, especially in arid regions the normally heavy buildings, and the ground below them, can store and release heat from one period to another. User influence on the indoor climate can also be identified if periods with and without occupation are measured, or if parallel observations of the occupants’ behavior are made such as number of people, activities, opening and closing windows and shutters etc. (Rosenlund, 1995, p.87). Longitudinal measurements can yield large amounts of data. Using for example 20 channels, measuring every hour over one year, gives about 180,000 values. When evaluating these results it is important to decide what to study and then to pick out the corresponding data. If normal conditions are sought, then normal, stable periods should be identified in each season. Extreme conditions and the thermal response of a building during and after such a period can also be studied. Regression analyses may also be carried out; although these require continuous data over a longer period in order to be reliable (Rosenlund, 1995, p.88). Longitudinal studies require programmable loggers for data acquisition. Although they are becoming more and more reliable, the loggers need periodic surveillance and control. Battery backup is essential, especially in remote areas, where there might be frequent power cuts, and infrequent site visits. The data loggers and their probes must be regularly calibrated. The accuracy may also be checked occasionally with simple hand instruments or against available data, such

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as those from meteorological stations. Lastly the researcher must decide in terms of his own experience if the results are reasonable (Rosenlund, 1995, p.88).

3.2 LONGITUDINAL RESEARCH DESIGN Longitudinal research design was used in the study on the relationship between micro-temperature change and urban built form because longitudinal design involves observation of the same sample (plot) at intervals over a specified period of time (Mugenda, 2011, p.72). In the case of Komarock Infill B Estate, thirty plots and sixteen open spaces were observed between 8th June 2013 and 19th September 2015. Each sample plot and open space was observed and data related to the dependent variable micro-temperature change and the independent variables related to the urban built form, were collected and processed individually using the longitudinal design techniques and methods as illustrated in detail in the processing of raw information from the stud b data loggers. An example of the application of the longitudinal design would help in enforcing the study objectives. Plot 41 of Komarock Infill B Estate was observed between the periods of 8th to 15th June 2013. Plot 41 temperatures and other urban built form data, were downloaded in the computer laboratory. Thereafter, the data went through a process of processing, data analysis and presentation, and expected results. Longitudinal research design proceeds with giving details of measurement of variables, measurement of dependent variable, standard tabulated dependent variable, listing and recording of data loggers, standard weekly batching and tabulation of data logger downloads, standard tabulated data loggers weekly temperatures, standard tabulated average data loggers temperatures, standard tabulated average monthly temperature and baseline data for meteorological station, plotted baseline and temperature data, standard tabulated observation sheets, measurement of independent variables, planning regulation variables, building 76

regulation variables, building elemental variables, attribute and attitude variables, open space variables, standard tabulated digitized observation sheet, advantages and disadvantages of using longitudinal research design, list of variables assessed, and test of significance and level of confidence.

MEASUREMENT OF VARIABLES In the section, the study outlines the methods used for measuring the dependent variable related to the micro-temperature change and the independent variables related to the urban built form based in terms of operations performed to cause the phenomenon to occur.

MEASUREMENT OF DEPENDENT VARIABLE Micro-temperature change (Y) in the study was measured as the dependent variable temperature change (ΔT) in degree Celsius (oC), and calculated using the following formula: Y = ΔT

(Formula 3.1)

Where Y is the micro-temperature change in degree Celsius (oC) and ΔT is the temperature change in degree Celsius (oC). By implication micro-temperature change (Y1) for sample plot 1 can be equated to the temperature change (ΔT 1) for the same sample plot 1 and shown as follows: Y1 = ΔT1

(Formula 3.2)

Where Y1 is the micro-temperature change for sample plot 1 in degree Celsius (oC) and ΔT1 is the temperature change for sample plot 1 in degree Celsius (oC). Temperature change varies from plot sample to plot sample and for say temperature change for plot sample 1 (ΔT1) and micro-temperature change for plot sample 1 (Y1), it was calculated using the following formula: Y1 = ΔT1 = T1 – TB 77

(Formula 3.3)

Where Y1 is the micro-temperature change for plot sample 1 in degree Celsius (oC), ΔT1 is the temperature change for plot sample 1 in degree Celsius (oC), T1 is the data logged temperature for plot sample 1 in degree Celsius ( oC) and TB is the baseline temperature for the month when data was collected for sample 1 in degree Celsius ( oC). Just like Y1 was the micro-temperature change for sample plot 1, Y2 was the micro-temperature change for sample plot 2 and Y∞ was the microtemperature change for sample plot infinity.

STANDARD

TABULATED

DEPENDENT

VARIABLE,

LISTING

AND

RECORDING OF DATA LOGGERS Table 1.5 (Appendix 1: Standard tabulated observation sheets) indicates the standard tabulated dependent variable (micro-temperature change) and their description, and gives the various dependent variable terminologies, symbol, unit and descriptions in relation to temperature change (ΔT), temperature change for sample 1 (ΔT1), baseline temperature (TB), outside air temperature (To), inside air temperature (Ti), average temperature (TAve), minimum temperature (TMin) and maximum temperature (TMax). Processing of raw information from data loggers commenced with the listing and recording of the data loggers into a data logger journal (Table 1.6: Standard data logger journal for a batch of data collected – Appendix 1: Standard tabulated observation sheets), in which, information in regard to item, description and symbol as Average Logger 1 Plot 1 Lounge (L1), Average Logger 2 Plot 1 Lounge (L2), Average Logger 3 Plot 2 Lounge (L3), Average Logger 4 Plot 2 Lounge (L4), Average Logger 5 Open Space (L5), Average Loggers Consolidated (LCon) and Consolidated Batch (CBatch) was displayed.

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STANDARD WEEKLY BATCHING AND TABULATION OF DATA LOGGER DOWNLOADS Data collected for any sample for a month is tabulated for each week as indicated Table 1.7 (Standard weekly batching and tabulation of data logger downloads - Appendix 1: Standard tabulated observation sheets).

STANDARD TABULATED DATA LOGGED WEEKLY TEMPERATURES Thereafter, the weekly batch of tabulated data, were averaged to achieve a daily temperature regime for that sample or data logger (Table 1.8: Standard tabulated data logged temperatures for sample logger for a week – Appendix 1: Standard tabulated observation sheets).

STANDARD TABULATED AVERAGE DATA LOGGED TEMPERATURES Table 1.9 (Appendix 1: Standard tabulated observation sheets) indicates the standard tabulated average data logged temperatures for sample logger for the month when data was collected which is generally the presentation of the rounding out column of Table 1.8 (Appendix 1: Standard tabulated observation sheets). Temperature data for sample plot 1 was observed for a specified period, usually a week or seven days. Average temperatures (T1 Ave) for plot sample 1 can now be calculated using the following formula: T1 Ave = ƩT1 ÷ n1

(Formula 3.4)

Where T1 Ave is the average temperature for plot sample 1 in degree Celsius (oC), ƩT1 is the sum of the data logged temperature for plot sample 1 in degree Celsius (oC), and n1 is the total number of temperature data points (No). And likewise for the average baseline temperature (TB

Ave)

was calculated using the

following formula: TB Ave = ƩTB ÷ nB 79

(Formula 3.5)

Where TB

Ave

is the average baseline temperature for the month when

temperature was data logged for plot sample 1 in degree Celsius (oC), ƩTB is the sum of baseline temperature for the month when temperature was data logged for plot sample 1 in degree Celsius (oC), and nB is the total number of baseline temperature data points (No). By implication of Formula 3.3, average microAve)

temperature change (Y1

and average temperature change (ΔT1

Ave)

were

calculated using the following formula: Y1 Ave = ΔT1 Ave = T1 Ave – TB Ave

(Formula 3.6)

Where Y1 Ave is the average micro-temperature change for plot sample 1 in degree Celsius (oC), ΔT1 Ave is the average temperature change for plot sample 1 in degree Celsius ( oC), T1 Ave is the average temperature for plot sample 1 in degree Celsius (oC) and TB

Ave

is the average baseline temperature for the month when

temperature was data logged for plot sample 1 in degree Celsius (oC). By substituting Formula 3.4 and Formula 3.5 results into Formula 3.6, average temperature change (ΔT1

Ave)

was calculated using the following consolidated

formula: Y1 Ave = ΔT1 Ave = (ƩT1 ÷ n1) – (ƩTB ÷ nB)

(Formula 3.7)

STANDARD TABULATED AVERAGE MONTHLY TEMPERATURE AND BASELINE DATA FOR METEOROLOGICAL STATION Table 1.10 (Appendix 1: Standard tabulated observation sheets) indicates the standard tabulated average monthly temperature data for Jomo Kenyatta International Airport Embakasi for the period 1959 to 1980 used as baseline temperature in degree Celsius (oC). Meteorological station data (See: Processing of raw information from data loggers) indicates the process for converting data from the local meteorological station into baseline temperatures. Depending on which month the data were collected, daily baseline temperatures (Table 1.11: Standard tabulated baseline temperatures for the month 80

when data was collected for the sample logger – Appendix 1: Standard tabulated observation sheets) were generated for that logger using the Temperature Template of the Ebenergy Software (See: Processing of raw information from data loggers).

PLOTTED BASELINE AND TEMPERATURE DATA As data from the Komarock Infill B Estate site was collected during various times of the year it became necessary to use the concept of the Baseline Temperature so as to calculate the micro-temperature change factor. A baseline is a controlled measurement carried out before an experimental treatment and is mainly used for benchmarking of a research variable (University of South Carolina Libraries (2014: Glossary of Research Terms). In this regard, the study embraced the use of a digital tool called a Temperature Template (Ebrahim, 2010c) of the Ebenergy Software (Ebrahim, 2010). The use of the Query Workbook, Data Collected Logbook, meteorological station data and Temperature Template are explained in detail in the processing of raw information from data loggers. Suffice to say at this point, that the standard tabulated observation sheets (Appendix 1) were used to tabulate and structure the data and thereafter passed through the Ebenergy Software for processing. The Query Workbook (See: Processing of raw information from data loggers) which forms the entry point of the Ebenergy Software was used to codify and answer basic questions related to the data being inputted. The collected data was converted into spreadsheets through the process of tabulation through use of the Data Collected Logbook (See: Processing of raw information from data loggers). Bench marking was done by use of meteorological data from Jomo Kenyatta International Airport (Kenya Meteorological Department, 1984, p.61) as described in use of meteorological station data and the Temperature Template (See: Processing of raw information from data loggers).

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Figure 3.1 is the plotted baseline data of outside air temperature (To) for the month of June using Temperature Template of Ebenergy Software. Each of the sampled plots and open space had a generated baseline data on outside air temperature for the month that data was being collected at Komarock Infill B Estate.

Temp: Deg.C

Time: Hours

Figure 3.1: Indicates plotted baseline data as outside air temperature (To) for the month of June using Temperature Template of Ebenergy Software. Source: Author (2013). Processed data from the sampled plots and open spaces were tabulated and plotted against the generated baseline data in order to derive the micro-temperature change for the individual plot or open space, as explained in detail in Temperature Template (See: Processing of raw information from data loggers). Figure 3.2 indicatesb plotted temperature data compared to time for Batch 1 (June 2013). Whereby, Series 1 was the average temperatures for logger 1 (L1) for plot number 41 located at the lounge, Series 2 for logger 2 (L2) plot number 41 located at the garden, Series 3 for logger 3 (L3) for plot number 48 located at the lounge, Series 4 for logger 4 (L4) for plot number 48 located at the garden, Series 5 logger 5 (L5) for 82

the green space and Series 6 (To: June) for the baseline temperature. Also the data logged temperatures formed a pattern of logged compared to simulated figures which were used to establish the temperature change factor.

Temp: Deg.C

Plot 41 Internal Temp Plot 41 External Temp Plot 48 Internal Temp Plot 48 External Temp Green Space Temp Baseline Temp

Time: Hours

Figure 3.2:Indicates plotted temperature data compared to time for Batch 1 (June 2013). Source: Author (2013).

STANDARD TABULATED OBSERVATION SHEETS Table 1.12 (Appendix 1: Standard tabulated observation sheets) indicate the standard tabulated resultant data logged for sample logger (TS) during the period of data collection, and the tabulated baseline temperatures (TB) during the month of data collection. Table 1.13 (Appendix 1: Standard tabulated observation sheets) indicates the standard tabulated resultant micro-temperature change for the plots during the month of data collection. Average readings for minimum temperature (T1 (T1

Max)

and temperature average (T1

Ave)

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Min),

maximum temperature

for sample plot 1, was read and

comparisons made with readings from those of other samples and other sources. This exercise was repeated for minimum micro-temperature change (YMin), maximum micro-temperature change (YMax) and average micro-temperature change (YAve) figures in degree Celsius (oC) observed from the data sets.

MEASUREMENT OF INDEPENDENT VARIABLES Urban built form in the study was divided into either building or open space variables. Urban built form building variables were operationalized as the independent variables related to planning regulation variables, building regulation variables, building elemental variables, building attributes and building attitudes, constructed, based on how the particular phenomenon or object or thing operates or looks like.

PLANNING REGULATION VARIABLES Planning bylaws included surrogates such as the ground cover (GC), plot ratio (PR), provisions for car parking, provisions for open spaces, sizes and allocation of roads and reserves, building lines and allocation of lanes or access for services and fire-fighting or prevention, pedestrian and vehicular access, provision for the physically impaired or for specialized use, etc. Table 1.14 (Appendix 1: Standard tabulated observation sheets) shows standard tabulated planning regulations, variables, description and explanation. Descriptions and respective explanations are provided for a plot, plot width, plot length, ground cover, plot ratio, car park provision, open spaces, road reserve, building line, building set-back, service lanes and entry. Both buildings and open spaces were operationally defined and measured using surrogates. Building surrogates related to the urban built form variables were operationally defined and measured as building type and building heights in metres, 84

plot size in square metres, orientation in degrees from the north, building classification and row of building widths in metres, road proximity in metres, ground coverage and plot ratio as percentages of the plot size. Building type variable (X1) was observed on plot samples which were Maisonette, Villa or other plot usage, and had building height in metres (M) as the surrogate. Building type was obtained from site survey (photographs). Height of the building was measured from the finished floor level of the building to the highest point of the roof. The measurement was carried out with a tape measure. The dimensions of typical buildings were derived from architectural drawings, while non-typical buildings form as-built drawings were prepared through site surveys. Plot size variable (X2) was observed on plot samples which had small, medium or large plot size. Plot size was operationally defined as a piece of land enclosed by definite boundaries (Singh & Singh, 2010, p.99) and demarcated on site by boundary walls or fencing. Plot dimensions were obtained from architectural drawings. Plot size variable (X2) was calculated using the following formula: X2 = WP x LP (Formula 3.8) Where X2 is the plot size variable for the sample plot in square metres (M2), WP is the width of the sample plot in metres (M) and LP is the length of the sample plot in metres (M). Building orientation variable (X3) was observed on plot samples , with the orientation measured in degrees (Degree) from the North in a clockwise fashion, as North to South (N-S) plot orientation or East to West (E-W) plot orientation. Building orientation was measured on site using a magnetic north compass. Building road proximity variable (X4) was observed on plot samples which had distance from the main or side road measured in metres (M) as corner internal plot, large plot near road or basic plot. Road proximity was obtained from architectural drawings.

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Building classification variable (X5) was observed on plot samples which were detached house, semi-detached house or row of housing, and had row of house width in metres (M) as the surrogate. Building classification information was obtained from aerial images (Google maps) and site surveys (photographs and measurements). Ground coverage variable (X9) was observed on plot sample and was the ratio of the building plinth divided by the plot size and given as a percentage (%). Building plinth was measured as the area covered by the building immediately after the ground floor (level 1), while ground coverage is also referred to as floor area ratio (Singh & Singh, 2010, pp.98 - 99). Ground coverage information was obtained from aerial images (Google maps) and site surveys (photographs and measurements). Ground coverage was calculated using the following formula: X9 = A1 ÷ X2 x 100

(Formula 3.9)

A1 = (W1 x L1)

(Formula 3.10)

X9 = (W1 x L1) ÷ X2 x 100

(Formula 3.11)

Where X9 is the ground coverage variable for the sample plot 1 given as a percentage (%), A1 is the ground floor (level 1) area of the building of the sample plot 1 in square metres (M2), W1 is the width of level 1 of the building of the sample plot 1 in metres (M), L1 is the length of level 1 of the sample plot 1 in metres (M) and X2 is the plot size of the sample plot 1 in square metres (M2). Plot ratio variable (X15) was observed on plot sample and was the ratio of total built-up area divided by the plot size given as a percentage (%). Plot ratio is also referred to as floor space index is the ratio of the total built up area inclusive of walls of all the floors to the area of the land on which the building stands (Singh & Singh, 2010, p.101). Plot ratio information was obtained from aerial images (Google maps) and site surveys (photographs and measurements). Plot ratio was calculated using the following formula: X15 = AB ÷ X2 x 100 (Formula 3.12) 86

AB = A1 + A2 + A3 ……. + A∞ (Formula 3.13) A1 = (W1 x L1)

(Formula 3.14)

A2 = (W2 x L2)

(Formula 3.15)

A3 = (W3 x L3)

(Formula 3.16)

A∞ = (W∞ x L∞)

(Formula 3.17)

AB = ƩA∞

(Formula 3.18)

X15 = [(W1 x L1) + (W2 x L2) + (W3 x L3) … + (W∞ + L∞)] ÷ X2 x 100 (Formula 3.19) X15 = ƩA∞ ÷ X2 x 100

(Formula 3.20)

Where X15 is the plot ratio variable for the sample plot 1 given as a percentage (%), AB is the total area of the building of the sample plot 1 in square metres (M2), A1 is the floor area (level 1) of the building of the sample plot 1 in square metres (M2), A2 is the floor area (level 2) of the building of the sample plot 1 in square metres (M2), A3 is the floor area (level 3) of the building of the sample plot 1 in square metres (M2), A∞ is the floor area (level ∞) of the building of the sample plot 1 in square metres (M2), W1 is the width of level 1 of the building of the sample plot 1 in metres (M), W2 is the width of level 2 of the building of the sample plot 1 in metres (M), W3 is the width of level 3 of the building of the sample plot 1 in metres (M), W∞ is the width of level ∞ of the building of the sample plot 1 in metres (M), L1 is the length of level 1 of the sample plot 1 in metres (M), L2 is the length of level 2 of the sample plot 1 in metres (M), L3 is the length of level 3 of the sample plot 1 in metres (M), L∞ is the length of level ∞ of the sample plot 1 in metres (M) and X2 is the plot size of the sample plot 1 in square metres (M2).

BUILDING REGULATION VARIABLES Building bylaws and other building standards are governed by respective statutory authorities and include the adherence and maintenance of minimum 87

provisions based on design criteria and minimal health standards. Table 1.15 (Appendix 1: Standard tabulated observation sheets) indicates the standard tabulated building regulations, variables, description and explanation. Descriptions and respective explanations are provided for the absorption of surface, emissivity of surface, thermal conductivity and thermal transmittance.

BUILDING ELEMENTAL, ATTRIBUTE AND ATTITUDE VARIABLES Table 1.16 (Appendix 1: Standard tabulated observation sheets) indicates standard tabulated building elemental, variables, description and explanation. Descriptions and respective explanations are provided for building length, mass, time, orientation, area, volume and density. Urban built form building and open space attributes and attitudes were also operationalized. Table 1.17 (Appendix 1: Standard tabulated observation sheets) indicates standard tabulated built form building attribute, variables, description and explanation for building and open space attributes related to district, node, edge, landmark, path and road. Table 1.18 (Appendix 1: Standard tabulated observation sheets) indicates standard tabulated built form building attitude variables, description and explanation for building attitude variables related to building type, plot size, building orientation, road proximity, building classification, ground coverage and plot ratio.

OPEN SPACE VARIABLES Open space included open grounds, paths and roads. Open space surrogates related to the urban built form variables were operationally defined and measured as open space size in square metres, hard landscape ratio as a percentage of the open space size, orientation in degrees from the north, light angle in degrees from the

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vertical, road proximity in metres, shading coefficient as a percentage of the north and open space length in metres. Open space size variable (X28) was observed as open space samples which had small open space size, medium open space size or large space size. Roads, paths and open ground were defined as areas left open to the sky (Singh & Singh, 2010, p.98). Open space area dimensions and other information were obtained from architectural drawings, aerial images (Google maps) and site surveys (photographs and measurements). Open space size variable (X28) was calculated using the following formula: X28 = WOS x LOS

(Formula 3.21)

Where X28 is the open space size variable for the sample road path or open space in square metres (M2), WOS is the width of the sample road path or open space in metres (M) and LOS is the length of the sample road path or open space in metres (M). Hard landscape ratio variable (X30) was observed on sample road path or open space and was the percentage of hard landscape to open space area. Hard landscape is the area covered by tarmac or other permanent finish for roads, pathway and other open spaces. Hard landscape information was obtained from aerial images (Google maps) and site surveys (photographs and measurements). Hard landscape ratio was calculated using the following formula: X30 = AHL ÷ X28 x 100 AHL = (WHL x LHL)

(Formula 3.22) (Formula 3.23)

X30 = (WHL x LHL) ÷ X28 x 100

(Formula 3.24)

Where X30 is the hard landscape ratio variable for the sample road path or open space given as a percentage (%), AHL is the hard landscape area of the sample road path or open space in square metres (M2), WHL is the hard landscape width of the sample road path or open space in metres (M), LHL is the hard landscape length 89

of the sample road path or open space in metres (M) and X28 is the open space size of the sample road path or open space in square metres (M2). Shading coefficient variable (X32) was observed on sample road path or open space, and was the percentage of shaded area (tree cover) to open space area. Shaded area is the area covered by tree shade. The area under shade of an open space was obtained from aerial images (Google maps) and site surveys (photographs). Shading coefficient was calculated using the following formula: X32 = AS ÷ X28 x 100 (Formula 3.25) AS = (WS x LS)

(Formula 3.26)

X32 = (WS x LS) ÷ X28 x 100 (Formula 3.27) Where X32 is the shading coefficient variable for the sample road path or open space given as a percentage (%), AS is the shaded area of the sample road path or open space in square metres (M2), WS is the shaded width of the sample road path or open space in metres (M), LS is the shaded length of the sample road path or open space in metres (M) and X28 is the open space size of the sample road path or open space in square metres (M2). Open space orientation variable (X33) was observed on open space samples which had orientation measured in degrees (Degree) from the North in a clockwise fashion, as North to South (N-S) open space orientation or East to West (E-W) open space orientation. Open space orientation was measured on site using a magnetic north compass. Open space light angle variable (X34) was observed on open space samples when measured in degrees (Degree) from the vertical in a clockwise fashion from the base of the building or wall adjoining the open space. Light angles are also referred to as light plane (Singh & Singh, 2010, p.101). Open space light angles information was obtained from aerial images (Google maps) and site surveys (photographs and measurements). 90

Open space road proximity variable (X35) was observed on open space samples which had distance from the main or side road measured in metres (M). Road proximity was obtained from architectural drawings. Open space length variable (X36) was observed on open space samples and measured as the dimension of the longest side of a road path or open ground in metres (M). Open space length information was obtained from aerial images (Google maps) and site surveys (photographs and measurements). Table 1.19 (Appendix 1: Standard tabulated observation sheets) indicates standard tabulated built form open space attitude variables, description and explanation for open space size, hard landscape, open space orientation, light angle, road proximity, shading coefficient and open space length.

STANDARD TABULATED DIGITIZED OBSERVATION SHEET Operational definition of the variables required the digitizing of the tabulated observation sheets and the organization of the data into a format ready for uploading into software for processing the data, data analysis and results, synthesis and interpretation of the data. Table 1.20 (Appendix 1: Standard tabulated observation sheets) is a standard tabulated digitized observation sheet with details of batch data collection for general information (Komarock Infill B Estate, Nairobi), sample reference number (Sample), cluster details, sample name (plot or open space number), floor number (living room or garden area: ground floor) and orientation. Table 1.21 (Appendix 1: Standard tabulated observation sheets) is a standard tabulated digitized observation sheet indicating the information to be entered into the query workbook and should be read in conjunction with Query Workbook (See: Processing of raw information from data loggers).

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Table 1.22 (Appendix 1: Standard tabulated observation sheets) is a standard tabulated digitized observation sheet indicating the information displayed in the consolidated summary sheet for plot samples and should be read in conjunction with Consolidated Summary Sheet (See: Processing of raw information from data loggers). Table 1.23 (Appendix 1: Standard tabulated observation sheets) is a standard tabulated digitized observation sheet indicating the information displayed in the consolidated summary sheet for open space samples and should be read in conjunction with Consolidated Summary Sheet (See: Processing of raw information from data loggers).

ADVANTAGES AND DISADVANTAGES OF USING LONGITUDINAL RESEARCH DESIGN Table 1.24 (Appendix 1: Standard tabulated observation sheets) is a list of advantages and disadvantages of using longitudinal research design in the study of relationship between micro-temperature change and urban built forms. An advantage of seeking longitudinal data related to the sample (plot) allowed the analysis of the duration of the phenomenon on the relationship between micro-temperature change and urban built form. Longitudinal design enabled the survey researchers to get closer to the kinds of causal explanations usually attainable only with experiments. Longitudinal design permitted the measurement of differences or change in the micro-temperature change variable from one period to another (i.e. the description of patterns of change over time). Use of longitudinal studies facilitated the prediction of future outcomes based upon earlier factors (University of South Carolina Libraries, 2014: Longitudinal Design). Every advantage of a research design is coupled with a disadvantage. These disadvantages in the use and application of the longitudinal design in the study needed to be addressed if the results and findings of the study were to address the 92

research objectives. Longitudinal data collection method may change over time (University of South Carolina Libraries, 2014: Longitudinal Design). Longitudinal design was used in the study to ensure that the procedures selected were valid, objective and accurate (Rukwaro, 2016, p.36). Strict organizational structures and procedures were put in place by the study for the data collection, processing data analysis and presentation, and expected results as attested in the processing of raw information from data loggers. Thereafter, these systems were tested during a pilot study prior to their full implementation in the study. Another challenge in the use of the longitudinal design was that of maintaining the integrity of the original sample, over an extended period of time (University of South Carolina Libraries, 2014: Longitudinal Design). Each sample plot or open space was observed for a week during the study period. Microtemperature was expected to change and is the thrust of the study. Urban built form variables for the thirty plots and sixteen open spaces varied and were read as corresponding to the temperature change. Another challenge was that, it is difficult to show more than one variable at a time when using longitudinal design (University of South Carolina Libraries, 2014: Longitudinal Design). Data on the micro-temperature change dependent variable and the urban built form independent variables were captured in tabulated data (Appendix 2) and table of drawings (Appendix 3). Longitudinal design often needs qualitative research in order to explain fluctuations in the data (University of South Carolina Libraries, 2014: Longitudinal Design). Analogue and digital quantitative means of data collection, processing data analysis and presentation were used in the study. However, the results, synthesis and interpretation of findings, conclusion and recommendation were qualitative in nature. Longitudinal research design assumes that present trends will continue unchanged (University of South Carolina Libraries, 2014: Longitudinal Design). 93

The micro-temperature change trends, results, synthesis and interpretation of findings, conclusions and recommendations were made in line with the scope and limitations of the study. It can take a long period of time to gather results (University of South Carolina Libraries, 2014: Longitudinal Design, indeed, the study collected data in a rather long period, i.e., 8th June 2013 to 19th September 2015. Another challenge was that there is a need to have a large sample size and accurate sampling in order to

achieve representativeness (University of South

Carolina Libraries, 2014: Longitudinal Design), the current study sampled 30 plots out of a plot population of two hundred and forty.

PANEL STUDIES AND LONGITUDINAL DESIGN Panel studies, which are a form of longitudinal design, were used in the study, whereby a sample (plot) of the population (total plots in Komarock Infill B Estate) was selected and observations made at regular intervals over time (Mugenda, 2011, p.73). Other types of longitudinal design include cohort, time series and retrospective. A description of these other forms of longitudinal designs indicates why the study opted for the panel study. In cohort studies, multiple observations of the characteristics of interest are made over multiple time periods. A cohort is a group of subjects, units or items that share common characteristics or experiences within a defined time period. Time series studies make observations on a single phenomenon over multiple time period and retrospective studies investigate past events rather than future occurrences (Mugenda, 2011, pp.772 – 74).

LIST OF VARIABLES ASSESSED Longitudinal design was deemed as appropriate in that it enabled the study to plan, manage and collect the data, as well as to analyze the data, and to realize the research objectives of the study (Rukwaro, 2016, p.35). 94

Table 1.25 indicates a list of variables assessed, variable operation model and data needs for the study, related to the micro-temperature change variable. Table 1.26 is a list of variables assessed, variable operation model and data needs for the study, related to plot attributes. Table 1.27 is a list of variables assessed, variable operation model and data needs for the study related to the urban built form variable (building surrogates). Table 1.28 (Appendix 1: Standard tabulated observation sheets) is a list of variables assessed, variable operation model and data needs for the study, related to the urban built form variable (open space surrogates).

TEST FOR SIGNIFICANCE AND LEVEL OF CONFIDENCE After providing the longitudinal models for operationalizing the microtemperature change variables and urban built form surrogates, it was incumbent upon the study to state how it intended to test for the significance and level of confidence of the models adopted. Generally speaking, the mean, mode, median, maximum and minimum measures and values for identified variables and surrogates were suitable estimators for the population mean for the study on micro-temperature change and urban built form in Komarock Infill B Estate (King’oriah, 2004, p.144). Narrow margins between the estimator and population mean provided higher levels of confidence in the results of the study. Population mean is related to the central limit theorem, which states that as the sample size increases, the sampling distribution of means approaches a normal distribution (Mutai, 2001, p.152). Thirty plots and 16 open spaces were sampled in the study. Mutai (2001, pp.152 – 153) suggests that with 30 plots sampled at random from a population of 240 plots, three situations would subsist. Firstly, the means of the samples would be normally distributed. Secondly, the mean value of the sample means would be the same as the mean of the population. And thirdly, the distribution of sample means would have its own standard deviation and a standard

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error of the mean. Standard error of the mean for the variables and surrogates in the study was calculated using the following formula: SẊ = S ÷ √N

(Formula 3.28)

Where SẊ was the standard error of the mean, S was the standard deviation of individual scores and N the size of the sample. Standard deviation of the variables and surrogates in the study was calculated using the following formula: σ = √(Ʃ(X – μ)2 ÷ N)

(Formula 3.29)

Where σ was the population standard deviation, Ʃ was sigma (the sum of), X the value of observations in the population, μ the mean of the population and N the number of observations in the population. With a normal distribution of observations, approximately 68 percent of the observations would fall within one standard deviation of the mean, 95 percent of the observation would fall within two standard deviations of the mean, and practically all (99.7%) would fall within three deviations of the mean (Mutai, 2001, p.153). The confidence level is expressed as a percentage and implies the number of times out of 100 that test results can be expected to be within a specified range (Mugenda & Mugenda, 2012, p.60). The study set a confidence level of 0.95 (95%) corresponding to a significance level of 0.05 (1 – 0.95). Rejecting the study null hypothesis at the 0.05 significance level for the variable or surrogate, indicates a difference in means as large as that found between experimental and control groups would have resulted from sampling error in less than 5 out of 100 replications of the experiment. This suggests a 95 percent probability that the difference was due to the experimental treatment rather than sampling error.

3.3 DATA SOURCES Data sources can be classified as either primary data or secondary data sources. Primary data sources for the study provide direct evidence concerning the problem under investigation (Rukwaro, 2016, pp.36 – 37). 96

Table 1.29 (Appendix 1: Standard tabulated observation sheets) is a list of variables assessed, the data needs and primary data sources for the study. The column of the table indicates the variable assessed, data needs and primary data sources. Digital logging was the primary data source used in the study. Others included surveys and related sources, measurements and related sources, observations and related sources and calculations and related sources.

DIGITAL LOGGING SOURCES Digital data loggers were the primary data source and were used to assess the dependent variable micro-temperature change for plot or open space ‘a’ (Ya Ave), average temperature change for plot or open space ‘a’ (ΔTa Ave), minimum microtemperature change (YMin), minimum temperature (T1 temperature change (YMax), maximum temperature (T1

Min), Max),

maximum micrototal number of

temperature (na) and baseline temperature (nB) data points. Data needs were temperature in degree Celsius (oC) and number of data points (No).

Figure 3.3: Shows a data collector placing Data Logger 1 Internal to a plot. Source: Field survey (2013).

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Digital devices provide uninterrupted data logging compared to the analogue devices which could only be used for instantaneous data. The data was also in digital format and could be easily tabulated in a spreadsheet, which in turn could be used for processing the data (See: Processing of raw information from data loggers). Generally speaking, a batch or set of data collected utilized five data loggers, with two loggers per plot and a logger for the open space. The loggers for the plots were placed on the internal and external wall surface, in order to ensure that the probes were lifted away from the surface as it is the air temperatures rather than the surface temperatures that were to be measured. Data loggers were also placed in a shade with adequate air flow in order to measure the air temperature and not the radiant temperature. Figure 3.3 shows a data collector placing a data logger in a plot, while Figure 3.4 shows the actual data logger.

Figure 3.4: Shows the actual data logger. Source: Field survey (2013). Analogue devices were provided to the research assistants so that they could observe the commencement and completion readings (See: Processing of raw information from data loggers). A wet and dry bulb hygrometer was provided to the researchers for the commencement and completion of temperature readings, a watch for the commencement and completion of time readings, a wind anemometer for 98

checking wind speeds, a wind gauge for the direction of the local winds and a magnetic north compass for the orientation of the buildings and open spaces. The data collected was tabulated and diagrams drawn for comparison with those received from the meteorological station for the study site.

SECONDARY DATA SOURCES Secondary data sources are retrieved from storage or existing documents and used by someone else other than the person who collected it. Because of advances in technology, there are many tools available to assist researchers locate secondary information including online and printed resources, journals, websites, books and articles and communication with experts through mobile technology, email, skype, etc. (Mugenda & Mugenda, 2012, p.294). Table 1.30 (Appendix 1: Standard tabulated observational sheets) is a list of variables assessed, data needs and secondary data sources for the study. Secondary data sources can be grouped as architectural drawings and related sources, google maps and related sources, meteorological station and related temperature sources. Other sources include review of related literature sources.

ARCHITECTURAL DRAWINGS AND RELATED SOURCES Architectural drawings and related sources were used to assess urban built form variables and surrogates related to building type, plot and open space sizes, orientation and road proximity. Data needs were in the form of site and location plan of study site, orientation, typical building plans and sections, sample population, building types and classification, plot and open space size, building and open space dimensions. Architectural drawings in the form of plans, sections and elevations were obtained from the developer and financier (Housing Finance Company of Kenya: HFCK), Nairobi City County (NCC) and other authorities including the architect 99

(MMI Architects), project manager and user of the buildings and open spaces in the study area. Architectural drawings and the other secondary sources were digitized using the AutoCAD 2007 software (See: Processing of raw information from data loggers). These formed a basis for creating basic research drawings for conducting an initial field survey as well as hard copy negatives from which blue prints were made of the Komarock Infill B Estate location plan and master plan indicating the sample population from which data collection and field surveys would be based. Figure 3.5 shows the site plan of Komarock Infill B Estate. Komarock Infill B Estate is approximately 4.72 hectares with a length of 478.8 metres and a general width of ninety seven meters, with an orientation of forty six degrees north and with a six metre fall across the site. Architectural drawings were also used to develop the drawings used in the study in the form of consolidated batching summary sheets, batch and plot details (See: Appendix 3: Table of drawings).

GOOGLE MAPS AND RELATED SOURCES Google maps and related sources are secondary data source and can be grouped as Google Earth maps, Commission for the Implementation of the Constitution (CIC) maps and Japan International Cooperation Agency (JICA) maps. Google maps were used to assess urban built form variables and surrogates related to the plot attributes, hard landscape coefficient (X14: %) and shading coefficient (X11: %). Data needs were in the form of size of open space, building type and classification, location and size of trees, soft and hard landscape coverage, and roof plan of the buildings located within the site. Commission for the Implementation of the Constitution maps were used to develop a neighbourhood plan and the data need for a Nairobi Metropolis region map. Japan International Cooperation Agency maps were used to assess the urban

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built form variable related to the building and open space surrogate road proximity. Data needed for the study was obtained from the site location plan. As is normal practise, preliminary secondary data was gathered before primary data and bulk secondary data collection. The use of preliminary secondary information during problem definition and formation paved way for a more focused primary and secondary data collection (Ngau & Kumssa Ed., 2004, p.71). Digital maps from Japanese International Cooperation Agency (JICA), Nairobi City County (NCC) and Google Earth for the study area were used as aerial views and served as secondary source for urban built form variable and surrogate assessment and to develop a roof and site plan of the study site. Figure 3.6 shows the roofscape. Information about the size, orientation and consistency of the building and open space were obtained from aerial photographs (Google Earth), imagery from the Survey Department of the Government of Kenya, and from the University of Nairobi.

