Unit 1 Foundations of GIS Assignment 2 TAA 2 ...

Assignment TAA2

Kishwar Ali_ Student ID:@00349476

Unit 1 Foundations of GIS

Assignment 2 TAA 2 Kishwar Ali Student ID: @00349476

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Assignment TAA2


Exercise 1 Working with Social and Census data Introduction GIS being a powerful database and data processor can connect various data sets of attributes data with the geometry data for visual display. It can link data from many sources and can produce high quality map displays for the ultimate aim of decision making in real world problems. The real world problems are often not very simple and straight forward but instead many factors work together to give rise to a particular event. Especially, the social structure of a society is dictated by the synergetic affect of various aspects of the society, i.e. economic, physical, and environmental etc. There is a very low probability that two communities in a society or two social settings are exactly the same. This is because of the presence of a universal imbalance in the society. Many researchers have investigated and identified a significant clear geospatial pattern in these socio-economic imbalances (see Gatrell & Elliott 2009, Wilkinson 2005). These inequalities may be responsible for some form of health issues, which can be directly or indirectly linked to the socio-economic factors (Marmot 2006). These relationships have also been found and reported in the recent National Audit Office report, stating that there is a seven years of difference in the life expectancy of poor and rich neighbourhood. The rich live longer that the poor (National Audit Office 2010, online: http://www.nao.org.uk/publications/1011/health_inequalities.aspx). Similar changes were also highlighted by Brunner & Marmot (2006:9) to be linked spatially.

In this exercise, GIS has been used to create map outputs to indicate the relationship of social factors and spatial variations in the district of Eden. For this purpose, data from many different sources was collected and related in GIS to find out any links between social factors and the spatial variation. Census statistics and boundary data were downloaded from CASWEB and UKBORDERS, while Public health data was obtained from Northwest Public Health Observatory. In this exercise health data was only restricted to Coronary Health Disorders data for the district of Eden. The main aims of the exercise is to (i) determine the spatial patterns of health experiences (and their inequalities) across Eden, and (ii) explore the linkages between these socioeconomic ‘determinants’ and health outcomes in the region by presenting graphical and cartographic outputs.

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Material and Method GIS was used as a tool for understanding the correlation between Coronary Health Disorder and the social inequalities in Eden district. A basic workflow diagram is given in Figure 1.

Figure 1

Workflow diagram for exercise 1

Data acquisition The data for the exercise was obtained from CASEWEB (Census 2001data) at Output Area level for the region, which was later converted to Middle Super Output Area (MSOA) level

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using the spatial join function, and UKBORDERS (EDINA) websites using UK Federation authentication Athens system. The census data for socioeconomic indicators was carefully selected to fulfil our needs of the correlation with the health data (see Table 1).

Table 1 Census-based socioeconomic indicators. S no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Data Name KS0010001 KS0010002 KS0010003 KS0020001 KS0020013 KS0020014 KS0030001 KS0030002 KS0030003 KS0030004 KS0070001 KS0070002 KS0070003 KS0070004 KS0070005 KS0070006 KS0070007 KS0070008 KS0070009 KS09A0001 KS09A0012 KS09A0013 KS09A0014 KS09A0015 KS14A0001 KS14A0009 KS14B0001 KS14B0009 KS0190001 KS0190004 KS0220001

Data Details All People (Residents) All Males All Females All People (Age Structure) People Aged 60-64 People Aged 65-74 All People Aged 16 or over in Household All Married People All Cohabiting People All Single People All People (Religion) All People of Christian Faith All People of Buddhist Faith All People of Hindu Faith All People of Jewish Faith All People of Muslim Faith All People of Sikh Faith All Other Religions/Faiths All People with No Religion All People (Economic Activity) All People Aged 16-24 Unemployed All People Aged 50+ Unemployed All People Who Have Never Worked All People Who Are Long Term Unemployed All People Aged 16-74 (Socioeconomic Classification) All People in Routine Occupations All Males Aged 16-74 (Socioeconomic Classification) All Males in Routine Occupations All Households (Over occupancy) Occupancy -1 or less (Over occupied) All Lone Parent Households

These data can be accessed from the following websites: UKBORDERS data service which is run by EDINA on behalf of all higher education institutions (HEIs) in the United Kingdom. http://edina.ac.uk/ukborders/ 4

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and the Census data can be obtained from the following websites: http://casweb.mimas.ac.uk/ Health statistics data was supplied by the unit administrator as CSV (Comma Separated Values) file named CHD downloadable from the UNIGIS Moodle site. This data have already been downloaded from the NWPHO at Middle Super Output Area (MSOA) level – and optimised for the exercise. For this exercise, optimization was based on the incidence of hospitalisation associated with Coronary Heart Disease. with a figure of 100 showing an average number of hospitalisations associated with Coronary Heart Disease – figures above 100 indicate above average numbers of hospitalisation events (i.e. poorer health than the average), and figures below 100 are demonstrative of better health than average using this indicator.