METEOROLOGICAL STATION AND RELATED TEMPERATURE SOURCES Meteorological station and related temperature sources are secondary data sources and can be grouped as Kenya Meteorological Department (KMD) and United Nations Climate Change Secretariat (UNCCS) sources. Data sources from Kenya Meteorological Department (KMD: 1984, p.61: Jomo Kenyatta International Airport) were used for secondary data resources and were useful in the assessment of micro-temperature change variables related to the baseline temperature. Data need was in the form of temperature data in degree Celsius (oC). United Nations Climate Change Secretariat (UNCCS: 2015a) and the secondary data sources were used to assess micro-temperature change variable related to the limit of climate change. Data need was in the form of temperature data in degree Celsius (oC).

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102

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Data from the meteorological station and related temperature sources served as secondary data, and yet was the only data available that could be moderately suited to assess the micro-temperature change variable. This form of secondary data, were less expensive to source in terms of money, time and effort, than primary data The study obtained a large amount of relatively free data from literature review of books, the internet and in the form of meteorological station data. However, the study had to accept the methods that the meteorological stations use to collect data (Montello & Sutton, 2013, p.62). This included limitations and omissions in publications of temperature data and in the way that the data is collected, processed and presented (Koenigsberger et al., 1973, p.13). The study selected meteorological station data (See: Processing of raw information from data loggers) from Jomo Kenyatta International Airport (Kenya Meteorological Department, 1984, p.61) which is the nearest meteorological station to the study site and identified baseline temperature data in degree Celsius (oC) for the study.

3.4 RESEARCH TOOLS Research tools are used to capture and measure a given phenomenon. Preparation of research tools such as observations should be done in precise detail prior to embarking on field data collection. Research tools should target the objects or respondents so that their responses help to answer questions. The quality of data gathered is dependent upon the number of tools used, the level of measurements whether nominal, ordinal, interval or ratio, and the sample size. The different tools may be used to ensure triangulation of the data and its reliability. Each research tool needs to have a general description, its justification and the level of measurement of variable. Research tools should adhere to ethical guidelines (Rukwaro, 2016, p.39). Table 1.31 (Appendix 1: Standard tabulated observation sheets) shows the research tools that the study formulated. The tools development was based on the 104

research questions, the observations to be investigated, the variables required and details on which data should be measured. The first research question read “what are the urban built form variables which cause temperature change in Komarock Estate study site? Here the observation to be investigated was micro-temperature change. The variables required here were: micro-temperature change for plot or open space ‘a’ (Ya Ave), average temperature change for plot or open space ‘a’ (ΔTa Ave),

minimum micro-temperature change (YMin), minimum temperature (T1

Min),

maximum micro-temperature change (YMax) and maximum temperature (T1 Max). Data was measured in degree Celsius (oC). Research question two read “what influences do the significant urban built form variables have in contributing to the temperature change” .The observation to be investigated was the urban built form variables. The variables required were: building orientation (X1: Degree) and open space orientation (X8: Degree), building classification (X2: M), building road proximity (X3: M) and open space road proximity (X9: M), building type (X4: M), plot size (X5: M2) and open space size (X13: M2) and open space length (X12: M). Data requiring measurement was : orientation of site, buildings and open spaces, distance to nearest road, dimensions, quantities and time. Research question three read “what is the impact of temperature change on design and planning strategies for sustainable urban built form”? The observation to be investigated was the urban built form surrogates. The variables required were: ground coverage (X6: %), plot ratio (X7: %), open space light angle (X10: Degree), open space shading coefficient (X11: %) and open space hard landscape coefficient (X14: %). Data was measured in terms of the heights of adjoining buildings to open spaces, tree numbers, dimensions, quantities and time.

3.5 OBERVATION METHOD Observation was one of the techniques that the study used to collect the data. The study required recording what was observed from a sample of subjects or 105

objects, related to specific behaviors, events, occurrences, characteristics, etc. The researcher used a structured observation checklist to record the information observed (Mugenda & Mugenda, 2012, p.218). The dependent variable which was micro-temperature change for the sample plot or open space was measured using a scale of temperature in degree Celsius, and the observation recorded. Likewise, observation of the urban built form variables and surrogates for the independent variables in the study area were also recorded. Table 1.32 lists the advantages and disadvantages of using observation method in the study on relationship between micro-temperature change and urban built forms. Table 1.33 (Appendix 1: Standard tabulated observation sheets) lists the variables assessed, data needs and research tools for the study. Research tools were observation book and sheets, batching journal, data logger journal, check lists and tabulations.

OBSERVATION BOOK AND SHEETS Observation book and sheets were one of the tools used in the study to assess the variables of micro-temperature change and urban built form. The data captured in the summary page included: general information and raw data from field study, micro-temperature change and urban built form analysis, and simulation techniques as indicate in the observation sheet (See: Pilot study), consolidated batching summary sheet, batch and plot details (Appendix 3: Table of drawings).

BATCHING JOURNAL Batching journal was a tool used in the study, to assess data collected in batches. The data was recorded under batch number (No), cluster and plot numbers (No) and expected results. Table 1.34 (Appendix 1: Standard tabulated observation sheets) is a sample tabulated batching journal and it tabulates observations made on

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batch number (1 – 15), the comparative study on cluster, plot number or open space number and the expected results based on remarks , testing and investigating results.

DATA LOGGER JOURNAL The data logger journal was another tool used to assess the data collected and to log in the logger details. The data needs for the study were: the commencement and completion date (date), batch number (No), cluster and plot numbers (No) and expected results. Table 1.35 (Appendix 1: Standard tabulated observation sheets) is a sample of the tabulated data logger journal and tabulates observations on the date of data logging commencing 8/6/13 to 19/9/15 , batch number (1 – 15), comparative study on cluster, plot number (30) or open space number (16) and expected results based on remarks, testing or investigating results.

CHECK LISTS AND TABULATIONS Check lists was another research tool used in the study. Through the checklist, the study assessed the level of compliance with regulations and standards. Data needs were for compliance to procedures and protocols, orientation of site and units (Degree), commencement and completion temperature (oC) and time data (Hours), transformation and modifications of typical units by users, occupancy pattern (Hours), number of occupants (No) and light bulbs (No), dimensions and asbuilt sketches of non-typical units. Tabulations were used as a tool for processing data and analysis. Data needs were for item, descriptions, data tabulation and remarks information.

3.6 SAMPLING DESIGN The sampling design for the study can be discussed generally as sample population and unit of analysis, plot attributes, planning and design attitudes, sampling technique, batching design and cluster sampling. 107

SAMPLE POPULATION AND UNIT OF ANALYSIS A sample has been defined as the number and/or identification of respondents in the population who will be or have been included in the survey (Alreck & Settle, 1995, p.443). The sample population for the study was 240 plots. Unit of analysis has been defined as the basic observable entity or phenomenon being analyzed by a study and for which data is collected in the form of variables (University of South Carolina Libraries, 2014: Glossary of Research Terms). Based on a preliminary search for information, a basic plot measured approximately one hundred and eight square metres in area. This varied depending on whether it was a corner plot, single unit or double storey, social amenity, front row, back row, on the main road, had a service lane next to it or behind it etc. Random sampling was used in the sampling procedure, here the researcher first numbered the plot and open space population according to their respective plot numbers, then used the random numbers table to select the sample. The sample was random because there was no regular or discernible pattern or order. As recommended, thirty plots were selected at random from the sampling population to give a representative sampling to meet the research objective and questions set for the study (Mutai, 2001, p.152).

3.7 PLOT ATTRIBUTES Plot attributes were used for sampling plots and open spaces based on how users of a city perceive the city image and its elements (Lynch, 1960). Plot attributes were grouped as districts, nodes, edges, landmarks, paths and axis. Administratively, the study site was divided into three Districts for the purpose of collecting data. Nodes may primarily be junctions, places of a break in transportation, a crossing or convergence of paths, moments of shift from one structure to another 108

(Lynch, 1960). In the study site, nodes were usually the open spaces. Figure 3.7 shows a road intersection as a node in the form of an Open Space (OG5).

Figure 3.7: Shows a road intersection as a node in the form of an Open Space (OG5). Source: Field survey (2013).

Edges were the boundaries between two phases, linear breaks in continuity, edges of development (Lynch, 1960). Figure 3.8 shows an edge plot, while Figure 3.9 shows a corner plot. Landmarks include specific buildings, signs, mountains and even distinctly observed districts in the site landscape (Lynch, 1960). Figure 3.10 shows a school, while Figure 3.11 shows shops.

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Figure 3.8: Shows edge plots. Source: Field survey (2015).

Figure 3.9: Shows centre plots. Source: Field survey (2015). 110

Paths may include the streets, walkways, transit lines, canals and railways which serve the city, and other spatial units that the viewer perceives (Lynch, 1960). Figure 3.12 shows a pedestrian circulation (path), while Figure 3.13 shows a vehicular circulation (roads).

3.8 PLANNING AND DESIGN ATTITUDES Planning and design attitudes were part of the criteria that the study used to derive the sampling and cluster design for purposes of collecting data. These aspects of the urban built form variables and surrogates were assumed to have an impact on the dependent variable micro-temperature change and were based on the review of related literature. Planning and design attitudes were grouped as building types, plot size, orientation, road proximity and building classification.

Figure 3.10: Shows a school (Plot 19) as landmark. Source: Field survey (2015).

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Figure 3.11: Shows a shop (Plot 69) as landmark. Source: Field survey (2015).

Figure 3.12: Shows a path (P6). Source: Field survey (2015). 112

Figure 3.13: Shows a road (R7). Source: Field survey (2015).

Building Types were mainly composed of Maisonettes (Figure 3.14) or Villas (Figure 3.15).

Figure 3.14: Shows a row of Maisonette. Source: Field survey (2015). 113

Figure 3.15: Shows a row of Villas. Source: Field survey (2015).

Orientation of plots were mainly North to South (N-S) orientation, East to West (E-W) orientation. Road Proximity was mainly defined for corner plots which were internally located, large plots which were located near the main road and basic plots. Building Classification was mainly defined as change of user plots for detached houses, semi-detached houses and row house plots.

3.9 SAMPLING TECHNIQUE Cluster sampling is a probability sampling technique applied when natural groupings are evident in the target population. The total population is divided into naturally-occurring groups or clusters and any number of groups is selected as the sample. Although the elements within each cluster should ideally be as heterogeneous as possible, there should be homogeneity among cluster means. Each cluster is considered to be a small scale version of the target population. The clusters should be mutually exclusive and collectively exhaustive. A random 114

sampling technique should then be used to select which clusters to include in the study. In single-stage cluster sampling, data is collected from all the units in each of the selected clusters. Alternatively, data may be obtained from a sample randomly picked from each selected cluster (Mugenda & Mugenda, 2012, p.50). A Cluster is defined as one group of individuals or sampling units that have proximity with one another within the sample frame in some respect, such as those within a given area (Alreck & Settle, 1995, p.443). Based on these definitions, the study collected data by allowing the researcher to group data according to certain characteristics or clusters. Cluster sampling was undertaken for the plots and open spaces, which served as the units of analysis for the study. Cluster design was primarily based on plot attributes, planning and design attitudes. A combination of the planning and design attitude based on building orientation variable (X1) and building type variable (X4) gave rise to Cluster 2 (Basic Maisonette N-S Orientation), Cluster 3 (Basic Maisonette E-W Orientation), Cluster 4 (Basic Villa N-S Orientation) and Cluster 5 (Basic Villa E-W Orientation). Another combination of the planning and design attitude based on building plot size variable (X5) and building road proximity variable (X3) gave rise to Cluster 6 (Corner plot internally located) and Cluster 7 (Large plot near main road). These clusters were designed based on the plot attribute of edge. Singular planning and design attitude such as building classification variable (X2) gave rise to Cluster 8 (Transformed plot) and Cluster 10 (Change of user plot). These clusters were designed based on the plot attributes of landmarks. Open spaces were generally grouped as open grounds, roads and paths and the cluster design took into account the plot attributes nodes and paths. In the study, open space clusters were Cluster 1 (Open Ground), Cluster 11 (Road: Vehicular

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Circulation) and Cluster 12 (Paths: Pedestrian Circulation) based on the design and planning attitude of road proximity. Planning and design attitudes were also directly sampled, these included building classification based on the classes of residential buildings (Change of user plots, detached house plot, semi-detached house plot and row house plot) and with the plot attribute of districts. Table 1.36 (Appendix 1: Standard tabulated observation sheets) shows tabulated cluster sampling (plots), description and explanation. Figure 3.16 shows the sampling of clusters. Colour coding and use of standardized graphics of the clusters visually and logistically assisted the study to carry out the sampling procedure.

3.10 BATCHING DESIGN AND CLUSTER SAMPLING The study had at its disposal five data loggers for purpose of measuring air temperature. Two data loggers were used for each building cluster and a single data logger was used for the open space cluster. This arrangement for collecting data for the study was referred to as a Batch. The Cluster sampling adopted in the study was that each batch captured two building clusters and one open space cluster, based on the batch design and tabulated in a Batching Journal (Table 1.34 – Appendix 1: Standard tabulated observation sheets) , prior to the data collection exercise. The batching philosophy was designed such that the clusters in each batch were measured based on the plot attributes, planning and design attitudes so as to address the research objectives of the study. An example in the application of this philosophy was Batch 1, where Cluster 4 (Basic Villa N-S Orientation) and Cluster 5 (Basic Villa E-W Orientation) comprised the same building type (Basic Villa) but had different orientations, where orientation was the urban built form variable measured.

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Another batch may have had the same orientation, but with diverse building types. For example Batch 7, where building types (Basic Villa and Basic Maisonette) were the urban built form surrogates measured. Out of the five data loggers, four were for the building clusters and one for the open space cluster. Of the two data loggers for the building cluster 1, one was placed internally to capture the internal air temperature (T1

Int)

and externally to

capture the external air temperature (T2 Ext). Likewise for building cluster 2, one was placed internally to capture the internal air temperature (T3

Int)

and externally to

capture the external air temperature (T4 Ext). The fifth data logger served the open space cluster and captured the air temperature outside (TO). This arrangement was apt, tested and illustrated in the pilot study. Table 1.35 (Appendix 1: Standard tabulated observation sheets) shows a tabulated data logger journal, while Table 1.36 indicates the tabulated cluster sampling (plots), description and explanation.

3.11 COLLECTION, PROCESSING AND PREPARATION OF DATA All the data collected from both primary and secondary was processed and placed in a format ready for data analysis.

PROCESSING OF RAW INFORMATION FROM DATA LOGGERS The study processed the raw information from data loggers, details on issues from the query workbook, the data collection logbook, meteorological station data, temperature templates, the consolidated batch workbook, ebstats software, the consolidated summary sheet, and then processed data analysis in summary.

QUERY WORKBOOK Digital data was input in the Query Workbook using the Ebstats Software. The researcher highlighted the items that require intervention by the user, while all 118

the other items were computed by the software. The Query Workbook made up four types of information, namely the general information, technological data, societal data and climatic data (Table 1.21: Appendix 1 – Standard tabulated observation sheets). The columns of the table indicated the item, description, details and remarks which were filled-in by the user of the software. General information of the query workbook related to the context and title of the study (Komarock Infill B, Nairobi). Sample reference number related to the batch, cluster, plot, variable, plot attribute or planning and design attitude that was being monitored in the study case (See: Batching Design) (Sample 1). Sample name was the description in simple words of the sample reference number (See: Appendix 3 Table of Drawings) and in this case referred to Drawing Number 2 (Appendix 3: Table of drawings - Batch 1 Plot Details: Diagram 4 – Location Plan: Clusters, Cluster 4’ and Column 4 as ‘Basic Villa N/S Plot 41). It is important to note that AutoCAD Drawings for all clusters, building types and units had by this time been made (See: Appendix 3: Table of drawings). Floor number was a reference to digitized drawings prepared for the study and related to the space studied or analyzed (See: Appendix 3: Table of Drawings) and in this case referred to Drawing Number 2 (Appendix 3: Table of drawings Batch 1 Plot Details: Diagram 2 Cluster 4 Details and Diagram 5 Dependent/Independent

Variable

Analysis,

Living Room, Ground Floor).

Orientation was a reference to the orientation of the sample and was obtained from the site plan of the study Drawing Number 2 (Appendix 3: Table of Drawings Batch 1 Plot Details: Diagram 5: Dependent/Independent Variable Analysis, NE Facing: 45 Degrees). Technological data referred to the logistics of the building. It could be obtained from architectural drawings and more specifically, from digitized drawings prepared for the study (See: Appendix 3: Table of Drawings). Technical specifications and literature can be got from Koenigsberger et al. (1973, pp.285 – 119

287 and p.291), Littlefield Ed. (2008, pp.35.1 – 35.41 and pp.39.1 – 39.35) and Neufert and Neufert (2000, pp.111 – 116). It is important to note that this version of the software was programmed to simulate the effect of a single room called the Test Cell. In the case of this study, the living room of the individual plots was the unit of measurement. The length of the room was 3 Metres, width of 4.1 Metres and height of 2.4 Metres. Schedule of materials comprised of a concrete block external wall with the height of the wall as 2 Metres, transmittance of the wall as 3.18 W/m2oC, absorbance of wall had a coefficient of 0.7 and surface conductance of 13.18 W/m2oC. A window on the external wall with single glazing had a glass height of 0.4 meters. It is important to note that the software calculates area of openings such as windows as if it were a long horizontal window across the length of the room. Thus it was important to sketch the true condition on the ground and the position assumed by the software. Thus, the user of the software calculated the area equivalence of the strip in comparison to the hole. Figure 3.17 depicts one of the sketches. The transmittance of the glass was 5 W/m2oC (See: Koenigsberger et al., 1973, p.288) and solar gain factor of coefficient of 0.75 (See: Koenigsberger et al., 1973, p.79).

Figure 3.17: Base station sketch of window area input into Ebenergy Software. Source: Field survey (2013). 120

Societal data are standards and expectations of the users and other stakeholders in regards to thermal comfort and other peculiarities of the space or building. They are related to literature and standards from ISO Standards (ISO 7345 1996, ISO 7726 2001 and ISO 7730 1995), Kenya Bureau of Standards (2007), British and American Standards. Information was obtained during the field survey and observation by the research team. Design indoor temperature is the temperature expectation of the user and Koenigsberger et al. (1973, p.78) recommends the room temperature to be maintained at 20 degree Celsius. Ventilation rate for the window in the form of the number of air changes per hour was 3 (See: Koenigsberger et al., 1973, p.78). Occupancy pattern related to the usage of the space in terms of the times of use, number of users or fittings and the activity. The amount of time spent within the room (Unit occupancy/day) was established as 13.5 hours by the field survey. And likewise, the number of occupants was 2 occupants, generating heat of 140 watts per occupant (See: 2.1.2 The body’s heat production - Koenigsberger et al., 1973, p.42). Number of electric bulbs in the room was only 1, which generated heat of 60 watts per bulb as established from the field survey. Climatic data for the region in question was obtained from the meteorological department or station for the country and area under study (See: Jomo Kenyatta International Airport Meteorological Station, Embakasi Nairobi Kenya Meteorological Department, 1984, p.61). Design outdoor temperature was calculated by the software and was the daily maximum temperature for the month in question; in this case, it was established as 23.6 degree Celsius. The incident radiation falling on the unexposed wall was established at 580 Watts per square meter (See: Koenigsberger et al., 1973, p.79). Geographical positioning of data for the region in question was obtained from the meteorological department or station for the country and area under study 121

(See: Jomo Kenyatta International Airport Meteorological Station, Embakasi Nairobi - Kenya Meteorological Department, 1984, p.61). The station name was Nairobi Jomo Kenyatta International Airport Meteorological Station, station number 91.36/168, latitude 01.19 South, longitude 36.55 East and altitude 1624 metres above sea level. Baseline climate temperature was derived from the data logger journal during the month when the temperature logging was being undertaken. Temperature input for Plot 41 which was a Basic Villa generally facing a North South orientation was in the month of June. Once the inputting of data into the Query Workbook of the Ebenergy Software was completed, the software processed the data and calculated a Comfort Score for the space or building under analysis. The comfort score for Batch 1 Villas Data (Urban Built Form Analysis, Mature Architecture) was negative 16.4 Kilowatt hour (KWh). This means that one needs about 17 Kilowatt hours cooling in the space to maintain comfort at the design indoor temperature of 20 degree Celsius for the month of June, and 23.6 degree Celsius design outdoor temperature. The negative (-ve) figure meant that the space or building required cooling to restore balance, while a positive (+ve) figure implied that the space or building required heating to restore the balance of an equivalent rating. One can check and simulate various scenarios by changing the variables and this provides a tool for simulation work such as the basis for the urban built form study. Once the Consolidated Sheet (Batch 1 Villa Data, Logger 1 Plot No. 41, Location Lounge)was filled in, the Ebenergy Software permutated the other to calculate the weekly average (Column) and daily average (Rows). Note that relative humidity figures were also worked out, however these fell outside the scope of this study although they are available to other researchers. The rounding of figures in the Summary Sheet (Batch 1: Villa Data) were automatically updated for the Average Logger 1 (Ave Log1 41 Lounge) data. Other

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information derived from other quarters of the software of the other loggers completed the picture. Information from the other spreadsheets were used to feed the Microtemperature Change Analysis (Batch 1 Villa Data) and automatically worked out a spreadsheet ready for analysis of Observations for Batch 1. The study prepared proceeded Observation Sheets for the different batches of data collected based on the batching design and thereafter carried out the analysis of the same.

DATA COLLECTED LOGBOOK Digital data was inputted in the Data Collected Logbook Spreadsheet (Batch 1, Villas Data Logger 1 Plot No. 41 Lounge Consolidated Sheet) of the Ebenergy Software. Table 1.37 (Appendix 1: Standard tabulated observation sheets) shows the tabulated data logged for Logger 2 (Plot 41 External) for the week 8th to 15th June 2013. The column of the table marked ‘Rounding (41 Ext)’ displays the data collected for Plot 41 in the garden area for a twenty four hour daily cycle. Table 1.38 (Appendix 1: Standard tabulated observation sheets) shows the tabulated average data logged temperatures for Logger 2 (Plot 41 External) for the month of June. Notice that the temperatures for 0.00 and 24.00 hours are almost the same figures (18.7 – 18.4 = 0.3 degree Celsius), and that the minimum temperatures for Plot 41 (External) was achieved at 7.00 hours (16.4 degree Celsius) and maximum temperatures at 13.00 hours (29.5 degree Celsius).

METEOROLOGICAL STATION DATA Climatological statistics of Kenya obtained from the Kenya Meteorological Department (1984) covers climatic information for data until 1980 (Printed 1984) for close to 100 stations in Kenya , Nairobi is listed as having five stations: Headquarters

Meteorological

Station

(Dagoretti:

Kenya

Meteorological

Department, 1984, p.60), Jomo Kenyatta International Airport Meteorological 123

Station (Embakasi, Kenya Meteorological Department, 1984, p.61), National Laboratories (Kenya Meteorological Department, 1984, p.62), Wilson Airport Meteorological Station (Kenya Meteorological Department, 1984, p.63) and Kabete Observatory Station (Kenya Meteorological Department, 1984, p.64). The climatological statistics were used to prepare a climatic and also bioclimatic analysis for the region under study.

The researcher chose the

information site close to the study site in this case, the Jomo Kenyatta International Airport Meteorological Station (Embakasi) seemed the most appropriate and was used for this study. The Jomo Kenyatta International Airport (Embakasi) Station climatological statistics is given as Station Name Nairobi (JKIA), Station Number 91.36/168, Latitude 01o 19’S Longitude 36o 55’S, Altitude 5327 Feet or 1624 Metres. Henceforth, the used the Jomo Kenyatta International Airport meteorological data as the bench mark for the Komarock Region. Table 1.39 (Appendix 1: Standard tabulated observation sheets) shows the critical data inputting into the Ebenergy Software for analysis and bench-marking of the primary data in relation to the dependent variable. Note that the meteorological station provided data for the times of the day based on the Greenwich Mean Time (GMT) which is generally behind by three hours on the Local Mean Time (LMT) for Kenya. Temperature data in degree Celsius was provided for 500 Local Mean Time (LMT) which recorded the minimum average temperatures, dry bulb temperatures for 900, maximum average temperatures for 1300 and dry bulb temperatures for 1500 Local Mean Time (LMT) for each of the twelve months of the year. It also shows that generally speaking, the hottest month for the year was March while July recorded the coldest average temperatures.

TEMPERATURE TEMPLATE The four temperature data points provided by the local meteorological station were insufficient in plotting a daily temperature regime picture for the 124

twenty four hour day and to analysing a particular month of a climatic region. In traditional schools of architecture, daily temperature regimes are manually plotted on a graph and through extrapolation, the daily temperature circle is derived. This is both cumbersome and immensely inaccurate for research work in a digital world. This is why the study made the decision to utilise the concept of the Temperature Template. The algorithm of the Temperature Template Software uses straight lines to link the four said coordinate points and establishes two other points given by the X coordinates of 0 and 24 hours. In the case of the month of June, the Y coordinates were established as the same at 15.4 degree Celsius. Table 1.40 (Appendix 1: Standard tabulated observation sheets) shows the tabulated baseline temperatures for the month of June generated by the Temperature Template Workbook of the Ebenergy Software. With the availability of the tabulated daily temperature cycle (temperature time series) for the different months of the year when temperature data was collected on site and the generated daily baseline temperatures for the same data points, it was now possible to have a basis for calculating the temperature change at the micro-level of 1.5 metres above the ground for the different sampled plots and open spaces of the built form variable on an hourly basis and also to calculate the average micro-temperature change for that particular plot. Table 1.41 (Appendix 1: Standard tabulated observation sheets) shows the resultant data logged for Logger 2 (Plot 41 External) for the week 8th to 15th June 2013 and tabulated baseline temperatures for the month of June. Plot 41 results in a micro-temperature change of 3.9 degree Celsius and results for the other twenty nine plots sampled within the study area are shown in Table 1.42 (Appendix 1: Standard tabulated observation sheets). The average micro-temperature change for the plots for Komarock Infill B Estate for the period 8th June 2013 and 19th September 2015 was 3.4 degree Celsius,

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while Plot 99 results show the minimum of 1.4 degree Celsius and Plot 142 the maximum of 7.2 degree Celsius. Micro-temperature change sampled within the study area for the sixteen open spaces are shown in Table 1.43 (Appendix 1: Standard tabulated observation sheets). The average micro-temperature change for open spaces for Komarock Infill B Estate for the period 8th June 2013 and 19 th September 2015 was 3.7 degree Celsius, while Open Space R9 results show the minimum of 1.6 degree Celsius and Open Space OG8 the maximum of 7.5 degree Celsius. Each of the 30 sampled plots and 16 open spaces collected data in 15 batches, reporting that micro-temperature change was dependent variable Y in degree Celsius, and had a corresponding significant and insignificant independent X variables of the built form.

BATCHING PLOT DETAILS Drawings of the data collected were prepared for the 30 batches in order to safeguard the data and to provide an avenue for easy access retrieving the information during the digitization process (Appendix 3: Table of drawings). Batching plot details are displayed as plot details for example, Batch 1 (Drawing 2 Appendix 3: Table of drawings) is a single sheet which gives all the vital information about the batching for ease of reference and shows the open space details (Diagram 1), Cluster 4 details (Diagram 2), Cluster 5 details (Diagram 3), location plan for clusters (Diagram 4), and dependent and independent variable analysis (Diagram 5). All the information collected about the batches, clusters, variables, plots etc. was therefore in one place for use in the data analysis stage. Open space details are shown in Diagram 1 of Drawing Number 2 (Appendix 3: Table of drawings), starting with the top of the diagram with the Cluster Number (e.g. No. 1A), colour code and description of the cluster (e.g. Cluster 1A: Open Ground), total number of open ground spaces within the cluster (e.g. No. 14), an explanation of the cluster (e.g. Primarily 10% of the total area is normally set aside for amenities, social and services), description (e.g. Open 126

Ground) and a site plan (e.g. OG3) showing the location and the context of the sample. Cluster details, for example Cluster 4 are shown in Diagram 2 of Drawing Number 2 (Appendix 3: Table of drawings), starting with the top of the diagram with the Cluster Number (No. 4), colour code and description of the cluster (Cluster 4: Basic Villa – N – S Orientation), total number of plots within the cluster (No. 61), Ratio of sampling to total plots in the cluster expressed as a percentage (25.4%), an explanation of the cluster (Smaller Unit in the ‘Desired Orientation’), description (Basic Villa on N – S Orientation) and a Ground Floor Plan, Section and Roof Plan showing the location and the context of the sample. Likewise details for Cluster 5 are shown in Diagram 3 of Drawing Number 2 (Appendix 3: Table of drawings), starting with the top of the diagram with the Cluster Number (No. 5), colour code and description of the cluster (Cluster 5: Basic Villa – E – W Orientation), total number of plots within the cluster (No. 59), Ratio of sampling to total plots in the cluster expressed as a percentage (24.6 %), an explanation of the cluster (Smaller Unit in the ‘Undesired Orientation’), description (Basic Villa on E – W Orientation) and a Ground Floor Plan, Section and Roof Plan showing the location and the context of the sample. The location plans for the clusters are shown in Diagram 4 of Drawing Number 2 (Appendix 3: Table of drawings) and was used as a master plan for reference basis. Dependent and independent variable analysis are shown in Diagram 5 of Drawing Number 2 (Appendix 3: Table of drawings) with the main columns showing the cluster and summary details, sub-columns showing the item or calculations, units and totals, while the rows displayed primarily Cluster 1 Data, Cluster 4 Data, Cluster 5 Data and Micro-temperature Change Analysis. Consolidated batching summary sheet (Drawing 1 – Appendix 3: Table of drawings) gives all the vital information about the batching for ease of reference and with the columns showing the cluster and plot numbers, and the rows showing data grouped under summary sheet data, data attributes, data attitudes, urban built 127

form variables data, open space data, baseline climate data, data logged temperature data and micro-temperature change data.

CONSOLIDATED BATCH WORKBOOK AND EBSTATS SOFTWARE Consolidated Batch Workbook (Batch 1, Villas Data, Micro-temperature Change Analysis) of the Ebstats Software is the digital version of the analogue consolidated batching summary sheet (Drawing 1 – Appendix 3: Table of drawings). Ebstats Software is a digital statistical analytical tool for primary and secondary data analysis (Ebrahim, 2015). It is primarily composed of three types of workbooks, namely mandatory, selective and additional workbooks. There were two mandatory workbooks and these formed a vital component of the basic program as shown by Table 1.44 (Appendix 1: Standard tabulated observation sheets). The Consolidated Summary Sheet consolidated both the primary and secondary data for analysis. It was principally the digital version of the analogue consolidated batching summary sheet (Drawing 1 – Appendix 3: Table of drawings). Data analysis summary sheet is a statistical data analysis summary sheet, and is actually a summary of the results of the statistical exercise.

CONSOLIDATED SUMMARY SHEET The study measured and codified 30 plots and 16 open spaces, paths and roads during the period 8th June 2013 to 19th September 2015, and the data was loaded into digital format. Table 1.45 (Appendix 1: Standard tabulated observation sheets) shows the part information that is displayed in the consolidated summary sheet of the Ebstats Software for Plot 41, and primarily analyses data related to the independent variables related to urban built form and the dependent variable related to microtemperature change. 128

Rows provide unit of measurement for analysis, while the columns gives the field results in relation to Plot 41, measure of central tendency (mean, median and mode) and measure of dispersion (minimum, maximum and range). Plot number displays the plot number of the sample being analysed in relation to the cluster, and the response is Plot Number 41. The district of this plot is District 1, compared to the other three available. Node relates to the open grounds and does not apply to this sample. Edge relates to whether the plot is a corner, edge or centre plot, and Plot 41 is an ‘Edge Plot’. Landmark relates to schools and shops, while paths and roads relate to paths and roads in the estate, and both don’t apply to Plot 41. Building type relates to the building height of the sample and whether it is a maisonette, villa or other type. Plot 41 recorded a building height of 5.3 metres compared to measures of central tendency for the estate mean of 6.4, median of 7.7 and mode of 7.7 metres. Measures of dispersion of the variable building type for the estate of minimum values was 5.3, maximum of 7.7 and range of 2.4 metres. The variable Plot Size relates to the plot area measured in square metres and plots were classified as small, medium or large. Plot 41 had a plot area of 108 square metres compared to the estate measure of central tendency, with mean of 140, median 108 and mode 108 square metres, Estate measure of dispersion for the plot size variable recorded a minimum value of 101, maximum of 422.3 and range of 321.3 square metres. Orientation variable was the clockwise measurement in degrees from the magnetic North, and plots were either generally a North – South or East – West oriented plot. Plot 41 recorded a 46 degree compared to a measure of central tendency for the estate of mean 171.2, median 136 and mode of 136 degrees.

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Measure of dispersion of the orientation variable for the estate was minimum values of 46, maximum of 316 and range of 270 degrees. Road proximity was the distance from the main or side road, and plots were classified as being a corner internal, large near road or basic plot. Plot 41 was 85.6 metres from the main road, compared to the mean for the estate of 51.9 metres, median 51.8 and mode of 51.8 metres, and measures of dispersion of minimum road proximity of 11.4, maximum 98.2 and range of 86.8 metres. Building classification was related to the width of the row of housing, and buildings were classified as being of a detached, semi-detached or row housing nature. Ground coverage was the foot print of the house on the plot, while plot ratio gave an indication of the densification of the plot. Micro-temperature change analysis identified the month under analysis, and in the case of Plot 41 it was the month of June. Average monthly baseline climate temperature (To) for the month of June for Plot 41 was 17.3 degree Celsius with a monthly mean of 17.6, median of 17.3, mode of 17.3, minimum of 16.4, maximum of 20 and range of 3.6 degree Celsius. Temperature readings on site for Plot 41 are given by the average data logged temperature for garden of Plot (Log 2 Garden: L2) and shows that temperatures recorded were 21.2 degree Celsius, with a mean of 21, median of 22, mode of 22, minimum of 18.8, maximum of 24.7 and range of 5.9 degree Celsius. Average difference between logger 2 (L2) and the baseline climate temperature (To) gives the building micro-temperature change (TΔ) for the plot in question. The micro-temperature change for Plot 41 was 3.9 degree Celsius, mean of 3.4, median of 3, mode of 3, minimum of 1.4, maximum of 7.2 and range of 5.8 degree Celsius. Percentage of micro-temperature change was achieved by dividing the building micro-temperature change by the average monthly baseline climate temperature and the figure reported as a percentage.

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DATA ANALYSIS SUMMARY Data analysis summary sheet (D Analysis Sum) forms a mandatory workbook of the Ebstats Software, and is a statistical data analysis summary sheet. The Data analysis summary sheet is actually a summary of the results of the statistical exercise whereby it was automatically updated with every summary of the individual workbooks of the Ebstats Software. Table 1.46 (Appendix 1: Standard tabulated observation sheets) shows a part of the information displayed in the Data Analysis Summary Workbook of Ebstats Software related to data collected for building the variables, while Table 1.47 was used for the open space variables. Table 1.48 (Appendix 1: Standard tabulated observation sheets) shows part of the information displayed in the Data Analysis Summary Workbook of Ebstats Software related to Measures of Central Tendency and Dispersion for Building Variables, while Table 1.49 (Appendix 1: Standard tabulated observation sheets) is for the Open Space Variables. Generally speaking, the description of the summary of the variables reported how they were analysed through measures of central tendency and dispersion, units, the mean, the median, the mode, the minimum value, the maximum value and the range ‘ these are provided in the results of the study.

PILOT STUDY It was important to observe and maintain the validity, reliability and accuracy of the information, data collected and general methodology of the chosen research method, in order to meet the expectations and mandates of the objectives and questions study. In terms of the tools, equipment and methodology, it is recommended that there is pre-test, testing and post-testing of all three devices, at all levels and stages of the study (Mugenda & Mugenda, 2003, pp.95 – 113). The

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study used a pilot study to establish the research variables and checklists and as captured by “Observation Sheet 1”.

OBSERVATION SHEET 1 Observations of primary and secondary data was recorded in observation sheets, and the study used these sheets to record data collected in relation to batches, plots and open spaces for ease of data inventory, analysis and synthesis. Observation Sheet 1 shows the summary page for Komarock Micro-temperature Change and Built Form Study (Batch 1, Plot 41, Plot 48 and Open Space OG3) conducted during the period 8th to 15th June 2013. Table 1.50 (Appendix 1: Standard tabulated observation sheets) shows the details of Batch 1 data collection. Observation Sheet 1 can be discussed as generally, raw data from field study, micro-temperature change (dependent variable) study and simulation techniques.

Figure 3.18: Shows a photograph of Sample 1A (Plot 41). Source: Field study (2013).

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GENERALLY Five data loggers were used to capture data for this batch (two per plot and one for the open space). The plot loggers (Sample 1A) were located in the Lounge (Logger 1) of the unit and in the Garden (Logger 2) of the plot, and likewise for Logger 3 (Lounge: Sample 1B) and Logger 4 (Garden: Sample 1B) and finally Logger 5 was for the open space (Sample 1C).

RAW DATA FROM FIELD STUDY Documents and equipment coming in from the field included the data loggers and photographs in digital format. Photographs included, Sample 1A of Plot 41 (Figure 3.18), Sample 1B of Plot 48 (Figure 3.19) and Sample 1C of Open Ground OG3 (Figure 3.20).

Figure 3.19: Shows Figure photograph of Sample 1B (Plot 48). Source: Field study (2013). 133

Figure 3.20: Shows photograph of Sample 1C (Open Ground OG3). Source: Field study (2013).

MICRO-TEMPERATURE CHANGE (DEPENDENT VARIABLE) STUDY Information from a spreadsheet automatically works out another spreadsheet (Batch 1, Villa Data, Micro-temperature Change Analysis), which becomes ready for analysis as Observations for Batch 1. The temperature Template of the Ebenergy Software (Ebrahim, 2010) and the meteorological data for Jomo Kenyatta International Airport (JKIA) Nairobi were used to develop Baseline Temperature and for establishing the Design Outdoor Air Temperature (To). Figure 3.21 shows plotted baseline data as outside temperature (To) for the month of June using Temperature Template of Ebenergy Software, while Figure 3.22 shows plotted temperature figures compared to time for Batch 1 (June 2013).

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Temp: Deg.C

Time: Hours

Figure 3.21: Shows plotted baseline data as outside temperature (To) for the month of June using Temperature Template of Ebenergy Software. Source: Author (2013).

Note that in Figure 3.22, Series 1 are for the average logger 1 data located in the lounge of Plot 41 (Ave Log1 (L1) 41 Lounge), Series 2 are for the average logger 2 data located in the garden of Plot 41 (Ave Log2 (L2) 41 Garden), Series 3 for the average logger 3 data located in the lounge of Plot 48 (Ave Log3 (L3) 48 Lounge), Series 4 for the average logger 4 data located in the garden of Plot 48 (Ave Log4 (L4) 48 Garden), Series 5 for the average logger 5 data located in the open space OG3 (Ave Log5 (L5) Green Space OG3) and Series 6 the baseline temperature (To - June) for the month of June 2013 when the data was being collected. Observe also that the established pattern of logged compared to simulated figures. Table 1.51 (Appendix 1: Standard tabulated observation sheets) show the tabulated mean data logger and simulated temperature values compared to time for Batch 1 (June 2013), whereby the time shown was relative to mean temperatures logged for the lounge of Plot 41, for the garden of Plot 41, for the lounge of Plot 48, 135

for the garden of Plot 48, for the open space of open space OG3 and the baseline temperature (To) for the month of June. Observe that the established pattern of logged compared to simulated figures.

Temp: Deg.C

Plot 41 Internal Temp Plot 41 External Temp Plot 48 Internal Temp Plot 48 External Temp Open Space Temp Baseline Temp

Time: Hours

Figure 3.22: Shows plotted temperature figures compared to time for Batch 1 (June 2013). Source: Author (2013).

Table 1.52 (Appendix 1: Standard tabulated observation sheets) shows the computed temperature variances compared to time for Batch 1 June 2013, whereby the time shown was relative to the average temperature of logger 5 minus the baseline temperature, the average temperature of logger 2 minus the average temperature of logger 5, the average temperature of logger 4 minus the average temperature for logger 5, the average temperature of logger 2 minus the average temperature for logger 4, the average temperature of logger 2 minus the baseline temperature and the average temperature of logger 4 minus the baseline temperature. 136

SIMULATION TECHNIQUES Retrofitting techniques are possible when dealing with an existing building by inputting the primary data collected on site, secondary data from drawings and from specifications of the building being analyzed. Built form data was inputted into the Query Workbook of the Ebenergy Software, meteorological data for the hottest month of the year (March) was assessed using the Temperature Template of the Ebenergy Software and the observed temperature inputted in the Data Loggers Workbook of the Ebenergy Software. Issues pertaining to the occupancy pattern, variance and daily or annual temperature peculiarities were controlled through a scoring system within the software and formed another level of intervention. Data relating to incidental gains by humans, animals and machinery or equipment which generate heat or coolant, were obtained from the users on site, international standards and factor of safety figures available through the digital repositories. In the case of the pilot study, the roof was considered to be the largest surface with the greatest solar exposure, and as such, was used for modeling or simulating the heating or cooling thermal requirements. The principal planes of the building or space were on a south to east facing of 46 degree orientation. Table 1.53 (Appendix 1: Standard tabulated observation sheets) shows the inputted data as the data logged and simulated stages in the Query Workbook of the Ebenergy Software and a comfort score generated. Displayed are the symbols of the data being inputted, the description of the Query Workbook data, the unit, the existing building value and the retrofit design value. A reduction in transmittance value from 8.5 to 1.3 Watts per metre squared degree Celsius (W/m2oC) (See: Transmittance of roof (Ur) by adding a new ceiling with insulation, reflective foil and ventilating it, would achieve a 892.9 Kilowatt Hours (KWh) or 75 percent reduction in air-conditioning or mechanical ventilation 137

energy load for a single element. Gauged against the cost of the additional building element, the designer can evaluate the cost or benefit of the proposal. Other measures were expected to achieve between 25 to 40 percent reduction in energy use. Relative humidity is controlled structurally and requires further investigations and analysis in future research. Information from the Observation Sheet was inputted into the Consolidated Batching Summary Sheet (Drawing 1 – Appendix 3: Table of drawings) ready for data analysis.

3.12 DATA ANALYSIS AND METHODS This section describes how the data collected and methods used were analyzed;, the analysis was linked to the objectives of the study under headings of data analysis techniques used in the study, graphic representation of data used in the study, use of digital statistical analytical tools in the study, data tests and analysis conducted in the study, and summarized analytical framework.

3.13 DATA ANALYSIS TECHNIQUES USED IN THE STUDY Data analysis and methods of the study commenced with tabulating and displaying the processed data related to building variables (Table 1.54 – Appendix 1: Standard tabulated observation sheets) and open space variables (Table 1.55 Appendix 1: Standard tabulated observation sheets). The data displayed for the building variables were building orientation X1 in degrees, building classification X2 in metres, road proximity X3 in metres, building type X4 in metres, plot size X5 in square metres, ground cover X6 as a percentage, plot ratio X7 as a percentage and micro-temperature change Y in degree Celsius. The data displayed for open space variables were open space orientation X8 in degrees, open space road proximity X9 in metres, open space light angle X10 in degrees, open space shading coefficient X11 as a percentage, open space length X12

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in metres, open space area X13 in square metres, open space hard landscape coefficient X14 as a percentage and micro-temperature change Y in degree Celsius. The data analysis and methods of the study proceeded with listing the data needs and analysis techniques, making a link between techniques of data analysis and the types of data needed (Table 1.56 - Appendix 1: Standard tabulated observation sheets). The dependent variable was micro-temperature change (Y) in degree Celsius (oC). Independent variables and surrogates related to the urban built form were building type (X4: M), plot size (X5: M2) and open space size (X13: M2), building orientation (X1: Degree) and open space orientation (X8: Degree), building road proximity (X3: M) and open space road proximity (X9: M), building classification (X2: M), ground coverage (X6: %), plot ratio (X7: %), hard landscape coefficient (X14: %), light angle (X10: Degree), shading coefficient (X11: %) and open space length (X12: M). Data collected was analysed at various levels and tested with techniques for data analysis including: summary statistics, Pearson correlation matrix, normality test (Shapiro-Wilk W test for normal data), multicollinearity test (variance inflation factors:

VIF),

heteroscedasticity

(Breusch-Pagan/Cook-Weisberg

test

for

heteroskedasticity test results), multiple regression results, hypothesis testing and forecasting.

3.14 GRAPHIC REPRESENTATION OF DATA USED IN THE STUDY It is not easy to understand the nature of a particular climate by merely looking at the vast amount of data published in the records of the nearest meteorological station and other data collected in a study. It was necessary to sort, summarize and simplify the collected data with reference to the objectives and requirements of the study. This was best accomplished through the adoption of a standardized method of graphic presentation (Koenigsberger et al., 1973, p.18).

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The study embraced the knowledge and skills of analysis, data interpretation, based on graphic representation of data in order to draw results, interpretation and synthesis of findings, conclusions and recommendations. Organization refers to the process of rearranging, reducing and summarizing the data so that it can be easily understood and utilized. It was necessary to organize the data before attempting to analyze it. The study used methods that were visually easy to interpret (Kabiru and Njenga, 2009, pp.135 – 6). Table 1.57 (Appendix 1: Standard tabulated observation sheets) shows a list of the purpose of information and graphical presentation options for the study. Graphic representation of data in the study included photograph, frequency distribution tables, bar chart, scattergram, frequency polygon, hyperspace diagrams, polar curves, nomogram, isotherm distribution map and diagrammatic summary table.

PHOTOGRAPH Photographs were a graphic presentation option in the study, with the purpose of capturing information that described an entire object or situation. Figure 3.23 shows the cooling effect of landscape on an open space, and is an example of the ability of a single photograph to capture observations of the built form variables in a natural way.

FREQUENCY DISTRIBUTION TABLE Frequency distribution table were used as graphic presentation options in the study, their purpose was to present the exact values, raw data or data that did not fit into any single pattern. Frequency distribution as numbers or observed values represented the measurements of each variable in a data set. Frequency distribution enabled a quick visual appreciation of the key characteristics of a data set. 140

Frequency distribution tables as the first output of the analysis of quantitative data, showed the response totals for each possible observation of the survey tool. Univariate statistics for each variable of the study included measures of dispersion (standard deviation and range) and measures of central tendency (mean, median and mode) as the output (Mugenda & Mugenda, 2012, p.128). An example of a frequency distribution table is Table 1.54 (Appendix 1: Standard tabulated observation sheets) shows part of the information displayed in the Consolidated Summary Sheet of Ebstats Software related to cross-sectional data of building variables and display of collected data.

Figure 3.23: Shows a photograph of an example of the cooling effect of landscape on the open spaces. Source: Field survey (2015).

BAR GRAPH Bar graph as a graphic presentation option of the study had the purpose of dramatizing differences, drawing comparisons and describing proportions. Bar graphs were graphic means of showing the distribution of categorical data and consisted f bars with lengths proportional to the frequencies in each category. 141

Conventionally, the bars were not joined but rather separated by a space because the variable was categorical rather than continuous in nature (Mugenda & Mugenda, 2012, p.28). Bar graphs were used in the study in the analysis of the measures of central tendencies (mean) and spread (range and standard deviation). Figure 3.24 is an example of a bar graph showing the inter-plot orientation variability over the study area for the plots sampled, whereby the mean was 171.2 degrees, standard deviation of 104.3 and range of 270 degrees.

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Figure 3.24: Bar graph showing inter-plot orientation variability over study area for the plots sampled. Source: Field survey (2015).

SCATTERGRAM Scattergram as a graphic presentation option was used in the study to represent, in two dimensions, the relationship between two variables. Scattergram, also called scatter diagram or scatterplot are a common way to graphically present relationships for any type of data. For each case, a dot is placed in the graph space 142

at the intersection of each pair of values of the variables placed on the X and Y axis (Montello & Sutton, 2013, p.239). Scattegram were used in the study for linear regression analysis defined by the intercept or constant alpha (α or a), the slope of the regression line by coefficient beta (β or b) and the standard error (u). Note that the graph of the regression line does not allow one to make quantitative statements about the relationship between the variables. One would need to know the exact values of the slope and the intercept. A regression line is little else than to fitting a line through the observations in the scattergram according to some principle (Kiel, 2015, p.13). Figure 3.25 is an example of a Scattergram with linear projection for the building orientation variable, whereby the constant was 1.208963 (standard error of 3.663526 degree Celsius), coefficient was 0.0033003 with standard error of 0.0031414. The coefficient being positive implied an ascending line as shown in the

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Figure 3.25: Scattergram with linear projection for building orientation variable. Source: Field survey (2015). 143

FREQUENCY POLYGON Frequency polygon was a graphic option used by the study to summarize trends, show interactions between two or more variables, relate data to constants, or emphasize an overall pattern rather than specific measurements. Frequency polygons were used in the study in the analysis of measures of skewness and kurtosis, in relation to the measures of central tendencies (mean, median and mode). For a normally distributed variable, Skewness (S) is equal to zero, while Kurtosis (K) is equal to three (Gujarati, 2012, p.128). Figure 3.26 is an example of a frequency polygon using Kernel density estimate with normal density projection for building orientation variable, with values of mean 171.2 degrees, median 136 degrees, skewness 0.1333 and kurtosis 1.61268.

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Figure 3.26: Frequency polygon using Kernel density estimate with normal density projection for building orientation variable. Source: Field survey (2015).

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HYPERSPACE DIAGRAM Hyperspace diagram was a graphic option used in the study to represent in three dimensions the relationship between three or more variables. A hyperspace diagram is a diagram representing grouped numerical data in which frequency is plotted against two or more independent variables. An independent variable is plotted on the X-axis and another on the Z-axis. Frequencies for each class interval were plotted along the Y-axis. In the study, hyperspace distributions were used in comparing different distributions when such distributions are drawn on the same graph using multivariate analysis (King’oriah, 2016, p.566). Figure 3.27 is an example of a hyperspace diagram using field work data for building orientation, building classification and micro-temperature change variables.

POLAR CURVE Polar curve was a graphic option used in the study to show the variable distribution pattern in concentric circles. Polar curves use polar coordinate axis (Koenigsberger et al., 1973, p.150, p.182 and p.231), or circular graph dimensions consisting of a radius axis of varying length and angle, appropriate for cyclic data, such as directions in space or measurements in repeating time periods (Montello & Sutton, 2013, p.239). Figure 3.28 is an example of a polar curve showing scatterplots of micro-temperature change and building orientation field data and Figure 3.29 of a polar curve showing scattergram of micro-temperature change and open space orientation field data.

NOMOGRAM Nomogram was a graphic option used in the study to show three or more variables in interrelated scales and trends. A nomogram is a combination of different scales displaying variables which are interlinked through relationship. 145

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One draws a line between the two scales and reads the result on a third one (Koenigsberger et al., 1973, p.156, p.161, p.167 and p.187). The study used two types of nomogram to explore multivariate analysis. Predictive nomogram was used as a nominal scale while remedial nomogram was used as a qualified scale. Figure 3.30 is an example of predictive (nominal scale) nomogram for building variables, while Figure 3.31 was a remedial (qualified scale) nomogram for building variables showing average micro-temperature change for structured neighbourhoods in tropical upland climates.

ISOTHERM DISTRIBUTION MAP Isotherm distribution map was a graphic option used by the study to show location and distribution of dependent variable frequencies. Isotherm distribution maps are a form of geospatial maps and use geospatial analysis to analyze and display the collected data. Geospatial data analysis explicitly takes account of the spatiality in geography and environmental data variously called spatial statistics or geo-statistics (Montello & Sutton, 2013, p.212). Isotherm distribution maps were used in the study to analyze the road proximity (building and open space) variable in relation to the micro-temperature change dependent variable. Figure 3.32 is an example of an isotherm distribution map.

DIAGRAMMATIC SUMMARY TABLE Diagrammatic summary table was a graphic option used by the study to summarize and display in diagrammatic terms trends, show interaction between two or more variables, relate data to constants, or emphasize an overall pattern and related to study results. Figure 3.33 is an example of a diagrammatic summary table as building variable part summary sheet.

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3.15 USE OF DIGITAL STATISTICAL ANALYTICAL TOOLS IN THE STUDY Problems which could not be solved earlier due to the sheer amount of computations involved were tackled accurately and rapidly with the aid of computers. For the study, the use of the computer to analyze complex data made complicated research designs practical. Computers are ideally suited for data analysis concerning large research projects. Researchers are essentially concerned with faster retrieval of the huge amount of data stored, and computers have only made the processing of data and analysis of techniques easier. Computer use, apart from expediting the research work, has reduced human drudgery and added to the quality of research activity. Computers can perform many statistical calculations easily and quickly. Computation of means, standard deviations, correlation coefficients, t tests, analysis of variance, analysis of covariance, multiple regression, factor analysis and various nonparametric analysis are just a few of the statistical outputs (Kothari, 2006, p.361 – 374). Table 1.58 (Appendix 1: Standard tabulated observation sheets) shows a list of digital statistical tools, statistical data needs and analysis techniques for the study. Digital statistical analytical tools used in the study were Microsoft Excel, Ebstats Software and Stata Software.

MICROSOFT EXCEL Microsoft Excel was a digital statistical analytical tool option used in the study for data that required tabulation, to organize data, fitting equations to data, interpolating between data points, solving single and multiple equations, finding optimum solutions, plot graphs, charts and diagrams, cut and paste data from spreadsheet to other software and platform for running Ebstats Software. Excel developed by the Microsoft Corporation is the world’s most widely used spreadsheet program. 152

Excel has many applications, however in the study it was used to determine the roots of algebraic equations, fitting curves through data sets, analyzing data statistically, carrying out studies in micro-temperature and urban built form analysis, and solving complicated optimization problems. Excel was also used to solve other types of technical problems, such as the evaluation of integrals and the solution of interpolation problems, even though it lacks special features that automate these tasks. Excel was especially well-suited for displaying data in various graphical formats (Gottfried, 2002, pp.148 – 172). The study used Microsoft Excel 2010 Version for the work.

EBSTATS SOFTWARE Ebstats Software was a digital statistical analytical tool option used in the study, where different data types needed to be consolidated in a summary sheet for cross-sectional data analysis, and for data analysis summary. Ebstats Software (See: Processing of raw information from data loggers) gives details on issues related to use of the ebstats software, consolidated summary sheet, collected data analysis, data analysis summary (Ebrahim, 2015). Ebstats Software commenced with inputting data from the micro-temperature change and urban built form variables, processed data from the 30 plots and 16 open spaces into the consolidated summary sheet, and carried out multivariate analysis and generation of a hyperspace diagram.

STATA SOFTWARE Stata Software was a digital statistical analytical tool option used in the study when various types of data needed to be summarized as statistics. Stata is a statistical and econometric software package developed by Stata Corp in College Station, Texas (USA) (Kiel, 2015). The study tested findings using: the Pearson correlation matrix, normality test (Shapiro-Wilk W test for normal data), multicollinearity test (variance inflation factors: VIF), heteroscedasticity (Breusch153

Pagan/Cook-Weisberg test for heteroskedasticity test results), multiple regression results and hypothesis testing. The Stata software therefore was a handy tool for statistical inference related to estimation, hypothesis testing and forecasting. The study used Stata version 14, running on Windows platform. Econometrics or economic measurements as part of finding the set of assumptions that were both sufficiently specific and sufficiently realistic to allow the study to take the best possible advantage of the data available (Gujarati, 2004, p.2).

3.16 DATA TESTS AND ANALYSIS CONDUCTED IN THE STUDY Data analysis procedures incorporated in the study for building and open space variables included: summary results, summary statistics, Pearson correlation matrix, normality test using Shapiro-Wilk W test for normal data, multicollinearity test using variance inflation factors (VIF), heteroscedasticity using BreuschPagan/Cook-Weisberg test for heteroskedasticity test results, multiple regression results and hypothesis testing.

3.17 SUMMARIZED ANALYTICAL FRAMEWORK There were many advantages in preparing an analytical framework to guide data analysis and drawing of results for the study. The framework ensured that the e analysis was not only well thought-out but also that it would answer the research questions. The study selected appropriate techniques of analysis o suit the data collected and purpose of analysis (Ngau & Kumssa Ed., 2004, p.203). Table 1.59 (Appendix 1: Standard tabulated observation sheets) shows summarized analytical framework for the study.

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3.18 REFLECTION ON THE RESEARCH METHODS As part of the research design and methodological framework, an operational model was developed of research methods that would facilitate the study of the relationship between micro-temperature change and urban built form. Research design studies were undertaken on field measurements, longitudinal measurements and studies, parametric modelling and studies, baseline cases, and experimental construction and building. Longitudinal research design was used in the study to measure and collect data for the independent urban built form and dependent micro-temperature change variables. Primary and secondary data sources were identified in light of the data to be collected. Observation method was used to identify research tools: observation book and sheets, batching journal, data logger journal, checklists and tabulations. Sampling design identified the sample population (240 plots), unit of analysis (plot and open spaces), plot attributes (districts, nodes, edges, landmarks, paths and axis), planning and design attitudes (building types, plot size, orientation, road proximity and building classification), sampling technique, batching design and cluster sampling. The study described the research methods employed in relation to collection, processing and preparation of data, data analysis and methods, data analysis techniques used in the study, graphic representation of data used in the study, use of digital statistical analytical tools in the study, data tests and analysis conducted in the study and a summarized analytical framework.

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CHAPTER FOUR RESULTS This chapter presents the results and analysis of the data collected in relation to micro-temperature change and urban built form. In attempting to address the first research objective which sought to identify the urban built form variables which have an influence on the temperature change, the results of the sampling the plot attributes, planning and design attitudes, and subjects were analyzed against a sampling and cluster bias. The processed data and summary statistics for building and open space variables is presented and analyzed under the theme of urban built form and impact on micro-temperature change. To address the second research objective which sought to determine the significant urban built form variables which contribute to the temperature change, the results of using the Pearson correlation matrix for building and open space variable and statistically inferring which urban built form causes micro-temperature change was undertaken using the Normality test, the Shapiro-Wilk W test for normal data for determining the significant building and open space variables. The processed data and summary statistics for significant urban built form variables which contribute to the temperature change is presented and analyzed under the theme of the prevalence of urban built form factors. As part of statistically inferring which urban built form factors provoke micro-temperature change, the study employed the Multicollinearity test using variance inflation factors (VIF) for building and open space variables, further the Heteroscedasticity test was carried out using Breusch-Pagan/Cook-Weisberg test in order to test results for building and open space variables. In terms of determining the types and prevalence of urban built form intervention on micro-temperature change, the study employed multiple regression results and hypothesis testing. In order to determine the prompts and barriers that affect sustainable urban built form in a temperature changing environment, the study employed isotherm distribution maps, inter-plot orientation and building classification variability results. 156

In order to address the third research objective which was to develop design and planning strategies in view of sustainable urban built form in a temperature changing environment, the study employed the predictive and remedial nomogram, reflecting on the results of significant urban built form variables which contribute to the temperature change for building and open space.

4.1 SAMPLING PLOT ATTRIBUTES Data analysis using sampling plot attributes, planning and design attitudes was used to further analyze data against sampling bias or errors. Sampling error is the degree to which the results from the sample deviate from those that would be obtained from the entire population, because of random error in the selection process. Results of the sampling of Districts (Figure 4.1) reflects the three districts within the Komarock Infill B Estate, with District One having 78 plots, District Two with 93 plots and District Three with 69 plots, representing 32.5, 38.8 and 28.7 percent, respectively. Results of sampling the Nodes (Figure 4.2) reflect the distribution of nodes on the site. The study identified fourteen nodes and one open ground (Corner Plot) along road stretches, intersections and culminations. Results of sampling the Edges (Figure 4.3) reflect the distribution of plots primarily as edge plots or corner plots, one corner plot, which was undeveloped had been designated for a school in the future. Edge plots were 185 in number while centre plots were 55, which translated into 77.1 and 22.9 percent, respectively. Results of sampling the Landmarks (Figure 4.4) shows the distribution of landmarks in the study site, these, which were primarily schools and shops, accounted for seven plots for landmarks, with 233 being other plots, or 2.9 and 97.1 percent, respectively.

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Results of sampling the Paths and Axis (Figure 4.5) was the application of the sampling concept of plot attributes on the study site and indicated the layout of paths (pedestrian circulation) and roads (vehicular circulation) for the site and a summary table, and translates into fifteen units for the former and twelve units for the latter, or 55.6 and 44.4 percent, respectively.

4.2 SAMPLING PLANNING AND DESIGN ATTITUDES Results of sampling the Building Types (Figure 4.6) shows the distribution of different building types within the study site. The study identified three building types: Maisonette, Villa and other plot usage with 74, 159 and 7 plots, representing 31.8, 66.3 and 2.9 percent, respectively. Results of sampling the Plot Size (Figure 4.7) shows the distribution of plots based on plot size within the study site. The study identified four plot criteria based on size of plot: change of user plot (7 number), small sized plot with less than 99 square metres area (5 number), medium sized plot of between 99 to 149 square metres area (65 number) and large sized plot of over 150 square metres area (163 number), representing 2.9, 2.1, 27.1 and 67.9 percent, respectively. Results of sampling the Orientation (Figure 4.8) shows the distribution of plots based on the orientation criteria within the study site. The study identified three plot orientations: North to South (N-S) orientation (98 plots), East to West (EW) orientation (135 plots) and change of user plots (7 plots), representing 40.8, 56.3 and 2.9 percent, respectively. Results of sampling the Road Proximity (Figure 4.9) shows the distribution of plots based on the road proximity criteria. The study identified three plot configurations and how they related primarily with the main road: corner plots were internally located (9 number), large plot located near the main road (58 number) and basic plot (173 number), representing 3.8, 24.2 and 72 percent, respectively. Results of sampling the Building Classification (Figure 4.10) shows the distribution of plots based on the building classification criteria. 163

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The study identified four classes of residential buildings: change of user plots (7 number), detached house plot (22 number), semi-detached house plot (17 number) and row house plot (194 number), representing 2.9, 9.2, 7.1 and 80.8 percent, respectively.

4.3 SUBJECTS Data analysis under subjects was used to analyze data against sampling and cluster bias or errors. Cluster bias is a form of selection bias resulting when a cluster sampling design selects respondents who are too closely related to one another within a cluster, so that they tend to give similar responses.

Maisonette N/S Maisonette E/W Villa N/S Villa E/W Corner Internal Plot Large Plot near Road Transformed Plot Change of User Plot

Figure 4.12: Shows a pie chart of a proportion of total number of plots in cluster to total number of plots in Komarock Infill B Estate. Source: Field survey (2015). Cluster sampling distribution map (Figure 4.11) shows the distribution of plot and open space sampling based on the data logger journal entries and statistical data analysis summary for clusters, while Table 2.1 (Appendix 2: Tabulated data) shows cluster description, plot and open space numbers. Out of the sample population of 240 plots available in Komarock Infill B Estate, 30 plots were 170

randomly sampled from the 8 clusters available and which represents 12.5 percent of the sample. Table 2.2 (Appendix 2: Tabulated data) shows the sampling technique used to sample building clusters, while Figure 4.12 shows the proportion of total number of plots in a cluster to the total number of plots in Komarock Infill B Estate. The sample was: 6 percent Basic Maisonette North South Orientation (Cluster 2: Sector 1), 13 percent Basic Maisonette East West Orientation (Cluster 3: Sector 2), 25 percent Basic Villa North South Orientation (Cluster 4: Sector 3), 25 percent Basic Villa East West Orientation (Cluster 5: Sector 4), 4 percent Corner internal plot (Cluster 6: Sector 5), 23 percent Large plot near main road (Cluster 7: Sector 6), 1 percent Transformed plot (Cluster 8: Sector 7) and 3 percent Change of user plot (Cluster 10: Sector 8).

Proportionate sample shortfall or excess: No.

Figure 4.13: Shows a column diagram (Series 1) of the shortfall or excess of the proportionate sample plots to the total number of plots in the cluster. Source: Field survey (2015).

Figure 4.13 shows a column diagram (Series 1) of the shortfall or excess of the proportionate sample plots to the total number of plots in the cluster. The 171

shortfall or excess was Basic Maisonette North South Orientation (Cluster 2: Column 1), Basic Maisonette East West Orientation (Cluster 3: Column 2), Basic Villa North South Orientation (Cluster 4: Column 3), Basic Villa East West Orientation (Cluster 5: Column 4), Corner internal plot (Cluster 6: Column 5), Large plot near main road (Cluster 7: Column 6), Transformed plot (Cluster 8: Column 7), Change of user plot (Cluster 10: Column 8) and the total (Column 9). Out of the sample population of 41 open grounds, roads or paths available in Komarock Infill B Estate, 15 open spaces were randomly sampled from the 3 clusters available representing 36.6 percent of the sample. Table 2.3 (Appendix 2: Tabulated data) shows the sampling technique used to sample open space clusters.

Open Ground Roads Paths

Figure 4.14: Shows a pie chart of proportion of total number of open spaces in cluster to total number of open spaces in Komarock Infill B Estate. Source: Field survey (2015).

Figure 4.14 shows the proportion of total number of open spaces in cluster to the total number of open spaces in Komarock Infill B Estate. The sample was 34 percent open ground (Cluster 1: Sector 1), 37 percent vehicular circulation (Road Cluster 11: Sector 2) and 29 percent pedestrian circulation (Path Cluster 12: Sector 3). 172

Figure 4.15 shows a column diagram (Series 1) of the shortfall or excess of the proportionate sample open spaces to the total number of open space in the cluster. The shortfall or excess was open ground (Cluster 1: Column 1), vehicular circulation (Road Cluster 11: Column 2), pedestrian circulation (Path Cluster 12: Column 3) and total (Column 4).

Proportionate sample shortfall or excess: No.

Figure 4.15: Shows a column diagram (Series 1) of the shortfall or excess of the proportionate sample open spaces to the total number of open spaces in the cluster. Source: Field survey (2015).

4.4 URBAN BUILT FORM AND IMPACT ON MICRO-TEMPERATURE CHANGE Queries were made on the observations related to the urban built form and the impact on the micro-temperature change during the observation period 8th June 2013 to 19th September 2015. Mean number of observations of building orientation was 171.2 degrees (SD = 104.3, range = 270), width of row of buildings was on average 40.1 metres (SD = 35.6, range = 147), building road proximity was 51.9 metres (SD = 25.6, range = 87.2), height of buildings was 6.4 metres (SD = 1.1, range = 2.4), plot size was 140 square metres (SD = 62, range = 321.3), ground coverage was 47.6 percent (SD = 16, range = 63), plot ratio was 65.9 percent (SD = 29.6, range = 133) and micro-temperature change was 3.4 degree Celsius (SD = 1.4, 173

range = 5.8). Table 2.4 (Appendix 2: Tabulated data) shows the tabulated collected data, while Table 2.5 shows tabulated summary statistics for building variables. Mean number of observations of open space orientation was 158.5 degrees (SD = 106.5, range = 270), open space road proximity was 60.7 metres (SD = 25.9, range = 72.2), open space light angle was 80.6 degrees (SD = 10.6, range = 35), open space shading coefficient was 47.6 percent (SD = 23.4, range = 86.6), open space length was 39.9 metres (SD = 12.8, range = 49.9), open space area was 759.4 square metres (SD = 525.9, range = 1721), open space hard landscape was 62.7 percent (SD = 18.8, range = 60) and micro-temperature change was 3.7 degree Celsius (SD = 1.53, range = 5.9). Table 2.13 (Appendix 2: Tabulated data) shows tabulated collected data, while Table 2.14 shows tabulated summary statistics for open space variables.

KERNEL DENSITY ESTIMATE For data analysis of frequency polygon, the study employed Kernel density estimate with normal density projection to summarize trends, show interactions between two or more variables, relate data to constants or emphasize an overall pattern rather than specific measurements. Kernel density estimate is a fundamental data smoothing problem where inferences about the population are made based on a finite data sample. Skewness is the extent to which a distribution of data departs from the normal distribution along the horizontal axis (normal = 0), while kurtosis is the measure of the peakedness of a distribution of data for a continuous random variable (normal = 3). Building orientation had a summarized trend of mean value 171 degrees, median 136 degrees, skewness 0.13 and kurtosis 1.61. Both the skewness and kurtosis were approaching a normal distribution of data with the mean providing a good measure of central tendency. The observation being that micro-temperature change seems to be displaying a normal pattern with the building orientation.

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Figure 4.16: Frequency polygon using Kernel density estimate with normal density projection for building classification variable. Source: Field survey (2015).

Building classification had a summarized trend of mean value 40 metres, median 36 metres, skewness 2.1 and kurtosis 7.39. Both skewness and kurtosis are abnormal (Figure 4.16). Kurtosis seems to be more pronounced than skewness, with the median prevailing over the mean as the central tendency for the distribution. The observation was that micro-temperature change seems to be displaying an abnormal pattern with the building classification. Building road proximity had a summarized trend of mean value 52 metres, median 46 metres, skewness 0.21 and kurtosis 1.76. The curve approached the normal distributed curve with the mean being the prominent statistic (Figure 4.17). The observation was that micro-temperature change seemed to be displaying a normal pattern with the building road proximity.

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Building type had summarized trend of mean value 6.4 metres, median 6.2 metres, skewness 0.14 and kurtosis 1.14. Even though the skewness approached zero, the curve distribution was indecisive (Figure 4.18). The mean would however prevail over the median as a statistic. The observation here was that microtemperature change seemed to be displaying an indecisive pattern with the building type being dominated by Villas of 5.3 metres and Maisonettes of 7.7 metres height.

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Figure 4.17: Frequency polygon using Kernel density estimate with normal density projection for building road proximity variable. Source: Field survey (2015).

Plot size had a summarized trend of mean value 140 square metres, median 108 square metres, skewness 3.4 and kurtosis 15.65. Both skewness and kurtosis are abnormal (Figure 4.19). Kurtosis seems to be more pronounced than skewness and thus, the median would prevail over the mean and the choice of distribution. The observation here was that micro-temperature change seemed to be displaying an abnormal pattern with the plot size.

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Figure 4.18: Frequency polygon using Kernel density estimate with normal density projection for building type variable. Source: Field survey (2015).

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Figure 4.19: Frequency polygon using Kernel density estimate with normal density projection for plot size variable. Source: Field survey (2015).

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Ground coverage had a summarized trend of mean value 48 percent, median 44 percent, skewness 0.83 and kurtosis 3.08. The curve is approaching a normal distributed curve with the mean being the prominent statistic (Figure 4.20). The observation here was that micro-temperature change seemed to be displaying a normal pattern with the ground coverage. Plot ratio had a summarized trend of mean value 66 percent, median 63 percent, skewness 1.60 and kurtosis 5.85. Both skewness and kurtosis are abnormal (Figure 4.21). Kurtosis seems to be more pronounced than skewness and thus, the median would prevail over the mean and the choice of distribution. The observation was that micro-temperature change seemed to be displaying an abnormal pattern with the plot ratio.

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Figure 4.20: Frequency polygon using Kernel density estimate with normal density projection for ground coverage variable. Source: Field survey (2015).

Micro-temperature change had a summarized trend of mean value 3.4 degree Celsius, median 3.3 degree Celsius, skewness 0.95 and kurtosis 3.89. The

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curve is approached a normal distributed curve with a peak at the mean statistic (Figure 4.22).

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Figure 4.21: Frequency polygon using Kernel density estimate with normal density projection for plot ratio variable. Source: Field survey (2015).

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Figure 4.22: Frequency polygon using Kernel density estimate with normal density projection for micro-temperature variable. Source: Field survey (2015). 179

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Figure 4.23: Frequency polygon using Kernel density estimate with normal density projection for open space orientation variable. Source: Field survey (2015).

Open space orientation had a summarized trend of mean value 159 degrees, median 136 degrees, skewness 0.25 and kurtosis 1.6. The curve is approached a normal distributed curve with the mean being the prominent statistic (Figure 4.23). The observation was that micro-temperature change seemed to be displaying a normal pattern with the open space orientation. Open space road proximity had a summarized trend of mean value 60.7 metres, median 62.5 metres, skewness 0.01 and kurtosis 1.43. The curve was approaching a normal distributed curve with the mean being the prominent statistic (Figure 4.24). The observation here seemed to be that micro-temperature change was displaying a normal pattern with the open space road proximity. Open space light angle had a summarized trend of mean value 81 degrees, median 84 degrees, skewness -1.76 and kurtosis 4.9. Both skewness and kurtosis are abnormal (Figure 4.25). Skewness is negative implying a right hand side skew, with kurtosis being more pronounced than skewness. Thus the median would prevail over the mean as a measure of central tendency. The observation here was that 180

micro-temperature change seemed to be displaying an abnormal pattern with the open space light angle.

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Figure 4.24: Frequency polygon using Kernel density estimate with normal density projection for open space road proximity variable. Source: Field survey (2015).