GIS analysis GIS ArcMap was used to carry out the processing of correlations and results display. Firstly, the different data sets were added to ArcMap and the joined and related using the join and relate tools in the software. Similar field names were used for these joins from the attributes tables of the data. This action has populated our Cumbria dataset, which provided the bases for the extraction of our Eden district dataset. Using further spatial join function, the data have been allocated to MSOA level. Now using the select by attribute function and the use of SQL statement, the district of Eden was selected and assigned as a new layer. The data was exported and saved as a separate database file. After assigning the events on the Eden district the shape file of Eden_OA was obtained. The health data was then linked using join and relate tools as used previously for the Cumbria datasets. The New Eden Health layer was obtained which was later on exported and saved as separate shape file. Results The Eden shape file was further used for calculating the percentage of different socioeconomic variables for comparison with the health data. This was carried out in the Field calculator tool of the ArcMap. The following new variables were calculated:

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Assignment TAA2 Table 2


Socioeconomic variables for comparison with health data

Attribute

Definition

OAPPERC

Percentage (%) of population aged 60-74

MARRPERC

Percentage (%) of people married or cohabiting in households

SINGPERC

Percentage (%) of single people in households

RELIGPERC

Percentage (%) of people with religious faith

NORELPERC

Percentage (%) of people with no religious faith

UNEMPLOY

Percentage (%) of people who are unemployed

LOWSEP

Percentage (%) of people in routine occupations

LOWSEPMAN

Percentage (%) of males in routine occupations

OVEROCCUP

Percentage (%) of households that is over occupied

LOANPERC

Percentage (%) of lone parent households

The new variables provide a good insight of the percentages of variation on the spatial scale. These were further classified and maps were drawn using the symbology options in the ArcMap. The results for Eden indicate that there is a variation in the distribution of population aged between 60-7, highly concentrated in the eastern parts of the district See Map 1.

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Map 1


Percentage of Population aged 60-74 in Eden

Map 2 shows that in the centre of Eden, there is a small area where households are over occupied while; the north-western parts are relatively less occupied than the eastern and southern areas. Maps similar to Map1 and 2 were created but will not be discussed here, as they are beyond the needs of the current exercise.

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Map 2. Percentage of households which are over occupied

The Figure 2 is a scattered graph created between the variables of Coronary Heart Disease and Over-occupancy of the households. This graph clearly indicates a direct link between the percentage of household occupancy and Coronary Heart Disease. It is obvious from the graph that the highest incidents (over 125 reports) of the Coronary Heart Disease were recorded for the percentage value of 8 of the over-occupancy (see Figure 2). 8

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Figure 2


Scattered Graph between Over-occupancy of the households and CHD.

The Figure 3 although suggests that the highest number of CHD is still recorded for the highest value of unemployed people but the moderate and low number of the disease incidence can also be encountered in the employed population.

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Figure 3


Unemployment vs CHD in Eden

Conclusions and Discussion It is very evident from the practical example that a clear link exists between the socioeconomic factors and health issues in the UK. Using the GIS technology, it was revealed that social factors like over-occupancy can lead to a poor life style which eventually can lead to poor health conditions. It is confirmed that GIS use for the analysis of complex spatio-temporal events can be very easy and straightforward. Complex patterns can analysed with simple functions in the GIS environment and quick decisions can be obtained in no time. References Journal of Health Geographics, Health and Place, Social Science and Medicine. Your discussion should attempt to evaluate the role of the different socioeconomic determinants on coronary heart disease in Cumbria. Some useful starter papers include: Sundquist, J., Johansson, S.E., Yang, M. & Sundquist, K. (2006) Low linking social capital as a predictor of coronary heart disease in Sweden: A cohort study of 2.8 million people. Social Science and Medicine 62:954-963. 10

Assignment TAA2


Uutela, A. & Tuomilehto, J. (1992) Changes in Disease Patterns and Related Social Trends. Social Science and Medicine 35(4):389-399. Wamala, S.P. Mittleman, M.A., Horsten, M., Schenck-Gustafsson, K. & Orth-Gomer, K. (2000) Job stress and the occupational gradient in coronary heart disease risk in women – The Stockholm Female Coronary Risk Study. Social Science and Medicine 51:481-489.

Acknowledgements 

This work is based on data provided through EDINA UKBORDERS with the support of the ESRC and JISC and uses boundary material which is copyright of the Crown.



The census data have been captured from the CASWEB website. The following statement must appear on any maps or documents which use this boundary dataset. Census output is Crown copyright and is reproduced with the permission of the Controller of HMSO and the Queen's Printer for Scotland Source: 2001 Census; District Boroughs of Newcastle and Sunderland.