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Figure 4.25: Frequency polygon using Kernel density estimate with normal density projection for open space light angle variable. Source: Field survey (2015). 181

Open space shading coefficient had a summarized trend of mean value 48 percent, median 47 percent, skewness 0.02 and kurtosis 2.6. The curve was approaching a normal distributed curve with the mean being the prominent statistic (Figure 4.26). The observation here, being that micro-temperature change seemed to be displaying a normal pattern with the open space shading coefficient.

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Figure 4.26: Frequency polygon using Kernel density estimate with normal density projection for open space shading coefficient variable. Source: Field survey (2015).

Open space length had a summarized trend of mean 40 metres, median 43 metres, skewness 0.01 and kurtosis 3.1. The curve was approaching a normal distributed curve with the mean being the prominent statistic (Figure 4.27). The observation was that the micro-temperature change seemed to be displaying a normal pattern with the open space length. Open space area had summary statistics with values of mean 759 square metres, median 751 square metres, skewness 0.84 and kurtosis 3. The curve was approaching a normal distributed curve with the mean being the prominent statistic

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(Figure 4.28). The observation here was that micro-temperature change seemed to be displaying a normal pattern with the open space area.

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Figure 4.27: Frequency polygon using Kernel density estimate with normal density projection for open space length variable. Source: Field survey (2015).

Open space hard landscape had a summarized trend of mean value 63 percent, median 58 percent, skewness 0.81 and kurtosis 2.7. The curve was approaching a normal distributed curve with the mean being the prominent statistic (Figure 4.29). The observation here was that micro-temperature change seemed to be displaying a normal pattern with the open space hard landscape. Open space micro-temperature change had a summarized trend of mean 3.7 degree Celsius, median 3.6 degree Celsius, skewness 0.98 and kurtosis 3. The curve was approaching a normal distributed curve with the mean being the prominent statistic (Figure 4.30).

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Figure 4.28: Frequency polygon using Kernel density estimate with normal density projection for open space area variable. Source: Field survey (2015).

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Figure 4.29: Frequency polygon using Kernel density estimate with normal density projection for open space hard landscape variable. Source: Field survey (2015).

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Figure 4.30: Frequency polygon using Kernel density estimate with normal density projection for micro-temperature change variable. Source: Field survey (2015).

4.5 PREVALENCE OF URBAN BUILT FORM FACTORS The Pearson correlation matrix was used to analyze data as a measure of association and strength of linear relationship between either two building or open space variables. Correlation coefficient (r) varies between negative one to positive one (-1 ≤ r ≥ 1). A coefficient that is close to ± 1 indicates a strong relationship while coefficients close to zero imply little or no association. A negative correlation coefficient implies that the variables are inversely related, whereas a positive correlation coefficient implies a direct relationship between variables. Table 2.6 (Appendix 2: Tabulated data) shows Pearson correlation matrix for building variables at the significance level of 1 percent (0.01). The strongest correlation relation was between plot ratio and ground coverage of 0.7252 at the significance level of 1 percent (0.01).

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Table 2.15 (Appendix 2: Tabulated data) shows Pearson correlation matrix of open space variables at the significance level of 1 percent (0.01). The strongest correlation relation was between open space light angle and open space area of 0.4902 at the significance level of 1 percent (0.01).

4.6 STATISTICAL INFERRED URBAN BUILT FORM CAUSES OF MICRO-TEMPERATURE CHANGE Data analysis using Shapiro-Wilk W (z) test was used to analyze data based on the test of the null hypothesis, a sample was been drawn from a population having a normal distribution in order to satisfy the condition of use of most linear modeling procedures such as regression and Analysis of Variance (ANOVA). The study was interested in analyzing the P (Prob) values, which should be greater than the significance level of 0.01. Table 2.7 (Appendix 2: Tabulated data) shows normality test using ShapiroWilk W test for normal data for building variables. Building variables with P values of greater than the significance level of 0.01, had curves approaching the normal distribution (building orientation, road proximity, building type, ground coverage and micro-temperature change), while those building variables with less than the significance level of 0.01 had skewed curve distribution (building classification, plot size and plot ratio). Table 2.16 (Appendix 2: Tabulated data) shows normality test using Shapiro-Wilk W test for normal data for open space variables. Open space variables with P values of greater than the significance level of 0.01, had curves approaching the normal distribution (open space orientation, open space road proximity, open space shading coefficient, open space length, open space area and microtemperature change), while the only open space variable with less than the significance level of 0.01 had skewed curve distribution (light angle).

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4.7

STATISTICAL

INFERRED

URBAN

BUILT

FORM

FACTORS

PROVOKING MICRO-TEMPERATURE CHANGE Variance inflation factors (VIF) was used to analyze data based on the test for multicollinearity as a measure of the degree to which the variance of the ordinary least square (OLS) estimator is inflated because of collinearity or as a condition that there is no exact linear relationship among the regressors. A variable has a multicollinearity problem if variance inflation factor value is above 10 (tolerance of above 0.1). Results should be smaller than 0.1 tolerance values with a significance level of 0.01. There was no multicollinearity with values of variance inflation factor of below 10 (tolerance of below 0.1). As long as collinearity was not perfect (i.e. equal to 1) as was the case with the building or open space variables, it was often suggested that the best remedy was to do nothing but simply present the results of the fitted model. Table 2.8 (Appendix 2: Tabulated data) shows multicollinearity test using variance inflation factors (VIF) for building variables. There was a high degree of collinearity amongst several variables, even the mean variance inflation factor was in excess of 2 (mean VIF = 3.48). None of the building variables had variance inflation factor values in excess of 10, with building orientation having the highest variance inflation factor value of 7.55. Table 2.17 (Appendix 2: Tabulated data) shows multicollinearity test using variance inflation factors (VIF) for open space variables. There was a high degree of collinearity amongst several variables, even the mean variance inflation factor value was almost 2 (mean VIF = 1.97). None of the open space variables had variance inflation factor values in excess of 10, with open space light angle having the highest variance inflation factor value of 2.56.

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CHI-SQUARE TEST Breusch-Pagan and Cook-Weisberg (Chi-square: χ2) test was used to analyze data for heteroscedasticity (unequal variance) in the error term. Error term is normally distributed as a condition for using test of significance such as t and F. Null hypothesis (Ho) is the joint hypothesis, that is skewness is equal to zero (S = 0) and kurtosis is equal to three (K = 3), and follows the Chi-square (χ2) distribution with 2 degree of freedom (df). There was two degree of freedom because the study had imposed two restrictions namely, that skewness is zero and kurtosis is three. Therefore if in an application of the Chi-square statistic exceeds the critical chisquare value, say at the 5 percent level, the study would reject the hypothesis that the error term is normally distributed. Interestingly, the t and F tests were approximately valid in large samples (30 random plots), the approximation being quite good as the sample size increased indefinitely. Table 2.9 (Appendix 2: Tabulated data) shows heteroscedasticity using Breusch-Pagan/Cook-Weisberg test for heteroscedasticity test results for building variables. Chi-square (1) was 0.71 and 0.3999, and did not exceed critical values of Chi-square (5% level) of 1.386. The hypothesis that the error term was normally distributed was accepted. Table 2.18 (Appendix 2: Tabulated data) shows heteroscedasticity using Breusch-Pagan/Cook-Weisberg test for heteroscedasticity test results for open space variables. Chi-square (1) was 3.02 and 0.0825, and exceeded the critical value of Chi-square (5% level) of 1.386. The hypothesis that the error term was normally distributed was rejected.

4.8

TYPES

AND

PREVALENCE

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FORM

INTERVENTION ON MICRO-TEMPERATURE CHANGE Scattergrams of the linear projection of the independent variables related to the urban built form was used to represent in two dimensions the relationship 188

between two variables, and to explain the constant, coefficient, standard error and slope of line of regression. Table 2.10 (Appendix 2: Tabulated data) shows multiple regression results for building variables, while Table 2.19 (Appendix 2: Tabulated data) shows results for open space variables of the study. Building orientation was represented by a constant 1.2 (standard error 3.7 degree Celsius), coefficient 0.0033 and standard error 0.0031. The coefficient being positive explained the ascending line of regression and the observed relationship

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Figure 4.31: Scattergram with linear projection for building classification variable. Source: Field survey (2015).

Building classification was represented by a constant 1.21 (standard error 3.7 degree Celsius), coefficient -0.011 and standard error 0.0082. The coefficient being negative explained the descending line of regression (Figure 4.31) and the observed

relationship

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classification.

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Figure 4.32: Scattergram with linear projection for building road proximity variable.

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Figure 4.33: Scattergram with linear projection for building type variable. Source: Field survey (2015).

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Building road proximity was represented by constant 1.21 (standard error 3.7 degree Celsius), coefficient -0.0044 and standard error 0.013. The coefficient being negative explained the descending line of regression (Figure 4.32) and the observed relationship between micro-temperature change and building road proximity. Building type was represented by constant 1.21 (standard error 3.7 degree Celsius), coefficient 0.35 and standard error 0.46. The coefficient being positive explained the ascending line of regression (Figure 4.33) and the observed relationship between micro-temperature change and building type. Plot size was represented by constant 1.21 (standard error 3.7 degree Celsius), coefficient -0.0051 and standard error 0.0052. The coefficient being negative explained the descending line of regression (Figure 4.34) and the observed

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Figure 4.34: Scattergram with linear projection for plot size variable. Source: Field survey (2015).

Ground coverage was represented by constant 1.21 (standard error 3.7 degree Celsius), coefficient 0.037 and standard error 0.046. The coefficient being

191

positive explained the ascending line of regression (Figure 4.35) and the observed

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Figure 4.35: Scattergram with linear projection for ground cover variable.

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Figure 4.36: Scattergram with linear projection for plot ratio variable. Source: Field survey (2015). 192

Plot ratio was represented by constant 1.21 (standard error 3.7 degree Celsius), coefficient -0.015 and standard error 0.025. The coefficient being negative explained the descending line of regression (Figure 4.36) and the observed relationship between micro-temperature change and plot ratio. Open space orientation was represented by constant 0.43 (standard error 6.9 degree Celsius), coefficient 0.0018 and standard error 0.005. The coefficient being positive explained the ascending line of regression (Figure 4.37) and the observed

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Figure 4.37: Scattergram with linear projection for open space orientation variable. Source: Field survey (2015).

Open space road proximity was represented by constant 0.43 (standard error 6.9 degree Celsius), coefficient -0.007 and standard error 0.025. The coefficient being negative explained the descending line of regression (Figure 4.38) and the observed relationship between micro-temperature change and open space road proximity. 193

Open space light angle was represented by constant 0.43 (standard error 6.9 degree Celsius), coefficient 0.04 and standard error 0.067. The coefficient being positive implies an ascending line of regression (Figure 4.39) and observed

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relationship between micro-temperature change and open space light angle.

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Figure 4.38: Scattergram with linear projection for open space road proximity variable.

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Figure 4.39: Scattergram with linear projection for open space light angle variable. Source: Field survey (2015). 194

Open space shading coefficient was represented by constant 0.43 (standard error 6.9 degree Celsius), coefficient -0.01 and standard error 0.026. The coefficient being negative explained the descending line of regression (Figure 4.40) and the observed relationship between micro-temperature change and open space shading coefficient. Open space length was represented by constant 0.43 (standard error 6.9 degree Celsius), coefficient -0.056 and standard error 0.047. The coefficient being negative explained the descending line of regression (Figure 4.41) and the observed relationship between micro-temperature change and open space length. Open space area was represented by constant 0.43 (standard error 6.9 degree Celsius), coefficient 0.0007 and standard error 0.0012. The coefficient being positive explained the ascending line of regression (Figure 4.42) and the observed

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Figure 4.40: Scattergram with linear projection for open space shading coefficient variable. Source: Field survey (2015).

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Figure 4.41: Scattergram with linear projection for open space length variable.

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Figure 4.42: Scattergram with linear projection for open space area variable. Source: Field survey (2015). 196

Open space hard landscape was represented by constant 0.43 (standard error 6.9 degree Celsius), coefficient -0.056 and standard error 0.047. The coefficient being negative explained the descending line of regression (Figure 4.43) and the observed relationship between micro-temperature change and open space hard

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landscape.

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Figure 4.43: Scattergram with linear projection for open space hard landscape variable. Source: Field survey (2015).

In summary, the urban built form variables with negative coefficient values were: building classification, building and open space road proximity, plot size, plot ratio, open space shading coefficient and open space length, which explained the descending line of regression and the observed relationship between microtemperature change and these urban built form variables. Similarly, the urban built form variables with positive coefficient values were: building and open space orientation, building type, ground cover, open space 197

light angle, open space area, and open space hard landscape, which explained the ascending line of regression and the observed relationship between microtemperature change and these urban built form variables. By definition, the null hypothesis for the relationship between the independent variable with the dependent variable should not have zero as a coefficient value. Either positive or negative coefficient values imply an ascending or descending relationship, and as such, an acceptance of the alternative hypothesis. Neither of the building nor open space variables had zero as a coefficient value. As such the null hypothesis was rejected and the alternative hypothesis was accept, which linked the building and open space variables of the urban built form with the micro-temperature change based on the coefficient in the regression at the significance level of 0.05 or 95 percent confidence level.

HYPERSPACE DIAGRAM The hyperspace diagram was used to compare different distributions of two independent variables with the dependent variable, when such distributions are drawn on the same graph using multivariate analysis. Figure 4.44 shows a hyperspace diagram of the results using cluster means data of building orientation, building classification and micro-temperature change variables.

MULTIPLE REGRESSIONS Multiple regression was used to analyze data and testing the hypothesis on the implicit null hypothesis, statistical significance of the estimated coefficient, testing the hypothesis about the true or population regression coefficient, test the hypothesis that all the slope coefficients are simultaneously equal to zero, overall significance of the regression, measure the goodness of fit of the estimated line or plane and to give a percentage explanation of either the seven building or the seven open space explanatory variables.

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COEFFICIENT OF REGRESSION TEST A decision on the null hypothesis based on the population value of the coefficient of the regression model is a negation (zero) or affirmation (one) based on the regression model for either the building or open space variable. That is, the particular regressor has no influence on the regressand, after holding the other regressor values as constant. The coefficient value lies between minus one and plus one. Variables with minus one depicted regression lines of descending order while positive coefficient values drew regression lines of ascending orders. Zero or neutral coefficient values had lines running parallel to the coefficient axis, implying no relation with the dependent variable Y.

P-VALUE The study tested the implicit null hypothesis using the t statistic and p-value for the population coefficient, the test is implied, with the smaller the p-value, the greater the evidence against the null hypothesis. Table 2.10 (Appendix 2: Tabulated data) gives p-values (P>t) of the building variables, Table 2.11 gives statistical tests citation for building variables, while Table 2.19 was for open space variables. Pvalues ranged between zero and one. Either building or open space variables with zero or near zero had a high chance of rejecting the implicit null hypothesis for the alternative hypothesis, while building variables with one or near one p-values had a high chance of accepting the null hypothesis and rejecting the alternative hypothesis for that particular building variable in relation to the dependent variable microtemperature change. Building variables with p-values of between zero and 0.49 were: building orientation, building classification, building type, plot size and ground cover. Building variables with p-values of between 0.5 and one were: road proximity and plot ratio. Open space variables with p-values of between zero and 0.49 were: open space length and open space hard landscape. Open space variables with p-values of 200

between 0.5 and one were: open space orientation, open space road proximity, open space light angle, open space shading coefficient and open space area. Null hypothesis based on the significance level (α) values chosen has an impact on the acceptance or rejection of the null hypothesis, whereby the null hypothesis is rejected when the P-value is lower than the chosen significance α value. At significance level of 0.01 (99% confidence level), that is, a value of almost zero and actually a very high significance and confidence level, all of the building and open space variables had p-values of over 0.01 and thus, rejection of the null hypothesis based on the significance level of 0.01. At significance level of 0.05 (95% confidence level), again, all the building and open space variables had p-values of over 0.05, and again the null hypothesis was rejected based on the significance level of 0.05.

T-TEST T-test of significance hypothesis testing of the true or population regression coefficient (bk) was based on accepting the null hypothesis that the population regression coefficient is zero in the comparison between computed t-statistic for that building variable and critical value of the t-distribution and to ascertain the probability of obtaining such a t-value or greater. If the probability of obtaining the computed t-value is small, say 5 percent or less, one can reject the null hypothesis that the population regression coefficient is equal to zero (bk = 0). Estimated t-value is statistically significant, that is, significantly different from zero. The common chosen probability values or levels of significance are 0.01 (10%) and 0.05 (5%). Associated with a t statistic is its degree of freedom. In the k variable regression, df is equal to the number of observations (n) minus the number of coefficients estimated (k).

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Table 2.12 (Appendix 2: Tabulated data) shows t-test hypothesis testing of the true or population regression coefficient with zero results for building variables, while Table 2.20 was for open space variables.

F-TEST OF OVERALL SIGNIFICANCE F-test of overall significance for testing hypothesis that the slope of the regression line are simultaneously equal to zero was used to compare the computed F-statistic for the either building or open space variables and the critical value of the F-distribution in order to determine the probability of obtaining such a F-value or greater. If the computed F-value is greater than its critical or bench-mark F-value at the chosen level of significance (α), one can reject the null hypothesis and conclude that at least one regressor is statistically significant. Computed F-value for the combined building variables was 0.62 with degree of freedom of 7 and 23 (Table 2.10 – Appendix 2: Tabulated data). Critical F-value at significance level of 5 percent (0.05) was 2.53 and at significance level of 1 percent (0.01) was 3.71. Computed F-value for the combined open space variables was 0.55 with degree of freedom of 7 and 8 (Table 2.19 – Appendix 2: Tabulated data). Critical F-value at significance level of 5 percent (0.05) was 3.58 and at significance level of 1 percent (0.01) was 6.37. The computed F-value is lower than the critical F-value at either the significance level of either 5 or 1 percent, and as such one can accept the null hypothesis that neither combined building nor the combined open space variables were statistically significant on the microtemperature change.

R-SQUARE TEST R-square test of the measure of goodness of fit of the estimated line or plane in order to give a percentage explanation of the seven building and seven open space explanatory variables was carried out. 202

The R-square (R2) value of 0.1583 (Table 2.10 – Appendix 2: Tabulated data) means that approximately 16 percent of the variation in the dependent variable (micro-temperature change) is explained by the variation in the seven explanatory building variables. It might seem that this R-square value is rather low, but one must keep in mind that one has 210 (30 by 7 equals 210) observations with varying values of the regressand and regressors. The R-square (R2) value of 0.3250 (Table 2.19 – Appendix 2: Tabulated data) means that approximately 33 percent of the variation in the dependent variable (micro-temperature change) is explained by the variation in the seven open space explanatory variables. It might seem that this R-square value is rather low, but one must keep in mind that one has 112 (16 by 7 equals 112) observations with varying values of the regressand and regressors. In such a diverse setting the R-square values are typically low and they are often low when individual-level data are being analyzed. R-square is an increasing function of the number of regressors – i.e. R-square values increases with an increase of a variable to the model.

SLOPE COEFFICIENT TEST Testing of hypothesis for building and open space variables was used in the study to: test the slope coefficients, which were simultaneously equal to zero, testing single building and open space variables (two-tailed and one-tailed test), type I error and type II error of hypothesis testing, interval and point estimates for hypothesis testing. Hypothesis testing involved assessing the veracity of the average micro-temperature change, whose building result was 3.42 degree Celsius and open space result was 3.73 degree Celsius, from the population to choose a random sample of 30 plots and 16 open spaces, and to see whether or not the average microtemperature change from that sample was statistically different from the actual temperature change of Y in degree Celsius (oC). 203

Hypothesis testing was undertaken using multivariate analysis of the regression results by the F-test statistic (overall significance of the regression). A two-tailed point estimation result of the hypothesis testing using the t test and a significance value of 5 percent of a single numerical value such as the building orientation mean of 171.2 degrees was compared against the results of the open space orientation for the random sample of 16 open spaces with mean value of 158.5 degrees, sample standard deviation of 106.5 and 15 degrees of freedom yielded a computed t value of -0.46 compared to a critical value of 2.7. Since the tvalue of – 0.46 is less in absolute value than the critical t-value; one can accept the null hypothesis at the 95 percent confidence level, that the population mean is 171.2 degrees, as opposed to the alternative hypothesis that it is not 171.2 degrees. A two-tailed interval estimation result of the hypothesis testing using the ttest and a 95% confidence interval around the sample mean was as follows: P (83.34 ≤ μX ≤ 233.66) = 0.95

(Formula 4.1)

Since the 171.2 degrees lies within the confidence interval and based on the 95 percent confidence interval one accepts the null hypothesis that the true population orientation is 171.2 degrees, and against the alternative hypothesis that the true population orientation is not equal to 171.2 degrees. If one were to conduct a one-tailed test for this case rather than a two-tailed test, the critical t-value would be 1.753 and again one would accept the null hypothesis and reject the alternative hypothesis that the population mean for the building orientation is 171.2 degrees. A one-tailed interval estimation result of the hypothesis testing using the ttest and a 95 percent confidence interval around the sample mean was as follows: P (- ∞ < μX ≤ 206.74) = 0.95

(Formula 4.2)

Since the 171.2 degrees lies within the confidence interval and based on the 95 percent confidence interval one accepts the null hypothesis that the true population orientation is 171.2 degrees and reject the alternative hypothesis that the true population orientation is less than 171.2 degrees. 204

4.9 PROMPTS AND BARRIERS ON SUSTAINABLE URBAN BUILT FORM IN A TEMPERATURE CHANGING ENVIRONMENT Results of the micro-temperature change data logged observations were marked at the locations sampled in relation to the plots, open space, road and paths, on the site plan. Further spot observations of the road and its surroundings were also marked on the same site plan. By using interpolation techniques of averaging two respective points and marking the result on the plan, the study was able to extrapolate lines of equal temperatures – i.e. isotherm distribution map.

ISOTHERM DISTRIBUTION Isotherm distribution maps were used in climatic design to understand a new and unfamiliar climate and especially micro-temperature (i.e. Komarock Infill B Estate micro-temperature climate), whereby one must relate the unfamiliar climate to a familiar one (i.e. Jomo Kenyatta International Airport Metrological Station temperature recordings) then measure and note essential differences (i.e. temperature change). This is best done by using the standard graphic presentation first for the climate of one’s home-town (i.e. software projections to establish a baseline temperature) and then for the strange climate being investigated (i.e. data logged temperature measurements). When the two graphs are placed side by side or superimposed (i.e. micro-temperature change) similarities and differences become apparent and characteristic features can be identified. Even the comparison of simplified climate graphs can reveal the most important differences. In the study, the isotherm line shows the development of steep contours or thermal ridges nearer the main road with temperature change readings of 6.0 degree Celsius and thermal depressions around open spaces of 1.0 degree Celsius. This thereby established a relation between the road proximity, with the extreme high temperatures measured at those locations. Buildings located further away from the 205

roads, recorded lesser temperatures, and open spaces depicted the least thermal impact in relation to the micro-temperature change. From the results of this study, it is clear that there was a relationship between road proximity variable to isotherm contours, given the increasing intensity of 4 to 6 degree Celsius. This result was evident in the isotherm contours running parallel to the primary roads and following the secondary roads into the estate, and at the entrances and exits. The tertiary system was affected by light angle and hard landscape coefficients. Heat sinks were related to the open space size and its proximity to the road.

INTER-PLOT VARIABILITY In the study, inter-plot variability was used to develop bar graph for the plots sampled. The inter-plot orientation variability ranged from minus 125.2 degrees to plus 144.8 degrees based on an average orientation of 171.2 degrees for the building orientation variable, while inter-plot building classification variability ranged from minus 34.1 metres to plus 112.9 metres and was based on an average row of buildings for the building classification variable. The variability study, run in the period 8th June 2013 to 19th September 2015 on 30 plots being sampled out of the population of 240 plots. Results of the inter-plot average building orientation variability suggests that as the independent building orientation variable value increases in degrees, the dependent micro-temperature change variable value also increases in degree Celsius. Average building orientation was 171.2 degrees (Y: 3.4oC). Table 2.21 (Appendix 2: Tabulated data) shows inter-plot minimum building orientation variability, while Table 2.22 (Appendix 2: Tabulated data) shows inter-plot maximum building orientation variability findings. Average minimum inter-plot building orientation variability was 46 degrees (Y: 3.0oC, R: 2.7oC), while average

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maximum inter-plot building orientation variability was 316 degrees (Y: 3.7oC, R: 1.1oC). In the case of the building classification, there seemed to be a steep rise in the positive values while the curve for the negative values was shallower (Figure 4.45).

Building classification variability: M

Sample: No.

Figure 4.45: Bar graph showing inter-plot building classification variability over study area for the plots sampled. Source: Field survey (2015).

Results of the inter-plot average building classification variability suggests that as the independent building classification variable value increases in unit metres, the dependent micro-temperature change variable value decreases in degree Celsius. Average building classification was 40.1 metres (Y: 3.4oC). Table 2.23 (Appendix 2: Tabulated data) shows inter-plot minimum building classification variability, while Table 2.24 (Appendix 2: Tabulated data) shows inter-plot maximum building classification variability findings. Average minimum inter-plot building classification variability was 6 metres (Y: 4.7oC, R: 1.9oC), while average 207

maximum inter-plot building classification variability was 153 metres (Y: 1.5oC, R: 0.2oC).

4.10 REFLECTION ON THE RESULTS The significant urban built form variables that had an impact on the microtemperature change in the structured neighbourhood of Komarock Infill B Estate, in an upland climate in Nairobi were identified as: building type, plot and open space size, building and open space orientation, building and open space road proximity, building classification, ground coverage, plot ratio, hard landscape coefficient, light angle, shading coefficient and open space length. Predictive nomogram for the average micro-temperature change for building variables for structured neighbourhoods in tropical upland climates were developed to show a linkage between the seven significant building variables (Figure 4.46) and seven significant open space variables (Figure 4.47). Bivariate and multivariate data analysis and testing of the hypothesis ensued that the drawing results were related to establishing the relationship between the independent urban built form variables and the dependent micro-temperature change variable, which in turn were incorporated in chapter five and will be reported in the synthesis and interpretation of findings.

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CHAPTER FIVE SYNTHESIS AND INTERPRETATION OF FINDINGS This chapter is a discussion and interpretation of the findings of the study, in line with the literature reviewed, it also attempt at synthesize the findings in line with the objectives of the study. Objective one sought to identify urban built form variables causing temperature change in Komarock Infill B Estate, while objective two sought to determine the influence of the significant urban built form variables in contribution to temperature change. The results of the study were presented in chapter four, which analyzed the data collected. Chapter five is essentially an attempt to answer the research questions of the study. Chapter five is organized in such a way as to capture and reveal how the findings were synthesized and interpreted, in line with objective three which sought to develop design and planning strategies in view of sustainable urban built form in a temperature changing environment. The synthesized findings were presented first before indicating how they relate to the literature reviewed. The chapter also elaborates on the consistency of the synthesized finding with findings from the authorities reviewed, stating clearly the contribution of the study findings in filling the knowledge gap identified in the literature reviewed in chapter two and in practice. An insight into the hypothesis testing is also attempted. Important drawings and tables are integrated within the text and the discussions are tailored as per the objective thematic areas which were urban built form variability and trend, micro-temperature change variability and trend, urban built form and microtemperature change relationship, and lastly a reflection on the findings.

5.1 URBAN BUILT FORM VARIABILITY AND TREND The objective and thematic area of urban built form variability and trend sought to tackle the issue of the results captured in the study, and in synthesizing and interpreting the findings in line with the third objective of the study which was to develop design and planning strategies in view of sustainable urban built form in 211

a temperature changing environment. Reviewed literature and practice on the issue of the significant building and open space variables of the urban built form independent variable that have an impact on the micro-temperature change dependent variable were weaved in. On the issue of the determination of significant building variables and temperature change, the findings of the study identified and determined the significance of the independent building variables of the urban built form as having an impact on the dependent variable micro-temperature change: these variables were: building orientation, building classification, building road proximity, building type, plot size, ground coverage and plot ratio.

BUILDING ORIENTATION The findings of the study related to the issue of the impact of the building orientation variable on the micro-temperature change variable established that the average building orientation was 171.2 degrees, correlated to an average temperature change of 3.4 degree Celsius (oC), while the minimum building orientation was 46 degrees correlating to 1.4oC, the maximum building orientation was 316 degrees and 7.2oC temperature change, and a positive and ascending regression line of relation between the building orientation and the temperature change. The data seems to suggest that the lower the building orientation in degrees from the north, the lower the temperature change values. However, as the orientation was measured in degrees in a clockwise motion from the north, the data was plotted in the form of a polar curve relationship between micro-temperature change and building orientation variable distribution pattern in concentric circles (Figure 5.1). Plot 211 (Basic Villa, O: 46 Degrees, W: 36 M, RP: 35.6 M, H: 5.3 M, PS: 108 M2, GC: 44%, PR: 44%) had temperature reading of 1.5oC (June), while Plot 225 (Basic Villa, O: 224 Degrees, W: 36 M, RP: 38.5 M, H: 5.3 M, PS: 108 M2, GC: 45%, PR: 45%) had temperature reading of 6.7oC (September). Building orientation and building road proximity being the dominant factors. 212

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This study findings were consistent with Singh and Singh (2010, pp.158 – 176), whose observations established the orientation of the building as a significant variable, related to the temperature change, by noting that planning the development of a site must take into account the site, its surrounding, local climate, traditions of the people and the natural elements such as the sun, rain, wind and orientation of the structures.

BUILDING CLASSIFICATION Findings of the study related to the issue of the impact of the building classification variable on the micro-temperature change variable established that the building classification and the width of the rows of houses were a significant variable with average width of the row of buildings of 40.1 metres (semi-detached house) scoring a 3.4 degree Celsius temperature change, minimum width of row of buildings of 6 metres (detached house) scoring 1.4 degree Celsius, maximum width of row of buildings of 153 metres (row housing) with 7.2 degree Celsius, and a negative and descending line of regression relation between the building classification and the temperature change. The data seems to suggest that the lower the width of the row of the building, the higher the temperature change figures. This study identified four categories of building: detached, semi-detached, row and change of user, and building classification related to the width of the row of buildings was a significant variable in the determination of temperature change. The findings of the study were consistent with Singh and Singh (2010, p.24, 27 and 39), who recommended the classification of buildings into six categories based on use and tenement basis.

BUILDING ROAD PROXIMITY On the issue of the impact of the building road proximity on the microtemperature change variable, the findings of the study established that the average 214

building road proximity was 51.9 metres, correlated to an average temperature change of 3.4 degree Celsius (oC), while the minimum road proximity was 11.4 metres correlating to 7.2oC, the maximum road proximity was 98.2 metres with 1.4oC temperature change, and a negative and descending line of regression relation between the distance of the building to the main road and the temperature change. The data seems to suggest that the shorter the distance from the main road, the higher the temperature change values. However, location and distribution of the dependent variable frequency was best represented on isotherm distribution map (Figure 5.2). The finding was consistent with Singh and Singh (2010, p.4, p.39 and p.44) which identified planning and design attitude as basic to urban built forms.

BUILDING TYPE The findings of the study established the building type and height of the building as a significant variable with an average height of 6.4 metres (Maisonette) scoring a 3.4 degree Celsius temperature change, minimum height of 5.3 metres (villa) scoring 1.4 degree Celsius, maximum height of 7.7 (change of user and transformed plots: Figure 5.3 and Figure 5.4) with 7.2 degree Celsius, and a positive and ascending line of regression relation between the height of the building and the temperature change. The data seems to suggest that the lower the height of the building, the lower the temperature change value. Plot 68 (Transformed, O: 46 Degrees, W: 12 M, RP: 68.4 M, H: 6.2, PS: 108 M2, GC: 74%, PR: 106%) had temperature reading of 3oC (February), while Plot 71 (Transformed, O: 46 Degrees, W: 6 M, RP: 51.8 M, H: 6.2 M, PS: 108 M2, GC: 86%, PR: 162%) had temperature reading of 3.7oC (February). Width of rows of buildings, building road proximity, ground coverage and plot ratio, seemed to be the dominant factors.

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Plot 99 (Basic Maisonette, O: 136 Degrees, W: 153 M, RP: 83.5 M, H: 7.7, PS: 108 M2, GC: 37%, PR: 71%) had temperature reading of 1.4oC (May), while Plot 142 (Basic Villa, O: 136 Degrees, W: 57.5 M, RP: 43 M, H: 6.4 M, PS: 108 M2, GC: 62%, PR: 82%) had temperature reading of 7.2oC (September). Width of rows of buildings, building road proximity, height of building, ground coverage and plot ratio, seemed to be the dominant factors. Heat is transmitted through the roof of low buildings (Basic Villa) compared to the taller building (Basic Maisonette). The month of data collection also seemed to have affected the results; September is traditionally warmer than May, in the tropical climates. The findings of the study are consistent with findings by Singh and Singh (2010, p.1), who noted that planning and designing of buildings does not only involve the building, but also the form and function relate to the building type, the choice of planning and design attitude to urban built form (Singh & Singh, 2010, p.3).

Figure 5.3: Shows the Transformed Plot 71. Source: Field survey (2015). 217

Figure 5.4: Shows the Transformed Plot 68. Source: Field survey (2015).

The findings of this study suggest that building type and the height of buildings were a significant built form variable in relation to the temperature change, and compare well with Firth and Wright (2008, p.1), who came up with recommendation based on building type for the temperate climate.

PLOT SIZE On the issue of the impact of the plot size on the micro-temperature change variable, the findings of the study established that the average plot size was 140 square metres (m2) and correlated to an average temperature change of 3.4 degree Celsius (oC), while the minimum plot size was 101 m2 correlating to 1.4oC, maximum plot size was 422.3 m2 and 7.2oC temperature change, and a negative and descending line of regression relation between the size of plot and the temperature change. The data seems to suggest that the lower the size of the plot, the higher the temperature change value.

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The findings of the study are in conformity with Singh and Singh (2010, p.22), who noted that setting of a minimum plot size is one aspect of density zoning. However, given the nature of the planning at Komarock Infill B Estate of placing the larger plots near the road and the small ones on the inside, the building road proximity seems to override the plot size in this regard by having an inverse relationship between the plot size and the temperature change variable.

BUILDING GROUND COVERAGE On the issue of the impact of the building ground coverage on the microtemperature change variable, the findings of the study established that the average building ground coverage was 47.6 percent and correlated to an average temperature change of 3.4 degree Celsius, minimum ground coverage of 23 percent correlated to 1.4 degree Celsius, maximum ground coverage of 86 percent with 7.2 degree Celsius, and a positive and ascending line of regression relation between the ground cover of the building and the temperature change. The data seems to suggest that the lower the ground coverage, the lower the temperature change value. The findings of the study were related to the temperature change, and suggest that both the ground coverage (urban built-up area) and light angle (height to width ratio) were significant building and open space variables. Other studies by Rose, Horrison and Venkatachalam (2011, p.5) of six urban built forms (dense compact mid-rise urban form and dispersed low-rise urban form) in relation to urban geometry (the density of building height to width ratio), sky view factor and green cover (vegetation), in Chennai Metropolitan Area (India) using a computer model, concluded that the height to width ratio influenced comfort conditions significantly when compared to the percentage of urban built-up area.

BUILDING PLOT RATIO On the issue of the impact of the building plot ratio on the microtemperature change variable, the findings of the study established that the average 219

building plot ratio was 65.9 percent and correlated to an average temperature change of 3.4 degree Celsius, minimum plot ratio of 29 percent correlated to 1.4 degree Celsius, maximum plot ratio of 162 percent with 7.2 degree Celsius, and a negative and descending line of regression relation between the ground cover of the building and the temperature change. The data seems to suggest that the lower the plot ratio, the higher the temperature change figures. The findings from this study suggests that with ground coverage (GC) of approximately 47.6 percent and plot ratio (PR) of 65.9 percent, on average 3.4 degree Celsius temperature change would be realized. The findings are in agreement with Mumina and Mundia (2014, p.41), who recommended the use of development controls, green building technology, establishment of Nairobi green corridors and space to ameliorate against urban micro-climate change.

SIGNIFICANT OPEN SPACE VARIABLES On the issue of the determination of significant open space variables and temperature change, the findings of the study identified and determined the significance of the open space variables of the built form having an impact on the independent variable micro-temperature change; these were: open space orientation, open space road proximity, open space light angle, open space shading coefficient, open space length, open space area and open space hard landscape coefficient.

OPEN SPACE ORIENTATION On the issue of the impact of the open space orientation on the microtemperature change variable, the findings of the study established that the average open space orientation was 159 degrees and correlated to an average temperature change of 3.7 degree Celsius (oC), while the minimum open space orientation was 46 degrees correlating to 1.6oC, maximum open space orientation was 316 degrees

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and 7.7oC temperature change, and a positive and ascending line of regression relationship between the orientation of the open space and the temperature change. The data seems to suggest that the lower the orientation value in degrees from the north, the lower the temperature change value. However, as the orientation was measured in degrees in a clockwise motion from the north, the data was plotted in the form of a polar curve relationship between micro-temperature change and open space orientation variable distribution pattern in concentric circles (Figure 5.5). The open space orientation is related with the open space size, in that, the bigger the open space size, the more positions available for measuring the open space orientation direction and thereby it’s impact on the temperature change. Open space OG8 (Open Ground, O: 226 Degrees, RP: 38.5 M, LA: 87 Degrees, SC: 27%, L: 36 M, A: 1071 M2, HL: 75%) had temperature reading of 7.5oC (September), while open space R9 (Road, O: 136 Degrees, RP: 83.5 M, LA: 80 Degrees, SC: 39.3%, L: 67.9 M, A: 815 M2, HL: 50%) had temperature reading of 1.6oC (May). Open space orientation and road proximity, light angle, shading coefficient, length, area and hard landscape were dominant factors in temperature change. Type of open space (open ground or road) and month of data logging seem to affect the findings. September is traditionally warmer than the month of May in the tropical climates. Open space P6 (Path, O: 316 Degrees, RP: 50.5 M, LA: 55 Degrees, SC: O%, L: 39 M, A: 117 M2, HL: 100%) had temperature reading of 2oC (June), while open space P3 (Path, O: 226 Degrees, RP: 74.5 M, LA: 55 Degrees, SC: 51%, L: 42 M, A: 126 M2, HL: 100%) had temperature reading of 6oC (September). Open space orientation and road proximity, shading coefficient, length and area were dominant factors on the temperature change. Month of data logging seemed to affect the findings, as September is traditionally warmer than June in the tropical climates. Use of plants and shrubbery, within the plots ameliorated the temperature (Figure 5.6).

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Figure 5.6: Shows the usage of garden areas for a Maisonette Plot 76. Source: Field survey (2015). OPEN SPACE PROXIMITY On the issue of the impact of the open space road proximity on the microtemperature change variable, the findings of the study established that the average open space road proximity was 61 metres and correlated to an average temperature change of 3.7 degree Celsius ( oC), while the minimum open space road proximity was 24.9 metres correlating to 7.5oC, maximum open space road proximity was 97 metres with 1.6oC temperature change, and a negative and descending line of regression relation between the orientation of the open space and the temperature change. The data seems to suggest that the shorter the distance from the main road, the higher the temperature change values. However, location and distribution of the dependent variable frequency was best represented on isotherm distribution map (Figure 5.7).

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High temperatures were recorded in open spaces within the plots near the main roads (Figure 5.8). Open space of Plot 19 (O: 205 Degrees, RP: 11.4 M) had temperature reading of 3.3oC (June).

OPEN SPACE LIGHT ANGLE On the issue of the impact of the open space light angle on the microtemperature change variable, the findings of the study established that the average open space light angle was 81 degrees and this correlated to an average temperature change of 3.7 degree Celsius, minimum light angle of 55 degrees correlated to 1.6 degree Celsius, maximum light angle of 90 degrees with 7.5 degree Celsius, and a positive and ascending line of regression relation between the light angle of the open space and the temperature change. The data seems to suggest that the lower the light angle value, the lower the temperature change figures. The finding of this study suggests a correlation of minimum light angle of 55 degrees with minimum temperature change of 1.6 degrees Celsius, while a maximum light angle of 90 degrees correlated to a maximum temperature change of 7.5 degrees Celsius. Average light angles of 81 degrees had 3.7 degrees Celsius. Kenyan legislation in the form of Draft Physical Planning Act (Laws of Kenya, 2009) and Republic of Kenya Physical Planning Act (2012) requires a sixty degree light angle for open spaces in respect to an adjoining building (Ebrahim, 2008a, p.42) is in conformity with the study findings. The study identified other planning and design guidelines in regard to the ground coverage, plot ratio and height of buildings in structured neighbourhoods in a tropical upland climate. The findings of the study concurs with Oke’s (1988) concern on the establishment of quantitative guidelines on street geometry and dimensions; Oke thus conducted urban canyon field studies, scale and mathematical modelling, shelter and urban geometry in air flow and natural ventilation in and around buildings, dispersion and urban geometry, urban warmth and geometry (Oke, 1988, 225

p.103). Oke (1988) deduced that open space variables were: orientation, height of adjoining buildings and width of open space, sky view angles and light angles.

Figure 5.8: Shows the usage of garden areas for a school Plot 19. Source: Field survey (2015).

OPEN SPACE SHADING COEFFICIENT On the issue of the impact of the open space shading coefficient on the micro-temperature change variable, the findings of the study established that the average shading coefficient was 48 percent and correlated to an average temperature change of 3.7 degree Celsius, minimum shading coefficient of zero percent correlated to 7.5 degree Celsius, maximum shading coefficient of 87 percent with 1.6 degree Celsius, and a negative and descending line of regression relation between the shading coefficient of the open space and the temperature change. The data seems to suggest that the lower the shading coefficient value, that is very little shading primarily from trees, the higher the temperature change figures, thereby implying an inverse relationship between the open space shading coefficient 226

and the micro-temperature change variables. The study finding suggests that with average shading of approximately fifty percent and a hard landscape coefficient of sixty percent, there was a 3.7 degrees Celsius temperature change. The study by Mumina and Mundia (2014, p.38), used Landsat Satellite Imagery and showed a negative relation between degrading of vegetation cover and the increase in surface temperature of Nairobi Metropolis.

OPEN SPACE LENGTH On the issue of the impact of the open space length on the microtemperature change variable, the findings of the study established that the average open space length was 40 metres and correlated to an average temperature change of 3.7 degree Celsius, minimum open space length of 18 metres correlated to 1.6 degree Celsius, maximum open space length of 68 metres with 7.5 degree Celsius, and a negative and descending line of regression relation between the length of the open space and the temperature change. The data seems to suggest that the lower the open space length value, the higher the temperature change figures.

OPEN SPACE AREA On the issue of the impact of the open space area on the micro-temperature change, the findings of the study established the average open space area as a significant variable with average open space area of 759.4 square metres correlating to 3.7 degrees Celsius, minimum open space of 117 square metres (7.5oC), maximum open space of 1838 square metres (1.6oC), and a positive and ascending line of regression relation between the size of the open space and the temperature change. The data seems to suggest that the lower the area value, the lower the temperature change figures. Again, as was the case with the building plot size, given the nature of the planning at Komarock Infill B Estate of placing the larger open spaces near the road and the small ones on the inside, the road proximity seems to 227

override the open space area in this regard by having an inverse relationship between the open space area and the temperature change variable. Also, the hard landscape ratio and the shading coefficient would seem to have an impact on the temperature change of the open spaces. The finding from this study suggests that open space areas of minimum 117 square metres, maximum 1838 square metres and average size of 759.4 square metres were recorded at Komarock Infill B Estate, compared to Kenya Building and Planning Legislation, Draft Amended Building Byelaws (Laws of Kenya, 2009) and Republic of Kenya Building Code (1976, p.18) which provides siting and space about buildings, requires courtyards and open spaces free from obstructions with minimum area of 31.5 square metres (Ebrahim, 2008a, p.42).

OPEN SPACE HARD LANDSCAPE COEFFICIENT On the issue of the impact of the open space hard landscape coefficient on the micro-temperature change variable, the findings of the study established that the average hard landscape coefficient was 62.7 percent and correlated to an average temperature change of 3.7 degree Celsius, minimum hard landscape coefficient of 40 percent correlated to 1.6 degree Celsius, maximum hard landscape coefficient of 100 percent with 7.5 degree Celsius, and a positive and ascending line of regression relation between the hard landscape of the open space and the temperature change. The data seems to suggest that the lower the open space hard landscape value that is a large area of green landscape, the lower the temperature change values. The finding of this study on structured neighbourhoods of Komarock Infill B Estate of 4.72 hectare area and with 240 plots lead to a 3.4 degrees Celsius temperature increase compared to the baseline temperature of rural Nairobi, which can be compared with Mumina and Mundia (2014, p.41), who related surface temperature increased with the formation of roads, buildings, urban sprawl and degrading of the green cover. 228

The finding of this study using meteorological data (Kenya Meteorological Department, 1984) which set a baseline temperature, measured an average 3.4 degree Celsius micro-temperature increase, with minimum temperature of 1.4 and maximum temperatures of 7.2 degree Celsius over a period of thirty one years (1984 – 2015). The Inter Government Panel on Climate Change (IPCC) study showed a 0.71 degree Celsius temperature change over a fifty year period (Shuckburg, 2007, p.6).

5.2 MICRO-TEMPERATURE CHANGE VARIABILITY AND TREND The objective and thematic area of micro-temperature change variability and trend sought to synthesize and interpret the findings in line with the third objective of the study which was: to develop design and planning strategies in view of sustainable urban built form in a temperature changing environment. The study reviewed literature and practice on isotherm distribution, and determined the significant building and open space variables of the built form independent variable that have an impact on the micro-temperature change dependent variable.

ISOTHERM DISTRIBUTION The findings of this study related the road proximity variable with isotherm contours of increasing intensity of 4 to 6 degree Celsius running parallel to the primary roads and following the secondary roads into the estate at the entrances and exits. The tertiary system was affected by light angle and hard landscape coefficients. Heat sinks were related to the open space size and its proximity to the road. The finding from the study made observations on the urban heat sinks around open spaces and isotherm contours related to the road network. This conformed to work by Meffert (1981, p.2) on the development of heat islands in

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Nairobi and Lamu. The study made observations related to urbanism and human health. The findings of study and observations on urban heat islands were in conformity with those achieved by Mumina and Mundia (2014, p.41), who related surface temperature increase with the development of heat islands. On the issue of open spaces, through using statistical and geospatial analysis, the study findings suggests that the open spaces acted as heat sinks while the roads had heat ridges projected as isotherm contours running parallel to the road. This is in conformity with Choi Lee and Byun (2012, pp.127 – 133), who noted that the relationship between urban climate and open space had not been examined in previous studies and attempted to fill this gap using spatial and seasonal variation analysis. The finding of the study suggests a minimum temperature change of 1.4 degree Celsius (oC), maximum temperature change of 7.2 degree Celsius and average temperature change of 3.4 degree Celsius. Koenigsberger (et al., 1973, p.37) reported an average of eight degree Celsius and a high of eleven degree Celsius between the city and its surrounding countryside. Copenhagen (2009) recommended 1.5 to 2 degree Celsius above baseline temperature as the limit for temperature change. The finding from the study suggests that if the mode and pace of the urbanization process continues unabated, on average, 3.4 degree Celsius temperature change would prevail, and as such exceed the limit of 2 degree Celsius set in Paris 2015. Minimum standards in regard to building and open space variables must be adhered to if the recommendations of the study finding are to have any meaningful delivery of sustainable built form. The study notes that the United Nations Framework Convention on Climate Change (UNFCCC, 2015a, 2015b and 2015c), United Nations Climate Change Secretariat (UNCCS, 2015, p.2 and 4) and Inter Governmental Panel on Climate 230

Change (UNFCCC, 2015, p.3) reviewed global temperature changes from preindustrial times and showed that the global temperature increased from 1.5 to 2 degree Celsius, recommending the establishment of a ‘Limit of Global Warming’ of 2 degree Celsius recommended at Paris 2015, to take effect in 2020 (UNCCS, 2015a, p.1). The finding of the study established an average temperature change of 3.4 degree Celsius and a range of 5.8 degree Celsius micro-temperature change. Taha (1997, pp.99 – 103) work on urban climates and heat islands in low and mid-latitude areas showed that air temperature were on average 2 and 4 degree Celsius. Taha (1997) also noted that air temperature change was related to characteristics of the local climate and examined three variables of surface albedo, evapotranspiration from vegetation and anthropogenic heating from mobile and stationary sources, with the greatest impact achieved by increasing the albedo of roofing and paving materials, and afforestation of urban areas.

5.3 URBAN BUILT FORM AND MICRO-TEMPERATURE CHANGE RELATIONSHIP The objective and thematic area of urban built form and micro-temperature change relationship sought to synthesize and interpret data in line with the third objective of the study which was: to develop design and planning strategies in view of sustainable urban built form in a temperature changing environment. The study reviewed literature and practice. In this section, the study discusses the urban built form and micro-temperature change relationship, providing insight into the interplot building orientation and inter-plot building classification, hypothesis testing, and design and development of building and open space nomogram for sustained development in a temperature changing environment.

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INTER-PLOT VARIABILITY The finding of the study on the building orientation variability revealed that the positive values almost equal the negative values, thereby implying a spiral relationship. The finding of the study suggests that there was a difference between the mean micro-temperature change of the building and the open space orientation variables of the built form variables. Orientation seems to be affected by the diurnal and annual cyclic movement of the sun relative to its position both in altitude and in azimuth. In the case of the building classification, there seemed to be a steep rise in the positive values while the curve for the negative values was shallower. It would seem that building classification was related to the wind patterns and general air flow around the building. The predominant winds over Nairobi are north easterlies and easterlies, and are associated with precipitation occasioned by moisture into the country from the Indian Ocean.

HYPOTHESIS TESTING The findings of the study on the hypothesis testing of the individual building and open space variables related to the scattergram plus slope of the regression line as regards to a null hypothesis of zero (0) and an alternative hypothesis of plus or minus one (± 1) or approaching one (1). The null hypothesis was rejected as none of the building or open space variables had a slope of zero relative to the microtemperature change. Building and open space variables that had a positive and ascending line of regression were: building and open space orientation, building type (height of buildings), ground coverage, open space light angle, open space area, and open space hard landscape. The positive and ascending line of regression suggests that as the independent urban built form variable increases in in unit value, the dependent micro-temperature change variable value also increases in degree Celsius.

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Building and open space variables with negative and descending line of regression were: building classification (width of rows of buildings), building and open space road proximity, plot size, plot ratio, open space shading coefficient and open space length. The negative and descending line of regression suggests that as the independent urban built form variable increases in in unit value, the dependent micro-temperature change variable value decreases in degree Celsius. The findings of the study report on the hypothesis testing of the individual building and open space variables related to curve analysis based on skewness (0) and kurtosis (3). Only the open space light angle had a negative skewness, all other building and open space variables had positive skewness. Skewness is the extent to which a distribution of data departs from the normal distribution along the horizontal axis and is a measure of symmetry. For a negative skew, the majority of the values cluster at the upper end of the distribution further away from zero. Positive skew implies that the majority of the cases cluster at the lower end closer to zero. Building and open space variables with under three (3) kurtosis value were: building and open space orientation, building and open space road proximity, building type, open space shading coefficient, open space area and open space hard landscape. Kurtosis is the measure of the peakedness of a distribution of data for a continuous random variable and is a measure of tallness or flatness of the probability distribution. A distribution having a negative kurtosis of less than 3 displays a flatter curve. Building and open space variables with over three (3) kurtosis value were: building classification, plot area, ground coverage, plot ratio, open space light angle and open space length. A distribution having a positive kurtosis of more than 3 displays a more peaked curve. The findings of the study on the hypothesis testing of measure of association between two variables used the Pearson correlation matrix. The Pearson correlation matrix measured the association between two variables that are measured at the 233

ratio or interval scales, and as a measure of the strength of linear relationship between two continuous variables, or the covariance of two variables divided by the product of their respective standard deviations. Correlation coefficient varies between negative one to positive one. A coefficient that is close to plus or minus one (1) indicates a strong relationship while coefficients close to zero imply little or no association. The findings suggest that no either two building or two open space variables had either perfect association value of one (1) or no association value of zero (0). The strongest correlation relating to building variables was between plot ratio and ground coverage, while for open space variables was between open space area and open space light angel at the significance level of 1 percent (0.01). The study carried out a hypothesis testing of the normality using the Shapiro-Wilk W test (z-test) as a test of the null hypothesis; a sample was drawn from a population having a normal distribution, and analyzed the P (Prob) values, which should be greater than the significance level of 0.01. The findings suggest that for urban built form variables with curves approaching a normal distribution at the significance level of 1 percent (0.01) were: building and open space orientation, building road proximity, building type, building ground coverage, open space area, building and open space micro-temperature change. Urban built form variables with skewed curve distribution were: building classification, building plot size, building plot ratio and open space light angle. The study tested the hypothesis using a multicollinearity test of variance inflation factor (VIF) as a measure of the degree to which the variance of the ordinary least squares (OLS) estimator was inflated. The study found that there was no exact linear relationship amongst the regressors. None of the building or open space variables had a perfect collinearity (i.e. equal to 1). The findings of the study on the hypothesis testing on heteroscedasticity (unequal variance) in the error term, used the Breush-Pagan and Cook-Weisberg (Chi-square: χ2) test and found that error term was normally distributed as a 234

condition for using test of significance such as t-test and F-test, by using the chisquare statistic. The study accepted the hypothesis that the error term was normally distributed for both building and open space variables of the urban built form. The findings of the study on the hypothesis testing, whereby the null hypothesis for slope coefficient is zero (0) and alternative hypothesis lying between minus one (-1) and plus one (+1), indicated that none of the building or open space variables had slope coefficient of zero (0) and thus, the null hypothesis for slope coefficient was rejected. The alternative hypothesis was accepted with negative and descending regression coefficients of building and open space variables such as: building classification, building and open space road proximity, building plot size, building plot ratio, open space shading coefficient and open space length. The alternative hypothesis was accepted with positive and ascending regression coefficients of building and open space variables such as: building and open space orientation, building type, building ground coverage, open space light angle, open space area and open space hard landscape. The findings of the study on the hypothesis testing on the implicit null hypothesis used t-statistic and p-value for the population coefficient, whereby the smaller the p-value (zero or near zero). For building and open space variables it gave evidence against the null hypothesis and one (1) or near one had a high chance of accepting the null hypothesis. The null hypothesis for implicit t statistic was rejected and the alternative hypothesis accepted for: building and open space variables for building orientation, building classification, building type, building plot size, building ground coverage, open space length and open space hard landscape. The null hypothesis for implicit t-statistic was accepted and alternative hypothesis rejected for: building and open space variables for building and open space road proximity, building plot ratio, open space orientation, open space light angle, open space shading coefficient and open space area. 235

The findings of the study on the hypothesis testing was based on the significance level chosen, whereby the null hypothesis was rejected when p value is lower than the chosen significance value. At the significance level of 1 percent (99% confidence level), all the building and open space variables had a p-value of over 0.01 (1%), and thus, the null hypothesis was rejected at the significance level of 0.01. At the significance level of 5 percent (95% confidence level), again all the building and open space variables had p values of over 0.05, and again the null hypothesis was rejected based on the significance level of 0.05. The findings of the study on the t-test of significance hypothesis testing of the true or population regression coefficient was based on assumption that the population regression coefficient is zero in comparison between the computed t statistic for the building and open space variable. The study was keen to determine the critical value of the t-distribution and the probability of obtaining such a t-value or greater. The findings of the study for individual building and open space variables showed that all the null hypotheses were rejected. The findings of the study on the F-test of overall significance (ANOVA) hypothesis testing of slope of regression lines was simultaneously equal to zero in comparing the computed F-statistic for the combined building or open space variables. The study was keen to determine the critical value of the F-distribution to probability of obtaining such a F-value or greater. The findings of the study at both the 5 percent (0.05) or 1 percent (0.01) significance level revealed that the null hypothesis was accepted. The findings of the study on the R-square test of measure of goodness of fit of the estimated line or plane showed that approximately 16 percent of the buildings and 33 percent of the open space variables in the dependent variable microtemperature change were explained by the seven building and seven open space explanatory variables. R-square is an increasing function of the number of regressors, that is, the R-square value increases with an increase of a variable. It 236

might seem that the R-square value was rather low, but one should keep in mind that one had 210 (30 by 7 equals 210) observations with varying values for the regressand and regressors. In such a diverse setting, the R-square values were typically low and more often were lower when individual level data was being considered. The findings of the study of a two-tailed point estimation of the hypothesis testing using the t test and a significance value of 5 percent (95% confidence level) of a single numerical value such as building orientation against another of say open space orientation indicate that there was acceptance of the null hypothesis that the population orientation mean was equal to the estimated orientation value, as opposed to the alternative hypothesis that the estimated orientation value was not the population orientation mean value. The findings of the study of a two-tailed interval estimation of the hypothesis testing using the t-test and 95 percent confidence interval (5% significance level) around the mean was as follows: P (83.34 ≤ μX ≤ 233.66) = 0.95

(Formula 5.1)

Null hypothesis was accepted that the true population orientation was equal to the mean value of the building and open space orientation variable. The findings of the study of a one-tailed interval estimation of the hypothesis testing using the t-test and 95 percent confidence interval (5% significance level) around the mean was as follows: P (- ∞ < μX ≤ 206.74) = 0.95

(Formula 5.2)

The null hypothesis was accepted that the true population orientation was equal to the mean value of the building and open space orientation variable.

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NOMOGRAMS The finding of the study based on the development of building and open space variables nomogram, the result of the study suggests that a multivariate approach where two or more variables are considered in tandem would yield sustainable built forms in a temperature changing environment. Remedial nomogram for the average micro-temperature change for building variables for structured neighbourhoods in tropical upland climates were developed to show a linkage between the seven significant building variables (Figure 5.9) and seven significant open space variables (Figure 5.10). By taking any two variables, one could assess the effect on the resultant micro-temperature change can be assessed and relevant informed decisions can be made by the parties concerned. Steps to be taken in using remedial nomogram are as follows: i.

Identify the thermal source (building orientation: degrees) on Scale A1 and Scale A2,

ii.

Identify the requite thermal barrier or buffer on a respective scale as follows: a. Scale B1 (road proximity: M) ranges from near, average and further from the main road, b. Scale B2 (plot size: M2) ranges from small plot, standard sized plot and large plot near major road, c. Scale C1 (building height: M) ranges from Villa, Maisonette or change of user plot, d. Scale C2 (building width: M) ranges from row housing, semidetached house or detached house, e. Scale D1 (ground coverage: %), and f. Scale D2 (plot ratio: %),

iii.

Lay a straight-edge from these two points across to the activity or situation (micro-temperature change: oC) on Scale E1 and Scale E2, 238

which ranges from baseline climate control, active system climate control, microclimatic (rural) controls, meso-climatic (peri-urban) control and macroclimatic (urban) controls. As an example, the findings of the study using the remedial nomogram suggests that by placing a limit of 23 percent on the building ground coverage and 29 percent on the building plot ratio, a temperature change of 1.4 degrees Celsius would be realized and would meet the United Nations Climate Change Secretariat (UNCCS, 2015a) recommendation of less than 2 degrees Celsius. The average figure on temperature change of 3.4 degree Celsius was related to a minimum building plot size of 101 square metres, 11.4 metres building road proximity, 5.3 metres building height, 46 degrees building orientation and 6 metres width for row of houses.

DIAGRAMMATIC SUMMARY SHEETS The finding of the study were used to develop summary sheets of the seven buildings (Figure 5.11) and of the seven open spaces (Figure 5.12) of the urban built form variables. Summary sheets can be used by architects and planners as guidelines for the planning design and development of structured neighbourhoods, such as Komarock Infill B Estate, in tropical upland climates in a changing temperature environment. As an example, the diagrammatic summary sheets suggested that minimum figure on temperature change of 1.4 degree Celsius was related to a minimum building height of 5.3 metres, minimum plot size of 101 square metres, minimum building orientation of 46 degrees, minimum width for row of houses of 6 metres, minimum building road proximity of 11.4 metres, minimum ground coverage of 23 percent and minimum plot ratio of 29 percent for structured neighbourhoods in tropical upland climates.

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5.4 REFLECTION ON THE FINDINGS Building and open space variables with negative and descending regression coefficients were: building classification (width of row housing), building and open space road proximity, building plot size, building plot ratio, open space shading coefficient and open space length. Building and open space variables with positive and ascending regression coefficients were: building and open space orientation, building type (height of buildings), building ground coverage, open space light angle, open space area and open space hard landscape.

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CHAPTER SIX CONCLUSION AND RECOMMENDATIONS This study deduced from the literature review that the built form embodied and had key indicators on the causes and possible remedies of the temperature change. This fundamental idea was the main focus of this study which focused on how structured neighbourhoods such as Komarock Infill B Estate are designed, planned and embody a unique fingerprint on the identity, determination of significant built form variables. The study was keen to determine the development and control mechanisms necessary in the designing and planning of sustained built forms that can mitigate and remain relevant in a temperature changing environment. It is with this in mind that the study research methodology was designed and the study commenced the data collection, processing and analysis, and eventually testing of the hypothesis. The results of the study were synthesized and the findings were interpreted. This chapter proceeds with reaffirming the purpose of the study, summarizing the findings as per the objective and research question, reporting the findings of hypothesis testing, stating the philosophy behind the work, limitation of findings, conclusion, implications of the study in practice and theory, recommendations based on findings and stated as per objectives, and suggested areas for further research.

6.1 PURPOSE OF STUDY The aim and problem statement posed by the study was to what extent did micro-temperature change at the Komarock Infill B Estate study site compare to the Jomo Kenyatta International Airport (JKIA) Nairobi office of the meteorological department, for the period 8th June 2013 to 19th September 2015 in respect to the significant identified urban built form variables, further, what factors were responsible for the temperature differentials between Komarock Infill B Estate study site and the information inferred from the Jomo Kenyatta (JKIA) Nairobi office of the meteorological department, on the urban built form variables. 245

The objectives of the study included establishing the influence of the urban built form on the micro-temperature change and the identification of urban built form variables causing temperature change in Komarock Infill B Estate study site, again the study wanted to determine the influence of the of the significant urban built form variables causing temperature change and to develop design and planning strategies in view of sustainable urban built form in a temperature changing environment. It was envisaged that the findings of the study would act as a valuable tool in future related studies; solving the potential challenges posed specially by temperature change at the local level, and broadly by environment change at the global level, by seeking and devising appropriate tools and measures for achieving sustainable built forms.

6.2 SUMMARY OF FINDINGS The findings identified urban built form variability and trends which determine and have impact on the dependent variable micro-temperature change. The building variables were: building type, plot size, building orientation, building classification, building road proximity, ground coverage and plot ratio. Open space variables were: open space size, open space orientation and road proximity, hard landscape coefficient, light angle, shading ratio and open space length. The findings of the study for the building variables correlated to a 3.4 degree Celsius average micro-temperature change, the averages were 6.4 meters for the building height, 140 square metres for the plot size, 171.2 degrees for the building orientation, 40.1 metres for the width of rows of the housing, 51.9 metres for the building road proximity, 47.6 percent for the ground coverage and 65.9 percent for the plot ratio. Similarly the open space variables which correlated to a 3.7 degree Celsius average micro-temperature change, the averages were 759.4 square metres for the open space size, 62.7 percent for the hard landscape ratio, 159 degrees for the open space orientation, 81 degrees for the light angle, 61 metres for the open space road 246

proximity, 48 percent for the shading coefficient and 40 metres for the open space length. The findings, based on the coefficient and slope of the regression line, suggests that urban built form variables with positive and ascending line of regression with the micro-temperature change were: building and open space orientation, building type (height of buildings), building ground coverage, open space light angle, open space area and open space hard landscape. Urban built form variables with negative and descending line of regression were: building classification (width of rows of houses), building and open space road proximity, plot size, plot ratio, open space shading coefficient and open space length. Isotherm lines findings showed the development of steep contours (thermal ridges) nearer the main roads with temperature change readings of 6 degree Celsius, and thermal depressions around open spaces of 1 degree Celsius as heat sinks. Inter-plot orientation variability and inter-plot building classification variability findings over Komarock Infill B Estate for 30 plots sampled, suggested that orientation variability displayed a normal distribution curve with positive values of the deviation from the mean almost equal the negative values, while the building classification variability displayed a skewed curve for the two values. There seems to be a difference between the mean micro-temperature change of the orientation and building classification variables of the built form variables. Orientation seems to be affected by the diurnal and annual cyclic movement of the sun relative to its position both in altitude and in azimuth. Building classification findings suggested a negative relationship with the micro-temperature change variable, suggesting that as the width of the row houses was increased, the micro-temperature change decreased in unit values. It would seem that it was related to the wind patterns and general air flow around buildings. The predominant winds over Nairobi are the north easterlies and easterlies; these

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winds are associated with precipitation occasioned by moisture into the country from the Indian Ocean. The study produced polar curves for the building and open space orientation variable, developed predictive and remedial nomograms, and summary diagrams that addressed the relationship between buildings and open space of the urban built form and micro-temperature change, that can be used by architects and planners as guidelines for the planning design and development of structured neighborhoods such as Komarock Infill B Estate which are located in tropical upland climates in a temperature changing environment. The study reported the micro-temperature change to a minimum of 1.4 degree Celsius, maximum 7.2 degree Celsius and average 3.4 degree Celsius compared to a global standard of 1.5 to 2 degree Celsius. Through meteorological data, the study set a baseline temperature measured at average micro-temperature increase of 3.4 degree Celsius, minimum temperature of 1.4 and maximum of 7.2 degree Celsius temperature increase over a period of thirty one years (1984 – 2015). The findings of the study suggests that by placing a limit of 23 percent on the building ground coverage and 29 percent on the plot ratio, a temperature change of 1.4 degree Celsius would be realized and would meet the United Nations Climate Change Secretariat (UNCCS) recommendation of less than 2 degree Celsius. Average figures of the micro-temperature change of 3.4 degree Celsius was related to a minimum plot size of 101 square metres, 11.4 metres road proximity, 5.3 metres building height, 46 degree building orientation and 6 metres width of row housing.

6.3 FINDINGS OF HYPOTHESIS TESTING The findings of hypothesis testing of the individual building and open space variables related to the scattergram plus slope of the regression line, suggests a rejection of the null hypothesis as none of the building or open space variables had a slope of zero relative to the micro-temperature change. Seven out of the 14 urban 248

built form variables had a positive and ascending line of regression and the other seven had negative and descending line of regression. Correlation of the urban built form variables as a measure of association and strength of linear relationship between two buildings or two open space variables had neither perfect association value of one nor no association value of zero. The strongest correlation relating to building variables was between plot ratio and ground coverage, and for open space variables was between open space area and open space light angle at the significance level of 1 percent (0.01). Implicit null hypothesis using the t-statistic and p-value for the population coefficient, rejected the null hypothesis, and the alterative hypothesis was accepted for urban built form variables related to the building orientation, building classification, building type, plot size, ground coverage, open space length and open space hard landscape. The null hypothesis for the implicit t statistic was accepted and the alternative hypothesis rejected for urban built form variables related to building and open space road proximity, plot ratio, open space orientation, open space light angle, open space shading coefficient and open space area. The findings of hypothesis testing based on the significance level chosen, suggests that both the significance level of 1 percent (99% confidence level) and 5 percent (95% confidence level) of the null hypothesis were rejected in favour of the alternative hypothesis. T-test of significance hypothesis testing of the true or population regression coefficient for individual urban built form variable, suggests that all individual null hypothesis were rejected in favour of the alternative hypothesis. T-test of overall significance hypothesis testing of the slope of regression lines were simultaneously equal to zero, suggesting that at both the 5 percent (0.05) and 1 percent (0.01) significance level, the null hypothesis could be accepted and the alternative hypothesis was rejected. R-square test of measure of goodness of fit of the estimated line or plane, suggested that approximately 16 percent of the building and 33 percent of the open 249

space variables in the dependent variable micro-temperature change were explained by the seven building and seven open space explanatory variables related to the urban built form.

6.4 PHILOSOPHY STATEMENT In regard to the hypothesis testing, the findings suggested that microtemperature change had a positive relation to building and open space orientation, building type, ground coverage, open space light angle, open space area and open space hard landscape, and had a negative relation with building classification, building and road proximity, plot size, plot ratio, open space shading coefficient and open space length of the urban built form. The philosophical statement for the study was that the temperature change near the ground for any area is directly affected by the planning, design and development decisions implemented by architects and planners. Given the urban built form variables (building and open space) chosen for analysis, a decision based on one of these urban built form variables would by choice have an impact on the micro-temperature change. The findings of the study suggest that by using two or more urban built form variables, the impact on the micro-temperature change would be more pronounced. It is important to understand that the decisions made by the architects today, can have a long term implications for the future in terms of micro-temperature change and the sustainable urban built form.

6.5 LIMITATION OF FINDINGS The building and open space orientation of the urban built form variable suggested that the positive and ascending line of regression relation between the building and open space orientation and temperature change was cyclic rather than linear in nature.

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The building classification of the urban built form variable suggested that the negative and descending line of regression relation between the building classification and the temperature change was of a qualitative rather than nominal nature. The building and open space road proximity of the urban built form variable suggested that the negative and descending line of regression relation between the distance of the building to the main road and the temperature change was geographical rather than linear in nature. The building type of the urban built form variable suggested that positive and ascending line of regression relation between the height of the building and the temperature change was of a qualitative rather than nominal nature. The plot size of the urban built form variable suggested that the negative and descending line of regression relation between the plot size and the temperature change, contradicted the open space area with a positive and ascending line of regression, but was in conformity with the open space length with a negative and descending line of regression to the temperature change. In shading coefficient of the urban built form variable suggested that the negative and descending line of regression between the shading coefficient of the open space and the temperature change was related to the open space area, the density of foliage, hard landscape coefficient and open space light angle, as captured in the predictive and remedial nomograms. The open space area and light angle resulted in the strongest correlation relationship amongst open space variables at the significance level of 1 percent (0.01). The ground coverage of the urban built form variable suggested that the positive and ascending line of regression relation between the ground cover of the building and the temperature change played off with the negative and descending line of regression relation between the plot ratio of the building and the temperature change, as captured in the predictive and remedial nomograms. 251

The plot ratio also played off with the building height, suggesting that the density of buildings in an area does not have to translate into height of the buildings. Plot ratio and ground coverage resulted in the strongest correlation relationship amongst building variables at the significance level of 1 percent (0.01).

6.6 CONCLUSION The perceived observations on the phenomenon associated with microtemperature change have either been misrepresented in terms of the relationship between urban built form and micro-temperature change, or not enough research has been done to justify a valued judgment on the degree of change or its causes. As such planning and design guidelines need to be developed based on minimum distances of buildings and open spaces to the primary and secondary roads based on isotherm distribution maps. Plots should have a minimum distance to the nearest main road of 70 metres, while open spaces should have a minimum of 42.5 metres in order to achieve a 1.5 to 2 degree Celsius micro-temperature change. With the usual building, a setback of 10 to 15 metres and northwest orientation (270 degrees), would achieve 3.5 degree Celsius micro-temperature change, and would require a thermal insulation to the building. Since the climate of an area and technological capability seem to have a positive long-term correlation with the architecture of a place, schools of architecture need to incorporate lessons in the building science curricula on standards for light angles, hard landscape coefficient and open space size based on the proximity to tertiary roads. Open spaces should have a maximum light angle of 61.5 degrees, maximum hard landscape coefficient of 25.5 percent and a minimum open space of 730 square metres. Nairobi’s built environment is classified as tropical upland climate; the study indicated that the climate has a relation with urbanism, health and the development of heat islands. The study findings therefore can be used to bridge the knowledge 252

divide. Geospatial and digital technology should be used to update teaching and practice materials for architects, through the development of digital software, databanks and libraries, support of research and development in educational and allied institutions, and facilitation for the attendance and presentation of research results and findings in relevant local and international seminars. Physical planners do not adequately take into account the physical regulations outlined in planning handbooks and the relevant Acts of Parliament and Laws of Kenya. There is need therefore for to draft regulations which require urban built forms to be responsive to micro-temperature change, the practice notes for practitioners should be updated so they adhere to the limit for micro-temperature change of 1.5 to 2 degree Celsius. Earlier studies on micro-temperature change had indicated that the built form lacked manifestations related to the temperature changing environment, this study results and findings can be used to bridge the knowledge divide. There seems to be a disparity between the unplanned structures, where the majority of Kenya’s urban population living on less than a dollar per day reside, no doubt due to the demand and supply of structured neighbourhoods. Minimum standards in regard to building and open space variables must be adhered to, if the recommendations of the study finding are to have any meaningful delivery of sustainable built form. Built form in urban areas has failed to respond to the temperature changing environment by using of temperature data drawn from predominantly open ground meteorological stations in bioclimatic design, as such, architects should instead use micro-temperature change data using nomogram diagrams. In a region plagued with dwindling resources and the vagaries of climate change, designer’s and planners need to seek new ways of optimizing resource use and methods for sustainable living. Appropriate linkages should be created by practitioners, researchers and the trainers of architects. Non-structured neighbourhoods and structures lacked meaningful and sustainable built form; this indicates that the design and planning strategies are 253

missing out in considering the impact of building form on temperature change. The study results and findings can be used by architects to understand and measure the impact of built form on micro-temperature change in urban areas, which is very different in the case of rural areas, and demands that they invest in sustainable built form. Digital and analogue technology must be harnessed in the training and practice of architecture. The use of predictive and remedial nomogram diagrams in the study crossed the boundaries of established disciplines; this suggests that the training of architects and environment scientists should incorporate a broad background in quite diverse disciplines in order to facilitate comprehension of the many crosscutting relationships that exist; this will promote a more rational interpretation of microclimatic concepts as they relate to both natural and man-modified environments. Further, if the structured neighbouhoods are to meet the global accepted temperature change of 2 degree Celsius agreed in the Paris 2015 Convention, then, the findings of both the predictive and remedial building nomogram, and the scattergram, on a threshold for each of the urban built form variables must be adhered. The findings had suggested that: minimum plot size of 108 square metres, should have a building orientation of between 46 to 136 degrees of the North (i.e. N-E direction), plots should have a minimum distance to the nearest main road of 70 metres, maximum building height of 5.3 metres, minimum width of the rows for housing of 72 metres, maximum ground coverage of the plot of 37 percent and maximum plot ratio of 34 percent.

6.7 IMPLICATION OF THE STUDY IN PRACTICE AND THEORY The study has revealed that the temperature change near the ground for any area is directly affected by the planning, design and development decisions implemented by architects and planners. Inferences drawn from the study of the relationship between micro-temperature change and urban built form especially of 254

structured neighbourhoods in a tropical upland climate may be expected to apply in similar circumstances. Gaps in the knowledge of the relationship between microtemperature change and urban built form, needs to be filled integrating the findings into theory courses in the architectural schools, studio programs and eventually into practice through the continuous professional development (CPD) conducted on an annual basis by the architectural associations of the country and the board of registration of architects. The study determined that there are building variables which correlate to average micro-temperature change. There is need to rethink climatic design and sustainable building form in the upland climate, and the planning of structured neighbourhoods, given the impact of vegetation cover in the overall reduction of air temperatures near the ground, densification of urban areas based on the limits of ground coverage and plot ratios. The findings on the coefficient and slope of the regression line of building and open space variables would suggest that architects and academics take on an inter-disciplinary approach to theory and practice in dealing with societal, technological and environmental issues; new opportunities will be available in the value added chain in dealing with the challenges and risks of temperature change. Architects and academics will need to use the strengths of the results and findings in order to deal with such new opportunities. The micro-temperature change variability and trend that was reveled through use of the isotherm distribution maps may have an impact in practice and theory. In stating the conditions of the study which may limit the extent of legitimate generalizations of the inferences, it is possible to add to the current body of knowledge by presenting the findings in the preparation of teaching and practice aids, in the form of books, and magazines and other tools of the trade in relation to the theory and practice of architecture. The inter-plot variability for plots sampled can have the desired effect if included as findings in search engines and digital libraries that focus on issues of 255

urban heat islands in tropical upland climates, temperature change at the microlevel, urban and rural temperature ranges and development of building and planning practice notes for students and practitioners. Further, interrogating and application of the research methods by developing of architectural friendly software, and applications for use in devices such as smart phones and other digital media would add value. The implication of the building and open space variables relationship with the micro-temperature change variable can be manifested by the results of the micro-temperature change minimum, maximum and average recordings; if these are compared with global standards and baseline temperature settings based on local meteorological station data, they will create future opportunities for research and development. Stating the relevant questions that still remain unanswered and the new questions raised by the study along with suggestions for the kind of research that would provide answers, can be achieved if the results and findings of the study guide evaluation and the decision making process of academics and practitioners. The suggestion of placing limits on the building and open space variables should assist academics and practitioners to develop planning and design guidelines for structural neighbourhoods such as Komarock Infill B Estate in terms of sustainable development in the different regions of Kenya. Kenya is a signatory to international conventions, just like the United Kingdom where such policies as the Green Deal whereby households in the United Kingdom were given a loan to make energy efficient improvements to their properties and were expected to make repayments using money saved from the lower energy bill can be applied here. The study used polar curves to determine the variables which contribute to urban built form and micro-temperature change. The study also used predictive and remedial nomogram and summary diagrams to determine the relationships between various variables. The results then can be used by architects and planners as guidelines for the planning design and development of structured neighbourhoods such as Komarock Infill B Estate, for tropical upland climates in a temperature 256

changing environment. Further, the results can assist in dealing with issues related to retrofitting of existing buildings and to diagnose and take remedial action on sick building syndrome (SBS).

6.8 RECOMMENDATIONS Arising from the problem statement and the research objectives, the study made several recommendations with a view to enhancing the appropriate and sustainable built form in a temperature changing environment, building variables which correlated to average micro-temperature change results, related to exposing future problems for further research and introduction of more questions based on the time limit of the current study. Planning of structured neighbourhoods and the design of urban built forms should involve the use and application of the building and open space prediction and remedial action design temperatures, polar curves, prediction and remedial nomogram, and summary tables. Recommendations based on issues related to coefficient and slope of the regression line, the summary of findings on micro-temperature change variability and trend by use of the isotherm distribution maps, related to exposing future unsolved problems associated to the area of study. Arising from the summary of findings of the study, it is suggested that the application of a bivariate approach to design where say two built form variables are used would be appropriate at the preliminary design stage, and multivariate approach where more than two built form variables would be more appropriate for a detailed design of sustainable built form in structured neighbourhoods in a temperature changing environment. Recommendations based on issues related to inter-plot variability for plots sampled, building and open space variable relationship with the micro-temperature change variable, results of the micro-temperature change minimum, maximum and average recordings compared to global standards, baseline temperature settings based on local meteorological station data, related to suggestions about improvements on generally the study undertakings, results and findings of the study 257

and pointing out areas that deserve further investigations. Arising from the design and planning of urban built form and based on data from the relevant meteorological data, there are recommendations that one must take into account the temperature changing environment if one wants to improve and make relevant the climatic design process of creating mature architecture based sound bioclimatic principles. Recommendations are also based on issues related to the suggestion of placing limits on the building and open space variables, and the related to aspect of firm appreciation of the study with sufficient thought on the implication to both the confines of the research topic and related fields. Arising from the appreciation that the design and planning process is dynamic and the only constant in the temperature environment is change, the structured neighbourhoods of the early eighties are being transformed into mid-rise flats and inevitably will be replaced with high-rise apartments on comprehensive developments, in large green areas, whereby the plot ratio would go up, but the ground coverage would be reduced. Recommendations on issues related to urban built form and microtemperature change relation suggest the use of polar curves for the building and open space orientation variable, predictive and remedial nomogram, summary diagrams for building and open space variable of the built form variables and related to aspect of study foresightedness and creativity. Arising from the study findings is the suggestion that black road surfaces are to be reduced; road proximity to these mitigated through appropriate green belt buffers for both thermal and noise control measures. Orientation and shading strategies need to be put in place especially for the tropical upland climate, use of appropriate light angles, shading coefficient and hard landscape measures in order to achieve reduced microtemperature changes.

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6.9 SUGGESTED AREAS FOR FURTHER RESEARCH There is a need for further research and adequate theoretical development concerning the relationship between climatic design and architecture, especially in regards to the limits set by adhering to the three research objectives of the study. Further research, related to the urban built form variables yet to be identified, and which cause temperature change in structured neighbourhoods may include the following: i.

Research in other manifestations and recordings of temperature as this study was limited to the study of air temperature, while other manifestations are radiate, surface and ground temperatures,

ii.

Research in other thermal indices as this study was limited to the study of air temperature, and a need exists to study relative humidity, air flow and other thermal controls,

iii.

Research in expanding the geographical limits to exceed the study scope which was limited to Komarock Infill B Estate, whereby 30 plots out of the 240 plot population were sampled, data collected, data processed, results and findings achieved, and

iv.

Research to include other climatic regions as the study scope was limited to tropical upland climate. Kenya also has within its geographical boundaries, warm humid climate, hot dry climate and Lakeland climate.

Further research related to undetermined urban built form variables in contribution to the temperature change includes the following: i.

Research on other

built

forms other than the structured

neighbourhood limit, ii.

Study the scope limited to building types that were observed at Komarock Infill B Estate which were the Villa with a height limit of 5.3 metres, Maisonette with a height limit of 6.4 metres and change of user with height of 7.7 metres, and building classification which 259

was the detached house with width of rows of 6 metres, semidetached house with 40.1 metres and row housing with 153 metres. Thus the need exists in the study of building types and classifications exceeding these height and width limitation respectively, and iii.

Study scope limited to plot sizes of between 101 and 422.3 square meters, road proximity of between 11.4 and 98.2 metres, ground coverage of between 23 and 86 percent and plot ratios of between 29 and 162 percent that existed at the study site and thus providing an opportunity for further research of undetermined building variables outside these ranges.

Further research related to the lack of development of design and planning strategies in view of sustainable urban built form in a temperature changing environment includes the following: i.

Research in other levels of temperature analysis as this study was limited to micro-temperature change near the ground which required the measurement of air temperature at 1.5 metres from the surface. There is a need to investigate meso-temperature and macrotemperature changes, and also artificial, hybrid and intelligent systems of climatic controls,

ii.

Research on other temperature changing variables as the study scope was limited to urban built form and need exists on study of other contexts and settings such as rural and peri-urban built forms,

iii.

Research on other climatic factors as the study scope was limited to temperature and need exists in the study of other climatic factors, and

iv.

Research on other environmental factors as the study scope was limited to climate and need exists in the study of other factors contributing to sustainable built form.

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271

APPENDICES APPENDIX 1 STANDARD TABULATED OBSERVATION SHEETS Table 1.1: List of micro-temperature change, urban built form and other variables identified as significant in review of related literature. Source: Developed from review of related literature (2016). Item

Authors (Year)

1

Geiger (1975)

2

Meffert (1981)

3

Taha (1997)

4

McDonald (2003)

5

Shulkburg (2007)

Micro-temperature change and urban built form variables identified as significant in review of related literature Temperature change, building type and size, open space size, shading coefficient, commercial activities, alteration of the heat and water budgets, atmospheric pollution, green areas and hard landscape. Temperature change, sol-air temperature, time of day and week, urban development, urban heat islands, city growth, population size, re-establish precipitation runoff, time-lag of materials, area of hard surfaces, vegetation, ventilated shade, evapotranspiration and cooling, low heat capacity materials, black or light surfaces, topography, climatic conditions, seasons and air population. Air temperature, urban-rural temperature differences, heat islands, urban climate change, weather conditions, urban thermo-physical, geometrical characteristics, anthropogenic heating from mobile and stationary sources, anthropometric moisture, aerial heat sources, urban pollutant concentrations, city area, surface albedo of roofing and walking surface materials, building classification, tree canopy and evapotranspiration from vegetation. Temperature, solar movement (solar altitude and azimuth), site orientation and slope, existing obstructions on site, overshadowing from obstructions outside the site, grouping and orientation of buildings, road layout and services distribution, glazing types and façade design, nature of internal spaces, insulation capacity and resistivity of materials. Global surface temperature, average air temperature, atmospheric concentrations of carbon dioxide and methane gases. 272

Table 1.1: Continues. 6

7

8

9

10

Firth and Wright Average temperature, external air temperature, (2008) building classification (purpose built flat and end terraces) and seasons. Commission for the Environmental legislation. Implementation of the Constitution (2010) Kane et al. (2011) Air temperatures, internal air temperature, environmental legislation impact, environmental compliance incentives, building classification (detached, flat, semi-detached, mid-terrace and endterrace) and occupancy pattern. Montello and Human activity, air land and water population, Sutton (2013) wilderness habitats, biodiversity loss and environmental legislation. Mumina and Temperature change, surface temperature, land Mundia (2014) surface temperature, storage of heat, urbanization process and rate, urban built-up area, vegetation cover, moisture content, land local cover (build-up and bare grounds areas), replacement of natural surfaces, artificial surfaces (roads, building and anthropogenic), urban sprawl, ecosystem imbalances, urban green spaces, population growth, solar radiation, absorption, evaporation rates, wind turbulence and heat island phenomenon.

Table 1.2: List of micro-temperature change, urban built form and other variables determined as influential in review of related literature. Source: Developed from review of related literature (2016). Item

1 2 3

4

Authors (Year)

Micro-temperature change and urban built form variables determined as influential in review of related literature Lynch (1960) Building attributes (nodes, edge, landmark, districts, paths and axis). Olgyay and Olgyay Societal, technological, environmental, climatic and (1963) bioclimatic variables. East African Regional meteorological data. Meteorological Department (1970) Koenigsberger et Macro-temperature, meso-temperature, 273

Table 1.2: Continues. al. (1973)

5

Meffert (1981)

6

Kenya Meteorological Department (1984) Oke (1988)

7

8 9 10

11 12

13 14 15 16

17

micro-temperature, internal room temperature, outside air temperature, design temperature, total heat balance, sol-air temperature, radiation intensity, absorbance of the surface, outside surface conductance, heat insulation quality of materials. Macro-temperature, meso-temperature, microtemperature, radiative excess temperature, incident radiation, absorption factor and surface conductance. National meteorological data.

Microclimate, street climate, climatic variables and contexts, design objectives, air flow, natural ventilation, orientation, shade and shelter, dispersion of pollutants, urban warmth, solar access, street and urban geometry, urban planning and design, street dimensions, building density and height, width of open spaces, geometric unit of open space, sky view and light angles. Rosenlund (1995) Climatic design. Muneer (2000) Digital methods of simulation. Nikolopoulou, Microclimatic characteristics, outdoor urban spaces Baker and Steemers and thermal environment. (2001) Capeluto (2002) Climatic design. McDonald (2003) Temperature change, eco-balance, deforestation, urbanization, desertification, greenhouse gases, manmade materials, environmental interventions, pollution, production transportation and technological systems. Lam (2004) Climatic design. Gitari (2006) Aerosols. Shuckburg (2007) Aerosols, land use change and historical perspectives. Ebrahim (2010, Upland climate, retrofitting and transformations of 2011, 2011a) built forms, bioclimatic regional classification, microtemperature change, software use and baseline temperature. Szokolay (2011) Temperature measurements, meteorological data, microclimatic controls, on-site measurements, climate, site climate (topography, slope, orientation, exposure, elevation, hills or valleys, ground surface, 274

Table 1.2: Continues.

18

19

20

natural or man-made), reflective (albedo, permeability, soil temperature, paved areas, vegetation), three dimensional objects (trees, tree belts, fences, walls), building influences (wind, cast shadows on site, subdividing the site) and climatic niches. Rose, Horrison and Air temperature, urban built forms (dense, compact, Venkatachalam mid-rise, dispersed and low-rise), urban and street (2011) geometry, density of buildings, height to width ratio, sky view factor, green cover, vegetation, percentage of urban built-up areas, daytime and nighttime temperature, development legislations and controls, and physiological equivalent temperature. United Nations Long-term climate change goal and upper limit for Framework global warming. Convention on Climate Change (2015) United Nations Paris climate change agreement. Climate Change Secretariat (2015a)

Table 1.3: List of micro-temperature change, urban built form and other variables used in developing design and planning strategies in review of related literature. Source: Developed from review of related literature (2016). Item

1 2 3

4

5

Authors (Year)

Micro-temperature change and urban built form variables used in developing design and planning strategies in review of related literature Olgyay and Olgyay Bioclimatic variables and air temperature. (1963) Givoni (1969) Air temperature. East African Regional meteorological data. Meteorological Department (1970) Koenigsberger et Outside temperature, average temperature, al. (1973) meteorological data, radiant temperature, surface temperature, bioclimatic standards, humidity, air flow, solar radiation, human response, and sensation, thermal comfort and standards. Kenya National meteorological data. 275

Table 1.3: Continued.

6 7

Meteorological Department (1984) Baker (1987) Gakuru (2006)

8

Shuckburg (2007)

9 10

Copenhagen (2009) Laws of Kenya (2009) Laws of Kenya (2010) Singh and Singh (2010)

11 12

13

Choi, Lee and Byun (2012)

14

Generally

Air temperature. National environmental and climate change aspirations. Climate change predictions, greenhouse gases emissions, temperature, micro-temperature, external air temperature and outside temperature. Climate change agreements. Draft amended building bylaws and draft physical planning act. National environmental and climate change aspirations. Building attitudes, building type, building classification, orientation, plot size and road proximity. Temperature change, urban heat distribution, concentric heat island pattern, densely built-up commercial, industrial neighbouring sectors, seasons, urban green spaces and urban central area. Air temperature, global air temperature, maximum temperature, minimum temperature, average temperature, isotherm distribution, urban built form physical characteristics (volume, perimeter, height, openings, texture, materials, specifications), modifications, transformations, typical plans, structured neighbourhoods, urban planning regulations (plot ratio, ground coverage, size of streets, density ratios, change of user permits), design attributes and attitudes, internal and external spaces, in-between spaces and temperature index.

276

Table 1.4: Matrix of the studied phenomena, structural dimension and empirical indicators of the phenomenon under study into the relationship between microtemperature change and urban built form for Komarock Infill B Estate. Source: Developed from review of related literature (2016). Studied phenomena

Structural dimension Empirical indicator inferred by what? (surrogate) captured by what? Micro-temperature change Temperature Temperature change, temperature change for sample, outside air temperature, inside air temperature, average temperature, minimum temperature and maximum temperature. Baseline temperature Baseline temperature and meteorological data. Urban built form Buildings Building attributes, (Buildings) building attitudes, building planning regulations, building regulations and building elemental. Building attributes Node, edge, district, landmark, paths and axis. Building attitudes Building type, building classification, orientation, plot size and road proximity. Building planning Ground coverage (%) and regulations plot ratio (%). Building elementals Length, mass, time, orientation, area, volume and density. Building type Maisonette, Villa or other uses (building height: M). Building classification Change of user plot, detached house plot, semidetached house plot and row houses plot (Building width: M). 277

Table 1.4: Continued. Orientation

North to south (N-S) orientation plot, east to west (E-W) orientation plot and change of user plot (Degrees). Plot size Change of user plot, small sized plot, medium sized plot and large sized plot (Square metres). Road proximity Corner plot internally located, large plot near main road and basic plot (Metres). Urban built form (Open Open spaces Open space planning spaces) regulations, open space elemental and open space attitudes. Open space planning Hard landscape ratio (%), regulations light angle (Degrees) and shading coefficient (%). Open space elemental Open space length (Metres). Open space attitudes Open space size (M2), orientation (Degrees) and road proximity (M).

Table 1.5: Standard tabulated dependent variable (micro-temperature change) and their description. Source: Developed from research methods (2010). Dependent variable terminologies

Symbol

Unit

Description

Temperature Change

ΔT

o

Temperature variation compared to the baseline temperatures.

o

Temperature variation for sample 1 compared to the baseline temperature for the month when data was collected.

Temperature ΔT1 change for sample 1

C C

278

Table 1.5: Continued. TB

o

Baseline temperature calculated for the region for the month when data was collected.

Outside Air To Temperature

o

Measured temperature courtyard to the sample.

Inside Air Ti Temperature

o

Measured temperature from the living room of the sample.

Average Temperatures

TAve

o

Averaged temperatures over the period of measurement.

Minimum Temperatures

TMin

o

Temperature measurements taken as the extreme minimum data from the average temperature.

Maximum Temperatures

TMax

o

Temperature measurements taken as the extreme maximum data from the average temperature.

Baseline temperature

C

C C C C

C

from

the

entry

Table 1.6: Standard data logger journal for a batch of data collected. Source: Developed from research methods (2016). Item

Description

Symbol

1

Average Logger 1 Plot 1 Lounge

L1

2

Average Logger 2 Plot 1 Garden

L2

3

Average Logger 3 Plot 2 Lounge

L3

4

Average Logger 4 Plot 2 Garden

L4

5

Average Logger 5 Open Space

L5

6

Average Logger Consolidation

LCon

7

Consolidated Batch

CBatch

Table 1.7: Standard weekly batching and tabulation of data logger downloads. Source: Developed from research methods (2010). Data No:

Logger Month:

Week No: 279

Table 1.7: Continued. Week No:

Time: Hours:

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Total Readings (TTotal):

o

Average Readings (TAve):

o

Week No:

Time: Hours:

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

0.00

01.00

→

→

→ →

07.00

08.00

→

→

→ 12.00

C C C C C C C

C C

C C C C C C

280

06.00

Table 1.7: Continues. o

Date:

Readings (To):

C

Total Readings (TTotal):

o

Average Readings (TAve):

o

Week No:

Time: Hours:

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Total Readings (TTotal):

o

Average Readings (TAve):

o

Week No:

Time: Hours:

Date:

Readings (To):

o

Date:

Readings (To):

o

C C

13.00

14.00

→

→

→ 18.00

19.00

20.00

→

→

→ 24.00

C C C C C C C

C C

C C

281

Table 1.7: Continues. Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Date:

Readings (To):

o

Total Readings (TTotal):

o

Average Readings (TAve):

o

C C C C C

C C

Note: TAve = TTotal ÷ 7 Table 1.8: Standard tabulated data logged temperatures for sample logger for a week. Source: Developed from research methods (2016). Time (LMT) (Hours)

Month/Year/Temperature (oC) Day 1

Day 2

Day 3

Day Day 4 5

0.00 1.00 2.00 3.00 4.00 5.00 6.00 282

Day Day Day Weekly Rounding 5 6 7 Average (Sample number)

Table 1.8: Continues. 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 20.00 21.00 22.00 23.00 24.00 Average:

Table 1.9: Standard tabulated average data logged temperatures for sample logger for the month when data was collected. Source: Developed from research methods (2016). Time (LMT) (Hours)

Sample plot temperature TS (oC)

0.00

Remarks T S 0.00

1.00 2.00 3.00 4.00 283

Table 1.9: Continues. 5.00 6.00 7.00

TS Min

8.00 9.00 10.00 11.00 12.00 13.00

TS Max

14.00 15.00 16.00 17.00 18.00 19.00 20.00 21.00 22.00 23.00 24.00

TS 24.00

Average

TS Ave

Table 1.10: Standard tabulated average monthly temperature data for Jomo Kenyatta International Airport Embakasi for the period 1959 to 1980 used as baseline temperature. Source: Developed from research methods (2016). Month

Time (LMT) January

Temperature Minimum (TMin) (oC) 5.00

Temperature Dry Bulb o (T9.00) ( C) 9.00

Temperature Maximum (TMax) (oC) 13.00

Temperature Remarks Dry Bulb o (T15.00) ( C) 15.00

11.9

18.3

26.6

25.5

284

Table 1.10: Continued. February March

12.4 13.2

18.6 18.6

27.7 27.6

26.6 26.4

April May June

14.5 13.5 11.5

18.2 17.4 15.7

26.0 24.6 23.6

24.7 23.4 22.5

July

10.7

14.8

22.5

21.4

August September October November December

10.8 11.0 12.6 13.3 12.7

15.0 16.2 18.0 17.7 18.1

23.1 25.6 26.7 25.2 25.5

21.9 24.4 25.5 23.8 24.4

Hottest Month

Study Month Coldest Month

Table 1.11: Standard tabulated baseline temperatures for the month when data was collected for the sample logger. Source: Developed from research methods (2016). Time (LMT) (Hours)

Baseline temperature (TB) (oC)

0.00

Remarks TB 0.00

1.00 2.00 3.00 4.00 5.00

TB Min

6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 285

Table 1.11: Continues. 15.00

TB 15.00

16.00 17.00 18.00 19.00 20.00 21.00 22.00 23.00 24.00

TB 24.00

Average:

TB Ave

Table 1.12: Standard tabulated resultant data logged for sample logger for the period of data collected and tabulated baseline temperatures for the month of data collected. Source: Developed from research methods (2016). Time (LMT) (Hours)

Sample logger temperature (TS) (oC)

Baseline Micro-temperature Remarks temperature Change (TS – TB) month (TB) (oC) (oC)

0.00

TB 0.00 and TS 0.00

1.00 2.00 3.00 4.00 5.00

TB Min

6.00 7.00

TS Min

8.00 9.00

TB 9.00 286

Table 1.12: Continues. 10.00 11.00 12.00 13.00

TB Max

14.00 15.00

TB 15.00

16.00 17.00 18.00 19.00 20.00 21.00 22.00 23.00 24.00

TB 24.00 and TS 24.00

Average:

TAve

Table 1.13: Standard tabulated resultant micro-temperature change for the plots for the period of data collected. Source: Developed from research methods (2016). Item

Plot number

Micro-temperature change (oC)

Remarks

1 2 3 4 5

To Min

6 7 8

To Max 287

Table 1.13: Continued. 9 10 Average:

To Ave

Table 1.14: Standard tabulated planning regulations, variables, description and explanation. Source: Developed from research methods (2010). Description

Symbol

Unit

Explanation

Plot

P

Hectare

Legal entity of ownership or lease from government/NCC/Railways etc. (1 Hectare = 10 000 M2)

Plot width

WP

M

Horizontal short dimension of plot defined by title deed.

Plot length

LP

M

Long dimension of plot. Legally no restriction through title, but governed by light angle rights of neighbours through common law and Plot Ratio (PR).

Ground cover

GC

Ratio

Ratio of built-up area to overall plot size and defined by NCC Byelaws for designated areas under its jurisdiction.

Plot ratio

PR

Ratio

Ratio of total built-up area to size of Plot. Governed by stipulated ratios for designated areas.

Car park CP provision

Number/ Number of car park provisions per square Sq.M meter or per household or per flat etc. For Komarock Estate it stands as 1.5 to 2 cars/household and the reality on the ground varies from this provision.

Open spaces

OS

%

Allowance for playing grounds, amenities and infrastructure and excludes provision within the plot.

Road reserve

RR

M

Width of reserve depends on road regime, classification and provisions under the act for adoptive standards.

288

Table 1.14: Continues. Building line

BL

M

Building set-back, primarily next to road for expansion and provisions of services.

Building set-back

BS

M

Similar to BL along other sides of the plot to allow for access or lighting, ventilation or windows.

Service lanes

SL

M

Width of reserve depends on road regime, classification and provisions under the act for adoptive standards.

Entry

E

M

Main or service access or entry provisions.

Table 1.15: Standard tabulated building regulations, variables, description and explanation. Source: Developed from research methods (2010). Description

Symbol

Unit

Explanation

Absorption of a surface

Ratio

Absorption Coefficient: a + t + r = 1. As a → 1, r → 0. Thus good absorber and bad reflector.

Emissivity of e surface

Ratio

Emittance gives an indication of night reradiation or salient radiator. E.g. Black Body Emitter.

Thermal conductivity

c

W/M oC

Heat flow rate through unit area of unit thickness of substance with unit temperature difference between the two faces.

Thermal transmittance

C

W/M2 oC

Heat flow rate through unit area of body with unit difference in temperature of air on the two sides.

Table 1.16: Standard tabulated building elemental, variables, description and explanation. Source: Developed from research methods (2010). Description Symbol

Unit

Explanation

Length

L

M

Length of different elements of the building or other unit of measurement.

Mass

m

Kg

Weight of objects or elements or units for measurement. 289

Table 1.16: Continued. Time

T

Hours (H)

Relative to Local Mean Time (LMT) or Greenwich Mean Time (GMT).

Orientation

O

Degree

In a clockwise manner from the North (0oN) (Angle).

Area

A

Sq.M

Related to size of building elements (M2).

Volume

V

Cu.M

Related to capacity of building units or constructions (M3).

Density

D

Kg/M3

Density of materials or constructions.

Table 1.17: Standard tabulated built form building attribute, variables, description and explanation. Source: Developed from research methods (2016). Description

Symbol

Unit

Explanation

District (D)

XA

Plot located in district 1 or district 2 or district 3.

Nodes (N)

XB

Open ground.

Edges (E)

XC

Corner, internal or edge plot.

Landmarks (L)

XD

Schools or shops plot.

Paths (P) and XE Roads (R)

Paths or roads.

Table 1.18: Standard tabulated built form building attitude variables, description and explanation. Source: Developed from research methods (2016). Description

Symbol

Unit

Explanation

Building type (BT)

X1

M

Building height (BH): Maisonette (M), Villa (V) or other plot usage.

M2

Plot area (PA): Small (S), medium (M) or large sized plot.

Degrees

Orientation (O): Measured as degree from the North in a clockwise fashion – North to South (N-S) or East to West (E-W) orientation.

Plot (PS)

size X2

Building orientation (O)

X3

290

Table 1.18: Continued. Road proximity (RP)

X4

M

Distance from main or side road: Corner internal plot, large plot near road or basic plot.

Building X5 classification (BC)

M

Row housing – building width: Detached house (DH), semi-detached house (SH) or row housing (RH).

Ground coverage (GC)

%

Ratio of building plinth divided by the plot size given as a percentage.

%

Ratio of total built-up area divided by the plot size given as a percentage

Plot (PR)

X9

ratio X15

Table 1.19: Standard tabulated built form open space attitude variables, description and explanation. Source: Developed from research methods (2016). Description

Symbol

Unit

Explanation

Open space X28 size

M2

Open space area: Small, medium or large sized open space.

Hard landscape ratio

%

Percentage of hard landscape to open space area.

Open space X33 orientation

Degrees

Orientation: Measured as degree from the North in a clockwise fashion – North to South (N-S) or East to West (E-W) orientation.

Light angle

X34

Degrees

Measured in degree from the vertical in a clockwise fashion from the base of the building or wall.

Road proximity

X35

M

Distance from main or side road.

Shading coefficient

X32

%

Percentage of shaded area to open space area.

Open space X36 length

M

Measured as longest side of open space or road or path.

X30

291

Table 1.20: Standard tabulated digitized observation sheet showing details of batch data collection. Source: Developed from research methods (2010). Item

Description

Remarks

1

GENERAL INFORMATION:

Komarock Infill B Estate, Nairobi.

2

SAMPLE NUMBER:

REFERENCE Sample:

CLUSTER DETAILS: SAMPLE NAME:

Plot No.

FLOOR NUMBER:

Living Room or Garden Area (Ground Floor).

ORIENTATION: 3

SAMPLE NUMBER:

REFERENCE Sample:

CLUSTER No: SAMPLE NAME:

Plot No.

FLOOR NUMBER:

Living Room or Garden Area (Ground Floor).

ORIENTATION: 4

SAMPLE NUMBER:

REFERENCE Sample:

CLUSTER No: SAMPLE NAME:

Open space No.

FLOOR NUMBER:

Ground floor

ORIENTATION:

292

Table 1.21: Standard tabulated digitized observation sheet showing information to be entered into the query workbook. Source: Developed from research methods (2010). Item

Description

Details

Remarks

1

General Information:

Komarock Infill Estate, Nairobi

2

Sample Number:

3

Sample Name:

Cluster No:

4

Floor Number:

Living Room, Ground Floor

5

Orientation:

6

Technological Data:

Reference Sample: Plot No:

Building Length (L):

7

Width Room (W):

8

Height Room (H): Schedule Materials:

9

B

Concrete Walling:

of Block Height (Wall) (Hr):

10

Transmittance (Ur):

11

Absorbance Surface (a): Surface (Fo):

(Wall) of

Wall

Conductance

Window with Single Height (Glass) (Hg): Glazing:

Societal Data:

Transmittance (Ug):

(Glass)

Solar Gain (Glass) (Q)

Factor

Design Indoor Temperature (Ti):

293

Table 1.21: Continues. Number of Air Changes per Hour: Occupancy Pattern:

Unit Occupancy/Day: Number of Occupants (Nio): Heat Rate per Occupant (HRo): Number of Bulbs (Nie):

Electric

Heat Rate per Electric Bulb (HRb): Climatic Data:

Design Outdoor Temperature (To): Incident Radiation (I):

Geographical Positioning:

Baseline Climate:

Station Name:

Nairobi Jomo Kenyatta Int. Airport (JKIA) Met Station,

Station No:

91.36/168,

Latitude:

01.19S,

Longitude:

36.55E,

Altitude:

1624 Metres;

Temperature (Input):

Month:

Table 1.22: Standard tabulated digitized observation sheet showing information displayed in the consolidated summary sheet for plot samples. Source: Developed from research methods (2016). Measure of Tendency Item Description

Unit Plot No.

Central Measure of Dispersion

Mean Median Mode Minimum Maximum Range

1

Urban Built Form Building Analysis:

2

Plot No.

No. 294

Table 1.22: Continued. 3

District

4

Node

5

Edge

6

Landmark

7

Path & Road

8

Building Type

M

9

Plot Size

M2

10

Orientation

Deg.

11

Road Proximity

M

12

Building M Classification

13

Ground Coverage

%

14

Plot Ratio

%

15

Micro-temperature Change Analysis: Month:

16

To

o

17

Log 2 Garden (L2)

o

18

TΔ = L2 - To

o

19

Y9

%

C C C

Table 1.23: Standard tabulated digitized observation sheet showing information displayed in the consolidated summary sheet for open space samples. Source: Developed from research methods (2016). Measure of Tendency Item Description

1

Central Measure of Dispersion

Unit Open Mean Median Mode Minimum Maximum Range Space No.

Urban Built Form Open Space Analysis: 295

Table 1.23: Continued. 2

Open Space No. No.

3

Open size

4

Hard landscape ratio

%

5

Orientation

Deg.

6

Light angle

Deg.

7

Road proximity

M

8

Shading coefficient

%

9

Open space M length

10

Micro-temperature Change Analysis: Month:

11

To

o

12

Log 5 Open Space (L5)

o

13

TΔ = L5 - To

o

14

Y9

%

space M2

C C C

Table 1.24: Showing list of advantages and disadvantages of using longitudinal research design in the study of relationship between micro-temperature change and urban built forms. Source: Developed from research methods (2016). Item 1

2

Advantages Longitudinal data allow the analysis of duration of a particular phenomenon.

Disadvantages Longitudinal design, data collection method may change over time. Longitudinal design assumes present trends will continue unchanged. Longitudinal design enables survey Maintaining the integrity of the researchers to get close to the kinds original sample can be difficult over of causal explanations usually an extended period of time. attainable only with experiments. Longitudinal design takes a long 296

Table 1.24: Continues. 3

4

Longitudinal design permits the measurement of differences or change in a variable from one period to another (i.e. the description of patterns over time). Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.

period of time to gather results. It can be difficult to show more than one variable at a time. Longitudinal design needs a large sample size and accurate sampling to reach representativeness. Longitudinal design often needs qualitative research to explain fluctuations in the data.

Table 1.25: List of variables assessed, variable operation model and data needs for the study related to the micro-temperature change variable. Source: Developed from research methods (2016). Item 1

2

3

4

Variables assessed

Variable operation Data needs model Micro-temperature temperature Ya Ave = ΔTa Ave = Ta Average change for plot a (Ya change for plot a (ΔTa Ave: Ave – TB Ave o o C), average temperature Ave: C) for plot a (Ta Ave: oC) and average baseline temperature for the month when temperature was data logged for plot a (Ta o Ave: C). Average temperature T Sum of the data logged a Ave = ƩTa ÷ n1 o for plot a (Ta Ave: C) temperature for plot a (ƩTa: oC) and total number of temperature data points (na: No). Average baseline T of baseline B Ave = ƩTB ÷ nB Sum temperature (TB Ave: temperature for the month o C) when temperature was data logged for plot a (ƩTB: oC) and total number of baseline temperature data points (nB: No). Sum of the data logged temperature for plot a (ƩTa: oC)

Sum of the data ƩTa logged temperature for plot a (oC) 297

Table 1.25: Continues. 5

6

7

8

9

Sum of baseline ƩTB temperature for the month when temperature was data logged for plot a (oC) Total number of na temperature data points (No) Total number of nB baseline temperature data points (No) Minimum micro- YMin = Ta Min temperature change and minimum temperature (oC)

Sum of baseline temperature for the month when temperature was data logged for plot a (ƩTB: oC) Total number of temperature data points (na: No). Total number of baseline temperature data points (nB: No). Minimum microtemperature change (YMin: o C) and minimum temperature (Ta Min: oC)

Maximum micro- YMax = Ta Max temperature change and maximum temperature (oC)

Maximum microtemperature change (YMax: o C) and maximum temperature (Ta Max: oC)

Table 1.26: List of variables assessed, variable operation model and data needs for the study related to plot attributes. Source: Developed from research methods (2016). Item

Variables assessed

Variable model

1

District

XA

2

Nodes

XB

3

Edges

XC

4

Landmarks

XD

5

Paths and roads

XE

operation Data needs District (district 1, 2 or 3: surveys) Nodes (open ground: surveys). Edges (centre, edge or corner plot: survey). Landmarks (schools or shops: survey). Paths or roads (survey).

298

Table 1.27: List of variables assessed, variable operation model and data needs for the study related to the urban built form variable (building surrogates). Source: Developed from research methods (2016). Item

Variables assessed

Variable model

1

Building type (X1: M)

X1

2

Plot size (X2: M2)

X2 = WP x LP

3

Building orientation X 3 (X3: degrees)

Building orientation (NS or E-W plot: measurement magnetic north compass).

4

Building road X 4 proximity (X4: M)

Building road proximity (corner internal plot, large plot near road or basic plot: architectural drawings).

5

Building classification X 5 (X5: M)

Building classification (detached house, semidetached house or row of housing: observations, Google maps, surveys, photographs and measurements).

6

Ground coverage (X9: X = A ÷ X x 100 9 1 2 %)

Ground coverage (observations, Google maps, surveys, photographs and measurements).

299

operation Data needs Building type (maisonette, villa or other plot usage: observations, photographs and surveys) and building heights. Plot size (small plot size, medium plot size or large plot size: architectural drawings), width (WP: M) and length (LP: M) of plot.

Table 1.27: Continues. 7

8 9

Plot ratio (X15: %)

Plot ratio (observations, Google maps, surveys, photographs and measurements). Building area (AB: A = A + A + A ..+ Calculations. B 1 2 3 M2) A∞ Building floor 1 area A = (W x L ) Calculations. 1 1 1 2 (A1: M ) X15 = AB ÷ X2 x 100

10

Building heights (H: H M)

11

Building width (W: W M)

12

Building length (L: M) L

13

Plot width (WP: M)

WP

14

Plot length (LP: M)

LP

Height of building was measured from finished floor level of the building to the highest point of the roof (measurements, architectural drawings and surveys). Width of building was measured a row of house width in metres (observations, Google maps, surveys, photographs and measurements). Measurement: measuring tape, architectural drawing and survey. Measurement: measuring tape, architectural drawing and survey. Measurement: measuring tape, architectural drawing and survey.

300

Table 1.27: Continues. 15

Building orientation O (O: degrees)

Orientation of buildings was measured in degrees from the north in a clockwise fashion (measurements: magnetic north compass).

16

Road distances (R: M)

R

17

Dimensions (D: M)

D

18

Quantity (Q: No)

Q

Road distance from the main or side road measured in metres (M: architectural drawings). Measurement: measuring tape, architectural drawing and survey. Observations: calculator

19

Time (T: Hours)

T

Observations: clock

Table 1.28: List of variables assessed, variable operation model and data needs for the study related to the urban built form variable (open space surrogates). Source: Developed from research methods (2016). Item

Variables assessed

Variable model

1

Open space size (X28: X = W x L 28 OS OS M2)

Open space size (small open space size, medium open space size or large space size: observations, Google maps, surveys, photographs and measurements).

2

Hard landscape ratio X = A ÷ X x 100 30 HL 28 (X30: %)

Hard landscape ratio (observations, Google maps, surveys, photographs and measurements).

301

operation Data needs

Table 1.28: Continues. 3

Shading (X32: %)

4

Open space orientation X 33 (X33: Degree)

5

Open space light angle X 34 (X34: Degree)

6

Open space road X 35 proximity (X35: M)

7

Open space (X36: M)

length X 36

Open space length (observations, Google maps, surveys, photographs and measurements).

8

Open space (WOS: M)

width W OS

9

Open space (LOS: M)

length L OS

10

Hard landscape area A = (W x L ) HL HL HL (AHL: M2) Hard landscape width W HL (WHL: M)

Measurement: measuring tape, architectural drawing and survey. Measurement: measuring tape, architectural drawing and survey. Calculations.

11

12

coefficient X = A ÷ X x 100 32 S 28

Shading coefficient (observations, Google maps, surveys and photographs). Open space orientation (measurement magnetic north compass). Open space light angle (observations, Google maps, surveys, photographs and measurements). Open space road proximity (architectural drawings).

Hard landscape length L HL (LHL: M)

302

Measurement: measuring tape, architectural drawing and survey. Measurement: measuring tape, architectural drawing and survey.

Table 1.28: Continues. 13

Shaded area (AS: M2)

AS = (WS x LS)

Calculations.

14

Shaded width (WS: M)

WS

15

Shaded length (LS: M)

LS

16

Orientation degrees)

Measurement: measuring tape, architectural drawing and survey. Measurement: measuring tape, architectural drawing and survey. Orientation of open space was measured in degrees from the north in a clockwise fashion (measurements: magnetic north compass).

17

Road distances (R: M)

R

18

Dimensions (D: M)

D

19

Quantity (Q: No)

Q

Road distance from the main or side road measured in metres (M: architectural drawings). Measurement: measuring tape, architectural drawing and survey. Observations: calculator

20

Time (T: Hours)

T

Observations: clock

(O: O

Table 1.29: List of variables assessed, data needs and primary data sources for the study. Source: Developed from research methods (2016). Item Variables assessed Digital logging sources 1 Micro-temperature change for plot or open space a (Ya Ave), average temperature change for plot or open space a (ΔTa Ave), minimum micro-

Data needs

Primary data sources

Temperature (oC) and Data loggers number of data points (No).

303

Table 1.29: Continues. temperature change (YMin), minimum temperature (T1 Min), maximum microtemperature change (YMax), maximum temperature (T1 Max), total number of temperature (na) and baseline temperature (nB) data points. Surveys and related sources 2 Urban built form: building type (X1: M); district (XA), nodes (XB), edges (XC), landmarks (XD), paths and roads (XE); building height (H: M), building width (W: M), building length (L: M); plot (WP: M), open space (WOS: M), hard landscape (WHL: M) and shaded (WS: M) width; plot (LP: M), open space (X36: M and LOS: M), hard landscape (LHL: M) and shaded (LS: M) length; building and open space orientation (O: Degree), road distances (R: M), dimensions (D: M), quantity (Q: No) and time (T: Hours).

Preliminary and Surveys subsequent field survey, verification of information, districts (district 1, 2 or 3), nodes (open ground), edges (centre, edge or corner plots), landmarks (schools or shops), paths and roads, number of air changes per hour, occupancy pattern, number of occupants (No), number of light bulbs (No), month of data logging (month), heights of adjoining buildings to open spaces, tree numbers, orientation of site, buildings and open spaces, distance to nearest road, dimensions, quantities and time.

304

Table 1.29: Continues. Measurements and related sources 3 Urban built form: Dimensions (M) length, width and height of buildings and open spaces, building (X4: M) and open space (X35: M) road proximity. Observations and related sources 4 Total number of Number (No) temperature data points (na: No), total number of baseline temperature data points (nB: No) and Quantity 5 Commencement and Time (Hours) completion time recordings 6 General observations Visual/analogue 7

Commencement and completion temperature readings 8 Orientation of site, buildings (X3) and open spaces (X33). 9 Urban built form: building type (X1: M), plot ratio Calculations and related sources 10 Calculation and simulation

Temperature (oC)

Physical measurements using measuring tape

Observations

Clock

Sketch pad and writing materials Wet and dry thermometers.

Orientation (Degree)

Magnetic compass

Visual/digital

photographs

Plot (X2: M2) and open Calculator space (X28: M2) size; software building (AB: M2), hard landscape (AHL: M2) and shaded (AS: M2) area, building floor area (A1: M2); ground coverage (X9: %), plot ratio (X15: %), hard landscape ratio (X30: %), shading coefficient (X32: %), 305

north

and

Table 1.29: Continues. Building (X3: Degree) and open space (X33: Degree) orientation, open space light angle (X34: Degree). Table 1.30: List of variables assessed, data needs and secondary data sources. Source: Developed from research methods (2016). Item

Variables assessed

Data needs

Architectural drawings and related sources 1 Urban built form: Site and location plan of building type, plot and study site, orientation, open space sizes, typical building plans orientation and road and sections, sample proximity. population, building types and classification, plot and open space size, building and open space dimensions. Google maps and related sources 2 Urban built form: plot Size of open space, attributes, hard building type and landscape, shading classification, trees, soft coefficient. and hard landscape, roof plan of site. 3 Neighbourhood plan Nairobi Metropolis region map 4

Road proximity

Study site location plan

Meteorological station and related temperature sources 5 Baseline temperature Temperature (oC)

6

Limit of change

climate Temperature (oC)

306

Secondary sources

data

Architectural drawings.

Google maps

Commission for the Implementation of the Constitution map Japan International Cooperation Agency maps Kenya Meteorological Department (1984, p.61): Jomo Kenya International Airport. United Nations Climate Change Secretariat (2015a)

Table 1.30: Continues. Review of related literature sources 7 Technical literature Absorption coefficient and built form (ratio), surface standards emissivity (ratio), thermal conductivity (W/M oC), thermal transmittance and surface conductance (W/M2 oC), solar heat gain factor (ratio), heat rate per occupant (Watts), heat rate per light bulb (Watts) 8 Design temperature Temperature (oC) (TD) 9 Upland climate data Temperature (oC), incident radiation (Watts/M2)

Littlefield (Ed. 2008), Neufert and Neufert (2000), Koenigsberger (et al. 1973), International Organization of Standards (ISO), Kenya Bureau of Standards (KEBS, 2007)

Koenigsberger (et al. 1973, p.78) Hooper (1975, p.94 – 116) and Koenigsberger (et al. 1973, p.229 – 233).

Table 1.31: Showing formulating research tools (observation sheet) for the study. Source: Developed from research methods (2016). Research question 1: what are the urban built form temperature change in Komarock Estate study site? Item Investigated Variable required observation 1 Micro-temperature Micro-temperature change change for plot or open space a (Ya Ave), average temperature change for plot or open space a (ΔTa Ave), minimum micro-temperature change (YMin), minimum temperature (T1 Min), maximum microtemperature change (YMax) and maximum temperature (T1 Max). 307

variables which cause Detail in which data is measured Temperature (oC).

Table 1.31: Continues. Research question 2: what influence do the significant urban built form variables have in contributing to the temperature change? 2 Urban built form Building (X1: Degree) Orientation of site, variables and open space (X8: buildings and open Degree) orientation, spaces, distance to building classification nearest road, (X2: M), building (X3: dimensions, quantities M) and open space (X9: and time. M) road proximity, building type (X4: M), plot (X5: M2) and open space (X13: M2) size and open space length (X12: M). Research question 3: What is the impact of urban built form design and planning strategies on the temperature change? 3 Urban built form Ground coverage (X6: Heights of adjoining surrogates %), plot ratio (X7: %), buildings to open open space light angle spaces, tree numbers, (X10: Degree), open dimensions, quantities space shading and time. coefficient (X11: %) and open space hard landscape coefficient (X14: %). Table 1.32: Showing list of advantages and disadvantages of using observation method in the study of relationship between micro-temperature change and urban built forms. Source: Developed from research methods (2016). Item 1

2

Advantages Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what the study expects to observe (i.e. the data is emergent rather than pre-existing). Observational studies allow the researcher to collect a depth of information about a particular 308

Disadvantages Observational design doesn’t explain situations as the reliability of data is low because seeing behaviours occur over and over again may be a time consuming task and difficult to replicate. Observational research findings may only reflect a unique sample population and thus, cannot be

Table 1.32: Continues. behaviour. Observational studies can reveal interrelationships among multifaceted dimensions of group interactions. Observational studies can generalize results to real life situation.

3

4

5

Observational research is useful for discovering what variables may be important before applying other methods. Observational research designs account for the complexity of group behaviours.

generalized to other groups. Observational studies can have a problem with bias as the researcher may ‘see what they want to see’. There is no possibility to determine a ‘cause and effect’ relationship in observational design, since nothing is manipulated, sources or subjects may not all be equally credible. Any group that is observationally studied, is altered to some degree by the very presence of the researcher, therefore, skewing to some degree any data collected, i.e. the Heisenberg Uncertainty Principle.

Table 1.33: List of variables assessed, data needs and research tools for the study. Source: Developed from research methods (2016). Item 1

Variables assessed Micro-temperature change and urban built form

2

Data collection batch details

3

Data collection data logger details

4

Level of compliance to regulations and standards

Data needs Summary page, general information and raw data from field study, micro-temperature change and urban built form analysis, and simulation techniques. Batch number (No), cluster and plot numbers (No) and expected results. Commencing and completion date (date), batch number (No), cluster and plot numbers (No) and expected results. Compliance to procedures and protocols, orientation of 309

Research tools Observation book and sheets

Batching journal

Data logger journal

Check lists

Table 1.33: Continues.

5

site and units (Degree), commencement and completion temperature (oC) and time (Hours data, transformation and modifications of typical units by users, occupancy pattern (Hours), number of occupants (No) and light bulbs (No), dimensions and as-built sketches of non-typical units. Item, descriptions, data Tabulations tabulation and remarks.

Tabulation of data

Table 1.34: Showing tabulated batching journal. Source: Field survey (2015). Batch No Comparative Study

Expected Results

Cluster/Plot No

Cluster/Plot No

Open Remarks/Testing/Investigating Space No

1

BV, C4

BV, C5

OG, C1A

Comparing same building type: basic Villa (BV) - Effect of orientation.

2

BM, C3

BM, C2

OG, C1A

Comparing same building type: basic Maisonette (BM) – Effect of orientation.

3

LP, C7

CP, C6

OG, C1A

Testing effect of size of plot (Corner Plot: CP), proximity to road (Large Plot: LP) and densification.

4

Shop, C10

School, C10

R, C11

Testing change of user effect: Road as the open space – Different building types.

310

Table 1.34: Continues. 5

Shop, C8

Trans, C8

OG, C1A

Testing effect of transformations: Same orientation/shop/transformation.

6

BM, RH

BV,

OG, RH

Testing effect of building classification: Row Houses (RH): Basic Maisonette (BM) & Basic Villa (BV) – Same orientation: Same location.

7

BM, SD

BV, SD

OG

Testing effect of building classification: Semi-detached Houses (SD): Basic Maisonette (BM) & Basic Villa (BV) Same orientation.

8

BM, D

BV, D

OG

Testing effect of building classification: Detached House (D): Basic Maisonette (BM) & Basic Villa (BV) – Same orientation.

9

LP, M/V

SP, M/V

OG

Testing effect of size of plot: Same Row of Houses: Large Plot (LP) & Small Plot (SP): Maisonette (M) & Villa (V): Same orientation.

10

S1, M/V

S3, M/V

OG

Testing effect of sector and location: Sector (S): Same orientation. Maisonette (M) & Villa (V).

11

SS, M/V

LS, M/V

OG

Testing effect of open space size: Small & Large Open Spaces: Same orientation.

12

R

OG

13

RH, M/V

SD, M/V

Testing effect of open space type: Road (R) & Open Ground (OG): Same orientation: Same district. OG

311

Testing effect of building classification: Row House (RH) & Semi Detached (SD): Maisonette (M) & Villa (V):

Table 1.34: Continues. Same orientation district.

&

same

14

SD, M/V

D, M/V

OG

Testing effect of building classification: Semi Detached (SD) & Detached (D): Maisonette (M) & Villa (V): Same orientation & same district.

15

D!, M/V

D3, M/V

OG

Testing effect of district and location: District 1 (D1) & District 3 (D3): Maisonette (M) & Villa (V): Different districts, same orientation.

Table 1.35: Showing tabulated data logger journal. Source: Field survey (2015). Comparative Study Batch No

Date: Commencing Saturday

Expected Results

Cluster/Plot No

Cluster/Plot No

Open Space Remarks/Testing/ No Investigating

8/6/13 15/6/13

to 1

41, BV, C4

48, BV, C5

33, OG3, C1

Sampling same building type: Basic Villa (BV) – Effect of orientation. Met Data Used: June.

29/6/13 6/7/13

to 2

74, BM, C3

237, BM, C2

OG5, C1

Sampling same building type: Basic Maisonette (BM) – Effect of orientation. Met Data Used: July.

312

Table 1.35: Continues. 20/7/13 277/13

to 3

16, LP, C7

34, CP, C6

OG3, C1

24/1/15 31/1/15

to 4

77, C10

7/2/15 14/2/15

to 5

71, Shop, C8

68, Trans, C8 68, OG4, C1

Sampling effect of transformations: Same orientation/shop/tr ansformation. Met Data Used: February.

23/5/15 30/5/15

to 6

99, BM, C3

79, BV, C5

R9, C11

Sampling effect of different row housing/same orientation, different types/different road proximity.

6/6/15 13/6/15

to 7

211, BV, C4

234, BM, C2

P6, C12

Sampling of different rows/same orientation/differe nt types/different road proximity.

20/6/15 27/6/15

to 8

220, School, 122, LP Int, OG12, C1 C10 C7

Shop, 19, School, 76, R1, C11 C10

313

Sampling effect of size of plot (Corner Plot: CP), proximity to road (Large Plot: LP) and densification. Met Data Used: July. Sampling change of user effect: Road as the open space – Different building Type. Met Data Used: January.

Sampling of district 2 data/school + large plot.

Table 1.35: Continues. 4/7/15 11/7/15

to 9

180, LP Near 109, BV, C5 Rd, C7

R11, C11

Sampling district 2 data/kiosk + large plot near road.

18/7/15 25/7/15

to 10

137, SP BM, 158, LP near OG14, C1 C2 Rd., C7

Sampling of district 3 data/large plot near road/small plot internal/N-S Maisonette.

1/8/15 8/8/15

to 11

142, BV, C5

172, LP near R15, C11 rd, C7

Sampling district 3 data/large plot near road/BV E-W.

15/8/15 22/8/15

to 12

133, BV, C4

125, BM, C3

Sampling district 3 data/BV N-S/BM E-W orientations.

22/8/15 29/8/15

to 13

233, BV, C5

164, LPN OG14, C1 Rd, C7

Sampling district 3 data/BV E-W/LPN Rd E-W orientations.

29/8/15 5/9/15

to 14

54, C4

10, C7

P3, C12

Sampling district 1 data/BV N-S/LPN Rd E-W orientation.

12/9/15 19/9/15

to 15

225, C4

218, C7

OG8

Sampling district 2 data/BV NS/LPN Rd E-W orientation.

68, OG4, C1

Table 1.36: Showing tabulated cluster sampling (plots), description and explanation. Source: Developed from research methods (2010). Cluster No:

Description:

Explanation:

1

Open Ground

Primarily 10% of the total area is normally set aside for amenities, social and services. 314

Table 1.36: Continues. 2

Basic Maisonette on N-S Basic unit for measurement in the Orientation ‘Desired’ orientation.

3

Basic Maisonette on E-W Basic unit Orientation orientation.

4

Basic Villa Orientation

on

N-S Smaller unit orientation.

5

Basic Villa Orientation

on

E-W Smaller unit in an ‘Undesired’ orientation.

6

Corner Located

7

Large Plot near Main Road

Larger unit attributes.

8

Transformed Plot

Unit modified to accommodate user needs.

10

Change of User Plot

Unit modified to accommodate functional change by user.

11

Road: Vehicular Circulation

Primarily: 10% of the total area is normally set aside for amenities, social and services. Description: Vehicle circulation.

12

Paths: Circulation

Plot

in

an in

‘Undesired’

the

‘Desired’

Internally Larger unit with nominal attributes. with

extraneous

Pedestrian Primarily: 10% of the total area is normally set aside for amenities, social and services. Description: Pedestrian circulation.

Table 1.37: Showing tabulated data logged temperatures for Logger 2 (Plot 41 External) for the week 8th to 15th June 2013. Source: Field survey (2013). Time (LMT) (Hours)

June 2013 (oC) Sat 8

Sun 9

Mon 10

Tue Wed Thu Fri 11 12 13 14

0.00

19.0

19.0

19.0 18.3

19.4 17.5 16.8 18.44

18.4

1.00

18.3

18.7

19.0 17.1

18.3 16.8 17.1 17.9

17.9

2.00

17.1

18.3

18.3 16.8

18.3 16

17.4

315

Sat 15

Weekly Rounding Average (41 Ext)

17.1 17.41

Table 1.37: Continues. 3.00

16.8

18.3

18.3 17.1

18.3 15.6 17.5 17.4

17.4

4.00

16

17.9

17.9 17.1

17.5 15.2 17.1 17

17

5.00

15.6

17.9

17.5 16.4

17.1 14.5 16.8 16.54

16.5

6.00

15.2

17.5

17.5 16.4

17.1 14.1 16.8 16.4

16.4

7.00

14.9

17.5

17.5 16.8

16.8 14.5 16.8 16.4

16.4

8.00

16.4

17.9

17.5 17.9

17.1 16.8 16.8 17.19

17.2

9.00

21.7

17.9

19.0 19.8

18.3 19.8 17.9 19.2

19.2

10.00

25.2

18.7

24.0 29.5

20.6 25.6 20.6 23.4

23.4

11.00

28.3

21

26.3 26.7

24.4 37.4 19.0 26.2

26.2

12.00

26.7

28.3

23.2

27.1 29.1

24.4 38.3 21.7 27.37

27.4

13.00

31.9

30.3

25.6

25.6 33.6

25.6 38.3 25.2 29.5

29.5

14.00

31.5

30.7

25.6

24.4 31.9

29.5 31.9 26.3 28.99

29

15.00

29.9

29.1

25.6

24.8 28.3

26.3 29.9 27.9 27.73

27.7

16.00

28.3

27.9

24.0

24.0 26.7

24.4 26.3 27.9 26.2

26.2

17.00

27.1

26.3

23.2

22.1 25.6

24.4 25.2 24.8 24.84

24.8

18.00

25.6

24.0

22.1

21.3 23.6 22.1

23.6 22.9 23.15

23.2

19.00

24.0

22.9

21

20.2 22.1 20.2

22.9 22.1 21.91

21.9

20.00

22.3

22.5

20.6

19.8 21.3 19.4

18.3 21.3 20.71

20.7

21.00

21.7

21

19.8

19.0 21

18.7

17.1 21

19.90

19.9

22.00

20.6

19.8

19.8

19.0 21

18.7

17.1 21

19.62

19.6

23.00

20.2

19.4

19.4

18.7 19.8 17.5

17.1 20.6 19.09

19.1

24.00

19.0

19.0

19.0

18.3 19.4 17.5

16.8 20.6 18.71

18.7

Average: 25.3

21.8

20.4

20.7 22.1 20.5

21.9 20.5 21.25

21.2

Table 1.38: Showing tabulated average data logged temperatures for Logger 2 (Plot 41 External) for the month of June. Source: Field survey (2015). Time (LMT) (Hours)

Plot 41 (External) Temperature (41) (oC)

Remarks

0.00

18.4

T41 0.00

1.00

17.9 316

Table 1.38: Continues. 2.00

17.4

3.00

17.4

4.00

17

5.00

16.5

6.00

16.4

7.00

16.4

8.00

17.2

9.00

19.2

10.00

23.4

11.00

26.2

12.00

27.4

13.00

29.5

14.00

29

15.00

27.7

16.00

26.2

17.00

24.8

18.00

23.2

19.00

21.9

20.00

20.7

21.00

19.9

22.00

19.6

23.00

19.1

24.00

18.7

T41 24.00

Average

21.2

T41 Ave

T41 Min

T41 Max

317

Table 1.39: Showing tabulated average monthly temperature data for Jomo Kenyatta International Airport Embakasi for the period 1959 to 1980 used as baseline temperature. Source: Adapted from Kenya Meteorological Department (1984, p.61). Month

Temperature Minimum (TMin) (oC) 5.00

Temperature Dry Bulb (T9.00) (oC) 9.00

Temperature Maximum (TMax) (oC) 13.00

Temperature Remarks Dry Bulb (T15.00) (oC) 15.00

11.9 12.4 13.2

18.3 18.6 18.6

26.6 27.7 27.6

25.5 26.6 26.4

April May June

14.5 13.5 11.5

18.2 17.4 15.7

26.0 24.6 23.6

24.7 23.4 22.5

July

10.7

14.8

22.5

21.4

August September October November December

10.8 11.0 12.6 13.3 12.7

15.0 16.2 18.0 17.7 18.1

23.1 25.6 26.7 25.2 25.5

21.9 24.4 25.5 23.8 24.4

Time (LMT) January February March

Hottest Month

Study Month Coldest Month

Table 1.40: Showing tabulated baseline temperatures for the month of June generated by the Temperature Template Workbook of the Ebenergy Software. Source: Field survey (2013). Time (LMT) (Hours)

Baseline (To) (oC)

0.00

15.4

1.00

14.6

2.00

13.9

3.00

13.1

4.00

12.3

Temperature Remarks To 0.00

318

Table 1.40: Continues. 5.00

11.5

To Min

6.00

12.6

7.00

13.6

8.00

14.7

9.00

15.7

10.00

17.7

11.00

19.7

12.00

21.6

13.00

23.6

14.00

23.1

15.00

22.5

16.00

21.7

17.00

20.9

18.00

20.1

19.00

19.4

20.00

18.6

21.00

17.8

22.00

17

23.00

16.2

24.00

15.4

To 24.00

Average:

17.3

To Ave

To 9.00

To Max To 15.00

Table 1.41: Showing resultant tabulated data logged for Logger 2 (Plot 41 External) for the week 8th to 15th June 2013 and tabulated baseline temperatures for the month of June. Source: Field survey (2013). Time (LMT) (Hours)

Plot 41 Baseline (External) Temperature Temperature June (To) (oC) (41) (oC)

Micro-temperature Change (41 - To) (oC)

Remarks

0.00

18.4

3.0

To 0.00 and

15.4 319

Table 1.41: Continues. T41 0.00 1.00

17.9

14.6

3.3

2.00

17.4

13.9

3.5

3.00

17.4

13.1

4.3

4.00

17

12.3

4.7

5.00

16.5

11.5

5

6.00

16.4

12.6

3.9

7.00

16.4

13.6

2.8

8.00

17.2

14.7

2.6

9.00

19.2

15.7

3.5

10.00

23.4

17.7

5.7

11.00

26.2

19.7

6.6

12.00

27.4

21.6

5.8

13.00

29.5

23.6

5.9

14.00

29

23.1

6.0

15.00

27.7

22.5

5.2

16.00

26.2

21.7

4.5

17.00

24.8

20.9

3.9

18.00

23.2

20.1

3.1

19.00

21.9

19.4

2.5

20.00

20.7

18.6

2.1

21.00

19.9

17.8

2.1

22.00

19.6

17

2.6

23.00

19.1

16.2

2.9

24.00

18.7

15.4

3.3

To 24.00 and T41 24.00

Average:

21.2

17.3

3.9

TAve

320

To Min T41 Min To 9.00

To Max To 15.00

Table 1.42: Resultant micro-temperature change for the 30 plots. Source: Field survey (2015). Item

Plot No.

Micro-temperature Change (oC)

1

237

4.2

2

234

2.8

3

137

1.8

4

74

2

5

99

1.4

6

125

2.9

7

41

3.9

8

211

1.5

9

133

3.3

10

54

2.8

11

225

6.7

12

48

2.7

13

79

1.6

14

109

3.3

15

142

7.2

16

233

3.5

17

34

5.6

18

122

1.7

19

218

3.8

20

10

3.1

21

16

4.2

22

180

3.5

23

158

3

24

172

3.5

25

164

4.1

26

68

3

27

71

3.7

28

19

3.3

Remarks

To Min

To Max

321

Table 1.42: Continues. 29

77

5.5

30

220

3.1

Average:

3.4

To Ave

Table 1.43: Showing the resultant micro-temperature change for the 16 open spaces for the period 8th June 2013 to 19th September 2015. Source: Field survey (2015). Item

Open Space No.

Micro-temperature Change (oC)

1

OG3

3.8

2

OG3

2.4

3

OG4

2.6

4

OG5

3.8

5

OG12

4

6

OG14

3.3

7

OG13

4.1

8

OG14

3.9

9

OG8

7.5

10

R1

2.9

11

R6

5.5

12

R9

1.6

13

R11

2.9

14

R15

3.4

15

P6

2

16

P3

6

Average:

Remarks

To Max

To Min

3.7

To Ave

322

Table 1.44: Showing information displayed in the Analogue Workbook (Mandatory Workbooks) of Ebstats Software. Source: Adapted from Ebrahim (2015). Item

Workbook Type

Abbreviation

1

Mandatory Workbooks:

2

Consolidated Summary Sheet

Cons Sum

3

Data Analysis Summary Sheet

D Analysis Sum

Table 1.45: Consolidated Summary Sheet of Ebstats Software. Source: Field survey (2015). Measure of Tendency Item Description

Unit Plot 41

Central Measure of Dispersion

Mean Median Mode Minimum Maximum Range

1

Urban Built Form Analysis:

2

Plot No.

3

District

4

Node

5

Edge

6

Landmark

7

Path & Road

8

Building Type

M

5.3

6.4

7.7

7.7

5.3

7.7

2.4

9

Plot Size

M2

108

140

108

108

101

422.3

321.3

10

Orientation

Deg. 46

171.2

136

136

46

316

270

11

Road Proximity

M

85.6

51.9

51.8

35.4

11.4

98.2

86.8

12

Building M Classification

42

40.1

21

36

6

153

147

13

Ground Coverage

%

44

47.6

44

44

23

86

63

14

Plot Ratio

%

44

65.9

66

44

29

162

133

No.

41 D1 Edge

323

Table 1.45: Continues. 15. Micro-temperature change analysis: June. 16

To

o

17.3

17.6

17.3

17.3

16.4

20

3.6

17

Log 2 Garden (L2)

o

21.2

21

22

22

18.8

24.7

5.9

18

TΔ = L2 - To

o

C

3.9

3.4

3

3

1.4

7.2

5.8

19

Y9

%

22.5

19.5

17.9

8.7

8.5

39.8

31.3

C C

Table 1.46: Showing part information displayed in the Data Analysis Summary Workbook of Ebstats Software related to collected data for building variables. Source: Field survey (2015). Item

Plot No.

X1

X2

X3

X4

X5

X6

X7

Y

1

237

46

36

22

7.7

108

37

71

4.2

2

234

46

36

45.8

7.7

108

51

84

2.8

3

137

46

24.5

65.8

7.7

101

40

75

1.8

4

74

136

18

35.4

7.7

108

37

71

2

5

99

136

153

83.5

7.7

108

37

71

1.4

6

125

136

43.5

83.5

7.7

108

37

71

2.9

7

41

46

42

85.6

5.3

108

44

44

3.9

8

211

46

36

35.6

5.3

108

44

44

1.5

9

133

46

30

76

5.3

108

44

44

3.3

10

54

226

42

72.5

5.3

108

57

57

2.8

11

225

224

36

38.5

5.3

108

45

45

6.7

12

48

136

39

37.7

5.3

108

44

44

2.7

13

79

136

153

77.8

5.4

108

44

44

1.6

14

109

226

45.1

46.4

5.4

127

66

66

3.3

15

142

136

57.5

43

6.4

108

62

82

7.2

16

233

316

45

71.5

5.3

127

47

47

3.5

17

34

136

6

93.6

7.7

164.4

40

75

5.6

18

122

226

6.5

98.6

5.3

169

29

29

1.7

324

Table 1.46: Continues. 19

218

316

18

26.5

5.3

132

37

37

3.8

20

10

316

24

28.8

5.3

144

63

63

3.1

21

16

316

21

23.5

7.7

171.4

23

44

4.2

22

180

316

49.4

28.8

7.7

165

40

62

3.5

23

158

46

42

80.4

7.7

165

24

46

3

24

172

316

73.3

31

7.7

159

45

62

3.5

25

164

316

73.3

31

5.3

159

30

30

4.1

26

68

46

12

68.4

6.2

108

74

106

3

27

71

46

6

51.8

6.2

108

86

162

3.7

28

19

205

8.1

11.4

5.3

422.3

83

83

3.3

29

77

226

17.8

52

7.7

132.3

47

77

5.5

30

220

226

10

12.3

7.7

240

70

141

3.1

ƩX/n

Mean

171.2

40.1

51.9

6.4

140

47.6

65.9

3.42

Abbreviation

Ẋ1

Ẋ2

Ẋ3

Ẋ4

Ẋ5

Ẋ6

Ẋ7

Ẏ

Note: Details of the plots (Column 2) compared to the building orientation X1 in degrees (Column 3), building classification X2 in metres (Column 4), road proximity X3 in metres (Column 5), building type X4 in metres (Column 6), plot size X5 in square metres (Column 7), ground cover X6 as a percentage (Column 8), plot ratio X7 as a percentage (Column 9) and micro-temperature change Y in degree Celsius (Column 10).

Table 1.47: Showing part information displayed in the Data Analysis Summary Workbook of Ebstats Software related to collected data for open space variables. Source: Field survey (2015). Item

Open No.

1

OG3

2

OG3

Space X8

X9

X10

X11

X12

X13

X14

Y

46

91.9

85

81.8

45

822

68

3.8

226

97.1

85

81.8

45

822

68

2.4

325

Table 1.47: Continues. 3

OG4

46

75.4

87

86.6

39

1182

65

2.6

4

OG5

46

24.9

90

45.9

19.1

349

50

3.8

5

OG12

136

95

90

29.4

43.6

763

67

4

6

OG14

46

80.4

80

48.8

43.5

1838

49

3.3

7

OG13

46

76

86

65

30

738

80

4.1

8

OG14

316

31

88

49

53

1838

49

3.9

9

OG8

226

38.5

87

27

36

1071

75

7.5

10

R1

136

40

80

58

23

276

42

2.9

11

R6

226

50.1

82

40.4

18

270

40

5.5

12

R9

136

83.5

80

39.3

67.9

815

50

1.6

13

R11

316

32

80

35.5

45.1

541

50

2.9

14

R15

46

31

80

22

48.5

582

50

3.4

15

P6

316

50.5

55

0

39

117

100

2

16

P3

226

74.5

55

51

42

126

100

6

ƩX/n

Mean

158.5

60.7

80.6

47.6

39.9

759.4 62.7

3.7

Abbreviation

Ẋ8

Ẋ9

Ẋ10

Ẋ11

Ẋ12

Ẋ13

Ẏ

Ẋ14

Note: Details of the open spaces (Column 2) compared to the open space orientation X8 in degrees (Column 3), open space road proximity X9 in metres (Column 4), open space light angle X10 in degrees (Column 5), open space shading coefficient X11 as a percentage (Column 6), open space length X12 in metres (Column 7), open space area X13 in square metres (Column 8), open space hard landscape coefficient X14 as a percentage and micro-temperature change Y in degree Celsius (Column 10).

326

Table 1.48: Data Analysis Summary Workbook of Ebstats Software related to Measures of Central Tendency and Dispersion for Building Variables. Source: Field survey (2015). Item Description

Unit

Mean Median Mode Minimum Maximum Range

Building Variables Analysis 1

Building Type

M

6.4

7.7

7.7

5.3

7.7

2.4

2

Plot Size

M2

140

108

108

101

422.3

321.3

3

Building Orientation

Degree 171.2

136

136

46

316

270

4

Building Road Proximity

M

51.9

51.8

35.4

11.4

98.2

86.8

5

Building M Classification

40.1

21

36

6

153

147

6

Ground Coverage

%

47.6

44

44

23

86

63

7

Plot Ratio

%

65.9

66

44

29

162

133

8

Building Microtemperature Change

o

C

3.4

3

3

1.4

7.2

5.8

9

Building Microtemperature Change

%

19.5

17.9

8.6

8.5

39.8

31.3

Table 1.49: Data Analysis Summary Workbook of Ebstats Software related to Measures of Central Tendency and Dispersion for Open Space Variables. Source: Field survey (2015). Item Description

Unit

Mean Median Mode Minimum Maximum Range

Open Space Variables Analysis 10

Open Space Degree 158.5 Orientation

136

327

46

46

316

270

Table 1.49: Continues. 11

Open Space M Road Proximity

12

Light Angle

13

Shading Coefficient

60.7

50.5

50.1

24.9

97.1

72.2

Degree 80.6

80

80

55

90

35

%

47.6

45.9

81.8

0

86.6

86.6

14

Open Space M Length

39.9

39

39

18

67.9

49.9

15

Open Space M2 Area

759.4

541

822

117

1838

1721

16

Hard Landscape Coefficient

%

62.7

65

50

40

100

60

17

Open Space Microtemperature Change

o

3.7

3

3.8

1.6

7.5

5.9

18

Open Space % Microtemperature Change

21.1

17.7

22

8.5

41.4

32.9

C

Table 1.50: Shows details of Batch 1 data collection. Source: Field survey (2013). Item

Description

Remarks

1

GENERAL INFORMATION:

Komarock Infill B, Nairobi.

2

SAMPLE NUMBER:

3

REFERENCE Sample 1A:

CLUSTER:

4: Basic Villa N/S Plot.

SAMPLE NAME:

Plot No. 41

FLOOR NUMBER:

Living Room and Garden Area (Ground Floor)

ORIENTATION:

N Facing (30 Deg).

SAMPLE NUMBER:

REFERENCE Sample 1B. 328

Table 1.50: Continues.

4

CLUSTER:

5: Basic Villa E/W Plot.

SAMPLE NAME:

Plot No. 48

FLOOR NUMBER:

Living Room and Garden Area (Ground Floor):

ORIENTATION:

E Facing (120 Deg).

SAMPLE NUMBER:

REFERENCE Sample 1C:

CLUSTER:

1: Open Area: Surrogate.

SAMPLE NAME:

Open Ground: OG3.

FLOOR NUMBER:

Ground Floor: Open Space:

ORIENTATION:

Omni-direction.

Table 1.51: Shows tabulated mean data logger and simulated temperature values compared to time for Batch 1 (June 2013). Source: Field survey (2013). Time (Hours)

Temperatures (oC) 41 Lounge 41 48 Lounge (AveLog1: L1) Garden (AveLog3: (AveLog2: L3) L2)

48 Garden (AveLog4: L4)

Open To Space June OG3 (AveLog5: L5)

00.00

22.8

18.4

21.6

18.3

19.1

15.4

01.00

22.4

17.9

21.2

17.8

18.4

14.6

02.00

22

17.4

21

17.4

18.1

13.9

03.00

21.8

17.4

20.7

17.2

17.8

13.1

04.00

21.6

17

20.3

16.8

17.3

12.3

05.00 (ToMin)

21.3

16.5

20

16.4

16.9

11.5 (ToMin)

06.00

21.2

16.4 (L2Min)

19.6

16.1 (L4Min)

16.7 (L5Min)

12.6

07.00

21.1 (L1Min)

16.4

19.4 (L3Min)

16.1

16.7

13.6

329

-

Table 1.51: Continues. 08.00

21.1

17.2

19.4

16.8

17.2

14.7

09.00

21.3

19.2

19.8

18.4

19.7

15.7

10.00

21.9

23.4

20.2

20

22.3

17.7

11.00

22.5

26.2

20.9

21.5

24.3

19.7

12.00

23.4

27.4

22.6

23.9

25.8

21.6

13.00 24.5 (ToMax)

29.5 (L2Max)

24

25.1

26.5 (L5Max)

23.6 (ToMax)

14.00

25.3

29

23.9

24.7

26.1

23.1

15.00

25.5 (L1Max)

27.7

24.4 (L3Max)

25.3 (L4Max)

26.2

22.5

16.00

25.2

26.2

24.3

24.8

26

21.7

17.00

24.4

24.8

23.7

23.6

25

20.9

18.00

23.7

23.2

23.2

22.1

23.6

20.1

19.00

23.3

21.9

22.4

21

22.7

19.4

20.00

23.2

20.7

22.1

20.1

21.6

18.6

21.00

22.9

19.9

21.7

19.6

21

17.8

22.00

23.1

19.6

21.7

19.1

20.3

17

23.00

22.9

19.1

21.6

18.9

19.8

16.2

24.00

22.5

18.7

21.4

18.6

19.2

15.4

TAve

22.8

21.2

21.6

20

21.1

17.3

Table 1.52: Computed temperature variances compared to time for Batch 1. Source: Field survey (2013). Time Temperatures (oC) (Hours) L – T L2 – L5 5 o

Climate Change Derivitives (oC) L4 – L5

L2 – L4

L2 – To

L4 – To

00.00

3.7

-0.7

-0.8

0.1

3.0

2.9

01.00

3.8

-0.5

-0.6

0.1

3.3

3.2

02.00

4.2

-0.7

-0.7

0

3.5

3.5

03.00

4.7

-0.4

-0.6

0.2

4.3

4.1

04.00

5

-0.3

-0.5

0.2

4.7

4.5

330

Table 1.52: Continues. 05.00

5.4

-0.4

-0.5

0.1

5

4.9

06.00

4.2

-0.3

-0.6

0.3

3.9

3.6

07.00

3.1

-0.3

-0.6

0.3

2.8

2.5

08.00

2.6

0

-0.4

0.4

2.6

2.2

09.00

4

-0.5

-1.3

0.8

3.5

2.7

10.00

4.6

1.1

-2.3

3.4

5.7

2.3

11.00

4.7

1.9

-2.8

4.7

6.6

1.9

12.00

4.2

1.6

-1.9

3.5

5.8

2.3

13.00

2.9

3

-1.4

4.4

5.9

1.5

14.00

3.1

2.9

-1.4

4.3

6.0

1.7

15.00

3.7

1.5

-0.9

2.4

5.2

2.8

16.00

4.3

0.2

-1.2

1.4

4.5

3.1

17.00

4.1

-0.2

-1.4

1.2

3.9

2.7

18.00

3.5

-0.4

-1.5

1.1

3.1

2.0

19.00

3.3

-0.8

-1.7

0.9

2.5

1.6

20.00

3

-0.9

-1.5

0.6

2.1

1.5

21.00

3.2

-1.1

-1.4

0.3

2.1

1.8

22.00

3.3

-0.7

-1.2

0.5

2.6

2.1

23.00

3.6

-0.7

-0.9

0.2

2.9

2.7

24.00

3.8

-0.5

-0.6

0.1

3.3

3.2

TAve

3.8

0.1

-1.5

1.3

3.9

2.7

Table 1.53: Shows the inputted data at the data logged and simulated stages in the Query Workbook of the Ebenergy Software and comfort score generated. Source: Field survey (2013). Item

Symbol

Description: Workbook Data

Query Unit

TECHNOLOGICAL DATA REQUIRED Overall Dimensions

331

Existing Value

Retrofit Design Value

Table 1.53: Continues. 1

L

Building Length

m

34.7

34.7

2

W

Width Room

m

5.6

5.6

3

H

Height Room

m

27.5

27.5

27.5

27.5

8.5

1.3

0.4

0.4

W/m2oC

13.18

13.18

SCHEDULE OF MATERIALS: ROOFING Existing: Mabati Roofing Retrofitted: Mabati roofing with ventilated/insulated/reflecte d foil ceiling 4

Hr

Height: Roof

m 2o

5

Ur

Transmittance: Roof

W/m C

6

a

Absorbance of Roof Surface Ratio

7

Fo

Surface Conductance:

GLASS: Translucent sheets Window with single glazing 8

Hg

Height: Glass

m

0

0

9

Ug

Transmittance: Glass

W/m2oC

4

4

10

Q

Solar Gain Factor Glass

Ratio

0.85

0.85

o

20

20

SOCIETAL DATA REQUIRED 11

Ti

Design Indoor Temperature

12

N

Number of Air Changes per No. Hour

3

3

OCCUPANCY PATTERN

Hours

25

25

Nio

Number of Occupants

No.

20

20

HRo

Heat Rate per Occupant

W

140

140

Nie

Number of Electric Bulbs

No.

144

144

HRb

Heat Rate per Electric Bulb

W

10

10

o

32.5

32.5

13

To

CLIMATIC REQUIRED

DATA

Design Temperature

Outdoor 332

C

C

Table 1.53: Continues. I

Incident Radiation

W/m2

300

KWh

-1198.85 -306.07

300

BASELINE CLIMATE Temperature: MARCH MATURE ARCHITECTURE: Cool

KW

Comfort Score

Table 1.54: Consolidated Summary Sheet of Ebstats Software related to processed data of building variables and display of collected data. Source: Field survey (2016). Item

Plot No.

X1

X2

X3

X4

X5

X6

X7

Y

1

237

46

36

22

7.7

108

37

71

4.2

2

234

46

36

45.8

7.7

108

51

84

2.8

3

137

46

24.5

65.8

7.7

101

40

75

1.8

4

74

136

18

35.4

7.7

108

37

71

2

5

99

136

153

83.5

7.7

108

37

71

1.4

6

125

136

43.5

83.5

7.7

108

37

71

2.9

7

41

46

42

85.6

5.3

108

44

44

3.9

8

211

46

36

35.6

5.3

108

44

44

1.5

9

133

46

30

76

5.3

108

44

44

3.3

10

54

226

42

72.5

5.3

108

57

57

2.8

11

225

224

36

38.5

5.3

108

45

45

6.7

12

48

136

39

37.7

5.3

108

44

44

2.7

13

79

136

153

77.8

5.4

108

44

44

1.6

14

109

226

45.1

46.4

5.4

127

66

66

3.3

15

142

136

57.5

43

6.4

108

62

82

7.2

16

233

316

45

71.5

5.3

127

47

47

3.5

17

34

136

6

93.6

7.7

164.4

40

75

5.6

18

122

226

6.5

98.6

5.3

169

29

29

1.7

19

218

316

18

26.5

5.3

132

37

37

3.8

333

Table 1.54: Continues. 20

10

316

24

28.8

5.3

144

63

63

3.1

21

16

316

21

23.5

7.7

171.4

23

44

4.2

22

180

316

49.4

28.8

7.7

165

40

62

3.5

23

158

46

42

80.4

7.7

165

24

46

3

24

172

316

73.3

31

7.7

159

45

62

3.5

25

164

316

73.3

31

5.3

159

30

30

4.1

26

68

46

12

68.4

6.2

108

74

106

3

27

71

46

6

51.8

6.2

108

86

162

3.7

28

19

205

8.1

11.4

5.3

422.3

83

83

3.3

29

77

226

17.8

52

7.7

132.3

47

77

5.5

30

220

226

10

12.3

7.7

240

70

141

3.1

ƩX/n

Mean

171.2

40.1

51.9

6.4

140

47.6

65.9

3.42

Abbreviation

Ẋ1

Ẋ2

Ẋ3

Ẋ4

Ẋ5

Ẋ6

Ẋ7

Ẏ

Note: Details of the plots (Column 2) compared to the building orientation X1 in degrees (Column 3), building classification X 2 in metres (Column 4), road proximity X3 in metres (Column 5), building type X4 in metres (Column 6), plot size X5 in square metres (Column 7), ground cover X6 as a percentage (Column 8), plot ratio X7 as a percentage (Column 9) and micro-temperature change Y in degree Celsius (Column 10).

Table 1.55: Consolidated Summary Sheet of Ebstats Software related to processed data of open space variables and display of collected data. Source: Field survey (2016). Item

Open No.

1

OG3

2 3

Space X8

X9

X10

X11

X12

X13

X14

Y

46

91.9

85

81.8

45

822

68

3.8

OG3

226

97.1

85

81.8

45

822

68

2.4

OG4

46

75.4

87

86.6

39

1182

65

2.6

334

Table 1.55: Continues. 4

OG5

46

24.9

90

45.9

19.1

349

50

3.8

5

OG12

136

95

90

29.4

43.6

763

67

4

6

OG14

46

80.4

80

48.8

43.5

1838

49

3.3

7

OG13

46

76

86

65

30

738

80

4.1

8

OG14

316

31

88

49

53

1838

49

3.9

9

OG8

226

38.5

87

27

36

1071

75

7.5

10

R1

136

40

80

58

23

276

42

2.9

11

R6

226

50.1

82

40.4

18

270

40

5.5

12

R9

136

83.5

80

39.3

67.9

815

50

1.6

13

R11

316

32

80

35.5

45.1

541

50

2.9

14

R15

46

31

80

22

48.5

582

50

3.4

15

P6

316

50.5

55

0

39

117

100

2

16

P3

226

74.5

55

51

42

126

100

6

ƩX/n

Mean

158.5

60.7

80.6

47.6

39.9

759.4 62.7

3.7

Abbreviation

Ẋ8

Ẋ9

Ẋ10

Ẋ11

Ẋ12

Ẋ13

Ẏ

Ẋ14

Note: Details of the open spaces (Column 2) compared to the open space orientation X8 in degrees (Column 3), open space road proximity X9 in metres (Column 4), open space light angle X10 in degrees (Column 5), open space shading coefficient X11 as a percentage (Column 6), open space length X12 in metres (Column 7), open space area X13 in square metres (Column 8), open space hard landscape coefficient X14 as a percentage and micro-temperature change Y in degree Celsius (Column 10).

Table 1.56: Shows list of data needs and analysis techniques for the study. Source: Developed from research methods (2016). Item Techniques of data analysis Types of data needed Dependent variable: Micro-temperature change (Y: oC) Independent variables and surrogates (Urban built form): building type (X4: M), plot 335

Table 1.56: Continues. size (X5: M2) and open space size (X13: M2), building orientation (X1: Degree) and open space orientation (X8: Degree), building road proximity (X3: M) and open space road proximity (X9: M), building classification (X2: M), ground coverage (X6: %), plot ratio (X7: %), hard landscape coefficient (X14: %), light angle (X10: Degree), shading coefficient (X11: %) and open space length (X12: M). 1 Processed data Building and open space variables. 2 Summary statistics Number, mean, standard deviation, range, skewness, kurtosis, coefficient of variation, p25, median, p75 and sum. 3 Pearson correlation matrix Building orientation, building classification, road proximity, building type, plot size, ground coverage and plot ratio. 4 Normality test Shapiro-Wilk W-test for normal data: observation, W, V, z and Prob (Prob>). 5 Multicollinearity test Variance Inflation Factors (VIF): VIF and tolerance (1/VIF). 6 Heteroscedasticity Breush-Pagan/Cook-Weisberg test for heteroskedasticity test results. 7 Multiple regression results Regression results: coefficient, standard error, t and P (P>t), F-value and R-square. 8 Hypothesis testing Building, open space and micro-temperature change variables. 9 Forecasting Based on regression analysis results. Table 1.57: List of purpose of information and graphical presentation options. Source: Developed from research methods (2016). Item 1 2 3 4 5

Purpose of information

Graphic presentation option To describe entire object or situation. Photograph To present exact values, raw data, or data that Frequency distribution does not fit into any single pattern. table To dramatize differences, draw comparisons Bar chart and describe proportions To represent in two dimensions the relationship Scattergram between two variables To summarize trends, show interactions Frequency polygon between two or more variables, relate data to constants, or emphasize an overall pattern rather than specific measurements 336

Table 1.57: Continues. 6 7 8 9 10

To represent in three dimensions the relationship between three or more variables To show variable distribution patterns in concentric circles. To show three or more variables in interrelated scales and trends. To show location and distribution of dependent variable frequency. To summarize and display in diagrammatic terms trends, show interactions between two or more variables, relate data to constants, or emphasize an overall pattern and related to study results.

Hyperspace diagram Polar curve Nomogram Isotherm distribution map Diagrammatic table

summary

Table 1.58: Digital statistical tools, statistical data needs and analysis techniques. Source: Developed from research methods (2016). Item 1

2

3

Types of data needed Tabulate and organize data, fitting equations to data, interpolating between data points, solving single and multiple equations, finding optimum solutions, plot graphs, charts and diagrams, cut and paste data from spreadsheet to other software and platform for running Ebstats Software. Consolidated summary sheet, processed data analysis and data analysis summary. Summary statistics, Pearson correlation matrix, normality test (Shapiro-Wilk W test for normal data), multi-collinearity test (variance inflation factors: VIF), heteroscedasticity (BreuschPagan/Cook-Weisberg test for heteroskedasticity test results), multiple regression results, hypothesis testing and forecasting.

337

Digital statistical analytical tool Microsoft Excel

Ebstats Software

Stata Software

Table 1.59: Showing summarized analytical framework. Source: Developed from research methods (2010). Types of data:

Techniques of analysis:

Results expected:

Identification of urban built form variables causing temperature change Question 1: What are the urban built form variables which cause temperature change in Komarock Estate? Objective 1: To identify urban built form variables causing temperature change in Komarock Estate. Data related to Independent Variable X (Urban Built Form) and Dependent Variable Y (Microtemperature Change).

Inventory of the data collected was presented in the form of crosssectional data and summary statistics for building and open space variables.

Establish dependency ratio, potential and limits of utilization etc. and impact assessment of urban built form variables.

Determination of significant urban built form variables contribution to temperature change Question 2: What influence do the significant urban built form variables have in contributing to the temperature change? Objective 2: To determine the influence of the significant urban built form variables in contribution to temperature change. Population size, temperature changes, distribution, ratios, composition, growth, physical characteristics.

Data was analyzed using the Pearson correlation matrix for building and open space variables, Normality test using Shapiro-Wilk W test for normal data for building and open space variables, Multicollinearity test using variance inflation factors (VIF) for building and open space variables, Heteroscedasticity using BreuschPagan/Cook-Weisberg test for heteroscedasticity test results for building and open space variables, multiple regression results and hypothesis testing for building and open space variables.

338

Development of Predictive Nomograms, Frequency Polygons and Polar Curves.

Table 1.59: Continues. Development of design and planning strategies in view of sustainable urban built form in a temperature changing environment. Question 3: What is the impact of urban built form design and planning strategies on the temperature change? Objective 3: To develop design and planning strategies in view of sustainable urban built form in a temperature changing environment. Population size, Reflections on the results were Development of temperature changes, undertaken. Remedial Nomograms, distribution, ratios, Building Variables composition, growth Summary and Open planning variable. Space variable Summary.

339

APPENDIX 2 TABULATED DATA Table 2.1: Showing cluster description, plot and open space numbers. Source: Field survey (2015). Item/Description

Cluster numbers 1

Open Ground Basic Maisonette (N – S Orientation) Basic Maisonette (E – W Orientation) Basic Villa (N – S Orientation) Basic Villa (E – W Orientation) Corner Plot (Internally Located) Large Plot (Near Main Road) Transformed Plot Change of User Road (Vehicular Circulation) Paths (Pedestrian Circulation)

Plot and open space numbers

2

OG3, OG3, OG4, OG5, OG8, OG12, OG13, OG14, OG14. 137, 234, 237

3

74, 99, 125

4 5 6 7 8 10 11 12

41, 54, 133, 211, 225 48, 79, 109, 142, 233 34, 122 10, 16, 158, 164, 172, 180, 218 68, 71 19, 77, 220 R1, R9, R11, R15 P3, P6

Table 2.2: Shows the sampling technique used to sample building clusters. Source: Field survey (2015). Item

Description

Cluster numbers 2

Total

3

4

5

6

7

8

10

1

Total number of 15 plots in cluster

31

61

59

9

56

2

7

240

2

Proportion of 0.06 total number in cluster to total number of plots

0.13

0.25

0.25

0.04

0.23

0.01

0.03

1

3

Proportionate to 1.8 sample plots of 30

3.9

7.5

7.5

1.2

6.9

0.3

0.9

30

4

Rounding

4

8

7

1

7

0

1

30

2

340

Table 2.2: Continues. 5

Total sampled

plots 3

6

Shortfall (-ve) 1 or excess (+ve)

3

5

5

2

7

2

3

30

-1

-3

-2

1

0

2

2

0

Table 2.3: Shows the sampling technique used to sample open space clusters. Source: Field survey (2015). Item

Description

Cluster numbers

Total

1

11

12

14

15

12

41

1

Total number of open spaces in cluster

2

Proportion of total number in cluster to total 0.34 number of open spaces

0.37

0.29

1

3

Proportionate to sample open spaces of 15

5.1

5.5

4.4

15

4

Rounding

5

6

4

15

5

Total open spaces sampled

8

6

2

16

6

Shortfall (-ve) or excess (+ve)

3

0

-2

1

Table 2.4: Tabulated data showing collected data for building variables. Source: Field survey (2015). Item 1 2 3 4 5 6 7 8 9 10 11 12

Plot No. 237 234 137 74 99 125 41 211 133 54 225 48

X1 (Degree) 46 46 46 136 136 136 46 46 46 226 224 136

X2 (M) 36 36 24.5 18 153 43.5 42 36 30 42 36 39

X3 (M) 22 45.8 65.8 35.4 83.5 83.5 85.6 35.6 76 72.5 38.5 37.7 341

X4 (M) 7.7 7.7 7.7 7.7 7.7 7.7 5.3 5.3 5.3 5.3 5.3 5.3

X5 (M2) 108 108 101 108 108 108 108 108 108 108 108 108

X6 (%) 37 51 40 37 37 37 44 44 44 57 45 44

X7 (%) 71 84 75 71 71 71 44 44 44 57 45 44

Y (oC) 4.2 2.8 1.8 2 1.4 2.9 3.9 1.5 3.3 2.8 6.7 2.7

Table 2.4: Continues. 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Average

79 109 142 233 34 122 218 10 16 180 158 172 164 68 71 19 77 220

136 226 136 316 136 226 316 316 316 316 46 316 316 46 46 205 226 226 171.2

153 45.1 57.5 45 6 6.5 18 24 21 49.4 42 73.3 73.3 12 6 8.1 17.8 10 40.1

77.8 46.4 43 71.5 93.6 98.6 26.5 28.8 23.5 28.8 80.4 31 31 68.4 51.8 11.4 52 12.3 51.9

5.4 5.4 6.4 5.3 7.7 5.3 5.3 5.3 7.7 7.7 7.7 7.7 5.3 6.2 6.2 5.3 7.7 7.7 6.4

108 127 108 127 164.4 109 132 144 171.4 165 165 159 159 108 108 422.3 132.3 240 140

44 66 62 47 40 29 37 63 23 40 24 45 30 74 86 83 47 70 47.6

44 66 82 47 75 29 37 63 44 62 46 62 30 106 162 83 77 141 65.9

1.6 3.3 7.2 3.5 5.6 1.7 3.8 3.1 4.2 3.5 3 3.5 4.1 3 3.7 3.3 5.5 3.1 3.4

Note: Building orientation (X1), building classification (X2), road proximity (X3), building type (X4), plot size (X5), ground coverage (X6), plot ratio (X7) and micro-temperature change (Y).

Table 2.5: Tabulated data showing summary statistics for building variables. Source: Field survey (2016). Statistics Number Mean Standard deviation Range Skewness Kurtosis Coefficient of variation p25 Median

X1 30 171.2 104.3 270 0.13 1.61 0.61

X2 30 40.1 35.6 147 2.1 7.39 0.89

X3 30 52 25.6 87.2 0.21 1.76 0.49

X4 30 6.4 1.1 2.4 0.14 1.14 0.18

X5 30 140 62 321.3 3.4 15.7 0.44

X6 30 47.6 16.0 63 0.83 3.09 0.34

X7 30 65.9 29.6 133 1.6 5.85 0.45

Y 30 3.4 1.4 5.8 0.95 3.89 0.41

46 136

18 36

31 46.1

5.3 6.2

108 108

37 44

44 62.5

2.8 3.3

342

Table 2.5: Continues. p75 Sum

226 5135

45 1204

76 1558.7

7.7 192.8

159 57 4198.4 1427

75 1977

3.9 102.7

Note: Building orientation (X1), building classification (X2), road proximity (X3), building type (X4), plot size (X5), ground coverage (X6), plot ratio (X7) and micro-temperature change (Y).

Table 2.6: Tabulated data showing Pearson correlation matrix for building variables. Source: Field survey (2016). X1

X2

X3

X4

X5

X6

X1

1.0000

X2

0.0289

1.0000

X3

-0.3852

0.2182

1.0000

X4

-0.1526

0.0093

-0.0192

1.0000

X5

0.3146

-0.2587

-0.3852

-0.0499

1.0000

X6

-0.1046

-0.2495

-0.2565

-0.2884

0.3077

1.0000

X7

-0.2840

-0.2777

-0.1850

0.3323

0.1470

0.7252*

X7

1.0000

Note: * indicates significance at 1% (0.01), building orientation (X1), building classification (X2), road proximity (X3), building type (X4), plot size (X5), ground coverage (X6), plot ratio (X7) and micro-temperature change (Y).

343

Table 2.7: Normality test using Shapiro-Wilk W-test for normal data for building variables. Source: Field survey (2016). Variable Observation W V z Prob > z Building 31 0.98798 0.391 -1.943 0.97401 orientation Building 31 0.74738 8.229 4.367 0.00001 classification Road 31 0.94744 1.712 1.114 0.13260 proximity Building 31 0.95682 1.406 0.707 0.23987 type Plot size 31 0.60992 12.706 5.267 0.00000 Ground 31 0.92359 2.489 1.889 0.02943 coverage Plot ratio 31 0.84635 5.005 3.337 0.00042 Micro31 0.90843 2.983 2.264 0.01178 temperature change Table 2.8: Tabulated data showing multicollinearity test using variance inflation factors (VIF) for building variables. Source: Field survey (2016). Variable VIF Tolerance (1/VIF Building orientation 7.55 0.132533 Building classification 7.51 0.133092 Road proximity 3.71 0.269812 Building type 1.49 0.671102 Plot size 1.46 0.684923 Ground coverage 1.45 0.689552 Plot ratio 1.20 0.835603 Mean VIF 3.48 Table 2.9: Tabulated data showing heteroscedasticity using Breusch-Pagan/CookWeisberg test for heteroscedasticity test results for building variables. Source: Field survey (2016). Breusch-Pagan/Cook-Weisberg test for heteroscedasticity Ho: Constant variance Variables: Fitted values of temperature change Chi2 (1) = 0.71 and Prob > Chi2 = 0.3999 344

Table 2.10: Tabulated data showing multiple regression results for building variables. Source: Field survey (2016). Method: OLS Dependent variable: Micro-temperature change Coefficient Std. Err. Building 0.0033003 0.0031414 orientation Building -0.0110001 0.0082397 classification Road -0.0043829 0.0126472 proximity Building type 0.3522775 0.4545206 Plot size -0.0050629 0.0052329 Ground 0.0365329 0.0459403 coverage Plot ratio -0.0147858 0.0249051 Constant 1.208963 3.663526

t 1.05

P>t 0.304

-1.34

0.195

-0.35

0.732

0.78 -0.97 0.80

0.446 0.343 0.435

-0.59 0.33

0.559 0.744

F (7, 23) = 0.62 R-squared = 0.1583 Table 2.11: Statistical tests citation for building variables. Source: Field survey (2016). Item 1 2

3 4

5

6

Statistical test Pearson Correlation Matrix

Format r = 0.7252 α = 0.05 (Plot Ratio and Ground Coverage) z-test (Shapiro – Wilk W p > 0.01 (Building Orientation, Road Proximity, Test) Building Type, Ground Coverage and Microtemperature Change) p < 0.01 (Building Classification, Plot Size and Plot Ratio). Variance Inflation Factor VIF = 7.55 < 10 (Building Orientation) (VIF) Chi-square (Breusch – χ2 = 0.71 and p > χ2 = 0.3999 Pagan/Cook – Weisberg Test) p-values 0 < p < 0.49 (Building Orientation, Building Classification, Building Type, Plot Size and Ground Coverage) 0.5 < p < 1 (Road Proximity and Plot Ratio). t-test t(29) = 2.045, p < 0.05 345

Table 2.11: Continues. 7

Analysis of Variance F(7,23) = 0.62, p < 0.05 (ANOVA) (F-test Overall Significance) R-squared test R-squared = 0.1583

8

Table 2.12: Tabulated data showing t-test hypothesis testing of the true or population regression coefficient is zero results for building variables. Source: Field survey (2016). Variable name

df (n – k)

t

se(bk)

Computed bk

Building orientation Building classification Road proximity Building type Plot size Ground cover Plot ratio

29

1.05

0.0031414 0.0033

29

Critical t

2.045

Remark on null hypothesis Rejected

-1.34 0.0082397 0.01103

2.045

Rejected

29

-0.35 0.0126472 0.00442

2.045

Rejected

29

0.78

0.4545206 0.35453

2.045

Rejected

29 29

-0.97 0.0052329 0.00507 0.80 0.0459403 0.03675

2.045 2.045

Rejected Rejected

29

-0.59 0.0249054 0.01469

2.045

Rejected

Note: Critical t value at 0.05 significance level.

Table 2.13: Tabulated data showing collected data of open space variables. Source: Field survey (2015). Item

1 2 3 4 5 6

Open Space No. OG3 OG3 OG4 OG5 OG12 OG14

X8 X9 (Degree) (M)

X10 X11 (Degree) (%)

X12 (M)

X13 (M2)

X14 (%)

Y (oC)

46 226 46 46 136 46

85 85 87 90 90 80

45 45 39 19.1 43.6 43.5

822 822 1182 349 763 1838

68 68 65 50 67 49

3.8 2.4 2.6 3.8 4 3.3

91.9 97.1 75.4 24.9 95 80.4

346

81.8 81.8 86.6 45.9 29.4 48.8

Table 2.13: Continues. 7 8 9 10 11 12 13 14 15 16 Average

OG13 OG14 OG8 R1 R6 R9 R11 R15 P6 P3

46 316 226 136 226 136 316 46 316 226 158.5

76 31 38.5 40 50.1 83.5 32 31 50.5 74.5 60.7

86 88 87 80 82 80 80 80 55 55 80.6

65 49 27 58 40.4 39.3 35.5 22 0 51 47.6

30 53 36 23 18 67.9 45.1 48.5 39 42 39.9

738 1838 1071 276 270 815 541 582 117 126 759.4

80 49 75 42 40 50 50 50 100 100 62.7

4.1 3.9 7.5 2.9 5.5 1.6 2.9 3.4 2 6 3.7

Note: Open space orientation (X8), open space road proximity (X9), open space light angle (X10), open space shading coefficient (X11), open space length (X12), open space area (X13), open space hard landscape coefficient (X14) and microtemperature change (Y).

Table 2.14: Tabulated data showing summary statistics for open space variables. Source: Field survey (2016). Statistics X8 Number 16 Mean 158.5 Standard 106.5 deviation Range 270 Skewness 0.25 Kurtosis 1.6 Coefficient of 0.67 variation p25 46 Median 136 p75 226 Sum 2536

X9 16 60.7 25.9

X10 16 80.6 10.6

X11 16 47.6 23.4

X12 16 39.9 12.8

X13 16 759.4 525.9

X14 16 62.7 18.8

Y 16 3.7 1.53

72.2 0.01 1.43 0.43

35 -1.76 4.93 0.13

86.6 0.02 2.61 0.49

49.9 0.01 3.09 0.32

1721 0.84 2.997 0.693

60 0.81 2.65 0.2992

5.9 0.98 3.6 0.41

35.3 62.5 81.95 971.8

80 83.5 87 1290

32.5 47.4 61.5 761.5

33 42.8 45.1 637.7

312.5 750.5 946.5 12150

49.5 57.5 71.5 1003

2.75 3.6 4.05 59.7

Note: Open space orientation (X8), open space road proximity (X9), open space light angle (X10), open space shading coefficient (X11), open space length 347

(X12), open space area (X13), open space hard landscape coefficient (X14) and microtemperature change (Y).

Table 2.15: Tabulated data showing Pearson correlation matrix of open space variables. Source: Field survey (2016). X8

X9

X10

X11

X12

X13

X14

X8

1.0000

X9

-0.2949

1.0000

X10

-0.4001

0.0032

1.0000

X11

-0.4119

0.4775

0.3956

1.0000

X12

0.1352

0.3375

-0.0890

-0.0603

1.0000

X13

-0.1358

0.1620

0.4902

0.2762

0.4248

1.0000

X14

0.2050

0.3269

-0.6167

-0.1075

0.0681

-0.2797

1.0000

Note: * indicates significance at 1% (0.01), open space orientation (X8), open space road proximity (X9), open space light angle (X10), open space shading coefficient (X11), open space length (X12), open space area (X13), open space hard landscape coefficient (X14) and micro-temperature change (Y).

Table 2.16: Tabulated data showing normality test using Shapiro-Wilk W test for normal data for open space variables. Source: Field survey (2016). Variable Open space orientation Open space road proximity Open space light angle Open space shading coefficient

Observation W 16 0.96812

V 0.646

z -0.868

Prob > z 0.80732

16

0.89403

2.147

1.518

0.06453

16

0.74260

5.216

3.281

0.00052

16

0.96940

0.620

-0.950

0.82883

348

Table 2.16: Continues. Open space 16 0.93926 1.231 0.412 0.34001 length Open space 16 0.92113 1.598 0.931 0.17589 area Open space 16 0.90854 1.853 1.225 0.11022 hard landscape Micro16 0.91986 1.624 0.963 0.16784 temperature change Table 2.17: Tabulated data showing multicollinearity test using variance inflation factors (VIF) for open space variables. Source: Field survey (2016). Variable Open space orientation Open space road proximity Open space light angle Open space shading coefficient Open space length Open space area Open space hard landscape Mean VIF

VIF 1.45 2.19

Tolerance (1/VIF 0.688494 0.457488

2.56 1.82

0.390856 0.550668

1.81 1.92 2.04

0.553378 0.521564 0.489541

1.97

Table 2.18: Tabulated data showing heteroscedasticity using Breusch-Pagan/CookWeisberg test for heteroscedasticity test results for open space variables. Source: Field survey (2016). Breusch-Pagan/Cook-Weisberg test for heteroscedasticity Ho: Constant variance Variables: Fitted values of temperature change Chi2 (1) = 3.02 and Prob > Chi2 = 0.0825

349

Table 2.19: Tabulated data showing multiple regression results for open space variables. Source: Field survey (2016). Method: OLS Dependent variable: Micro-temperature change Coefficient Std. Err. Open space orientation Open space road proximity Open space light angle Open space shading coefficient Open space length Open space area Open space hard landscape Constant

t

P>t

0.001806

0.0050211

0.36

0.728

-0.0069516

0.0253656

-0.27

0.791

0.0400538

0.0667436

0.60

0.565

-0.0102052

0.0255198

-0.40

0.700

-0.0558715

0.046644

-1.20

0.265

0.0007021

0.0011682

0.60

0.564

0.0381042

0.033806

1.13

0.292

0.4285851

6.885985

0.06

0.952

F (7, 8) = 0.55

R-squared = 0.3250

Table 2.20: Tabulated data showing t-test hypothesis testing of the true or population regression coefficient is zero results for open space variables. Source: Field survey (2016). Variable name

df (n – k)

Open space 15 orientation Open space 15 road proximity Open space 15 light angle

t

se(bk)

Computed bk

0.36

0.0050211 0.00181

2.731

Remark on null hypothesis Rejected

-0.27 0.0253656 0.00685

2.731

Rejected

0.60

2.731

Rejected

0.0667436 0.04004

350

Critical t

Table 2.20: Continues. Open space 15 -0.40 0.0255198 shading coefficient Open space 15 -1.20 0.046644 length Open space 15 0.60 0.0011682 area Open space 15 1.13 0.033806 hard landscape Note: Critical t value at 0.05 significance level.

0.0102

2.731

Rejected

0.05597

2.731

Rejected

0.0007

2.731

Rejected

0.03819

2.731

Rejected

Table 2.21: Tabulated data showing inter-plot minimum building orientation variability findings. Source: Field survey (2017). Item

Plot No.

1 2 3 4 5 6 7 8 9 Total Average Range

237 234 137 41 211 133 158 68 71

Building orientation X1: Degrees 46 46 46 46 46 46 46 46 46 46

MicroAverage micro- Range: o temperature temperature C o change Y: change YAve: C o C 4.2 4.2 2.8 1.8 3.9 1.5 1.5 3.3 3 3 3.7 27.2 3.0 2.7

351

Comments

YMax

YMin

Table 2.22: Inter-plot maximum building orientation variability. Source: Field survey (2017). Item

Plot No.

Building orientation X1: Degrees 316 316 316 316 316 316 316

MicroAverage micro- Range: Comments o temperature temperature C o change Y: change YAve: C o C 1 233 3.5 2 218 3.8 3 10 3.1 3.1 YMin 4 16 4.2 4.2 YMax 5 180 3.5 6 172 3.5 7 164 4.1 Total 25.7 Average 316 3.7 Range 1.1 Table 2.23: Inter-plot minimum building classification variability. Source: Field survey (2017). Item

Plot No.

Building classification X2: M 6 6

Microtemperature change Y: oC 5.6 3.7 9.3

Average microtemperature change YAve: oC

Range: o C

1 34 5.6 2 71 3.7 Total Average 6 4.7 Range 1.9 Table 2.24: Inter-plot maximum building classification variability.

Comments

YMax YMin

Source: Field survey (2017). Item

Plot No.

1 99 2 79 Total Average Range

Building classification X2: M 153 153

Microtemperature change Y: oC 1.4 1.6 3

153

Average microtemperature change YAve: oC

Range: o C

Comments

1.4 1.6

YMin YMax

1.5 0.2

352

APPENDIX 3 TABLE OF DRAWINGS Table of drawings summary: Drawing 1

Consolidated batching summary sheet

Drawing 2

Batch 1: Plot Details

Drawing 3

Batch 2: Plot Details

Drawing 4

Batch 3: Plot Details

Drawing 5

Batch 4: Plot Details

Drawing 6

Batch 5: Plot Details

Drawing 7

Batch 6: Plot Details

Drawing 8

Batch 7: Plot Details

Drawing 9

Batch 8: Plot Details

Drawing 10

Batch 9: Plot Details

Drawing 11

Batch 10: Plot Details

Drawing 12

Batch 11: Plot Details

Drawing 13

Batch 12: Plot Details

Drawing 14

Batch 13: Plot Details

Drawing 15

Batch 14: Plot Details

Drawing 16

Batch 15: Plot Details

353