The chronic heart disease data have been captured from the NWPHO health profiler website

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Exercise 2 Using ArcGIS’s Model Builder in environmental analysis Introduction GIS acts as a useful tool for data capturing and processing. It has a variety of functions and tools available to perform geo-processing. Sometimes, to generate a simple map, one has to go through multiple geo-processing tasks and in a repetitive manner. This would require long time for processing if performed individually. But GIS has also the ability to perform multiple tasks in a sequential manner as models. This not only reduces time but the models can be stored and edited later on for other uses. ArcGIS has a tool called ModelBuilder which allows the user to combine multiple spatial operations, or commands, into a visual programming language (Allen 2011). In the current exercise different spatial operations will be performed to generate an index or measure of urban environmental quality in Gateshead and Sunderland districts – after Gunawan & Armitage (2011). Different diagrammatic models will be developed combining these commands and operations using ArcGIS’s ModelBuilder (Allen 2011). The current practical exercise will explore and carry out multiple tasks to evaluate environmental quality for the districts of Sunderland and Gateshead of the Tyne and Wear County. Writing up the practical You are expected to write up the results of this practical exercise in report format including: A background section which sets the scene, introducing the major concepts and theories involved, and outlines the primary aims of the exercise; A description of methods used in the practical exercise – best supported by means of a workflow or cartographic model; The results (including maps) of the different tasks; A brief discussion (including answers to any direct questions set), and; A list of references (of research and papers cited within the report). Note that this must be achieved within the page limit stated on the assignment cover sheet. Learning outcomes On completion of this practical exercise you should be able to: Access and download census statistics and boundary data from the CASWEB, DIGIMAP, and UKBORDERS services respectively; Perform basic processing of satellite imagery using ArcGIS; Utilise a range of geoprocessing tasks in ArcGIS; Create automated models for the processing of geospatial data in ModelBuilder, and; Create map outputs from ArcGIS to be included in your written report.

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GIS as a tool for Urban Environmental Quality evaluation The growing population of the earth and the improvement of quality of life for the human being has put an enormous pressure on the urban ecosystem. It was the top of the agenda of the urban planners to provide sounds living areas to the people while not compromising green spaces and the quality of air (Gunawan & Armitage 2011). There are some recent studies for example Gunawan & Armitage (2011) describing how technology especially, GIS can be used to measure the environmental quality. There are various indices which can provide direct or indirect measure of the environmental quality. The common indices used for this measure are given in Table 1.1. Table 1.1 Urban environmental quality indicators as used by Gunawan & Armitage (2011)

Indicator NDVI

Description The normalised difference vegetation index (NDVI) is a measure of vegetation abundance. It is often more simplistically described as a greenness measure. It is calculated using a combination of the Red and Near Infrared wavebands from a remote sensing system. In this case it is calculated using the Landsat 1 = Minimum NDVI value 10 = Maximum NDVI value The higher the value the greater the amount of greenness available and therefore a healthier and better environment

NDBI

The normalised difference built-up index (NDBI) is sensitive to the presence of buildings and features which are found in more heavily built-up environments. This index is a measure of the reflectivity of urban buildings and is calculated using the Mid Infrared and Near Infrared wavebands from a remote sensing system. In this case it is calculated using the Landsat TM sensor system. MIR – NIR / MIR + NIR Building Height Building height information as calculated from LiDAR imagery Building Density/Volume Volume of living space per unit of land area generated from LiDAR imagery Surface Temperature Calculated from the Thermal Infrared waveband of the Landsat TM sensor 1 = Maximum NDBI value 10 = Minimum NDBI value The higher the value the greater the density of the built-up environment and the poorer the potential environmental quality

Population Density 1 = Maximum Population Density value 10 = Minimum Population Density value The higher the population density the greater the number of people per household and with a greater potential for overcrowding the poorer the potential environmental quality

Living Environment

10 = Minimum Living Environment Deprivation value 1 = Maximum Living Environment Deprivation value The greater the deprivation of the living environment for households the poorer the potential environmental quality

Distance from Roads

1 = Minimum Distance from A Roads 10 = Maximum Distance from A Roads The closer to roads the greater the likelihood of noise pollution and traffic accidents and therefore the poorer the potential environmental quality

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Assignment TAA2 Distance from Water

Kishwar Ali_ Student ID:@00349476 1 = Maximum Distance from Rivers 10 = Minimum Distance from Rivers The closer to rivers the better for providing healthy recreational space and therefore the better the potential environmental quality

Material and Method A stepwise methodology is described below.

Data acquisition Data for the practical was obtained from web resources and some pre-processed data was provided by the course administrations via the Moodle site. Raster and vector data sets were used for the two districts of Tyne and Wear in the northeast of England i.e. Gateshead and Sunderland. The Lower Output Area(LSOA) level vector data was acquired for roads and networks. The LSOA was obtained from the web resources as in the previous exercise i.e. UKBORDERS digital boundary dataset service: http://edina.ac.uk/ukborders/ While census variable – that of total population – at the LSOA level was downloaded for Sunderland and Gateshead from the following URL: http://casweb.mimas.ac.uk/ The downloaded files were saved as CSV files and were further processed for utilization in ArcGIS. The Raster and Vector data supplied in given in Table 1.2): Table 1.2 Pre-formatted data provided Raster

Vector

NDVI.img

Living_Env.csv

NDBI.img

Rivers.shp

Colour_stack.img

Roads.shp

ArcMap processing of the Data In the first step the Living_Env.csv data was joined with the LSOA boundary (geometry) layer for the study area. This was done by clicking on the the ModelBuilder button from the toolbar and dragging the Add Join (Data Management) tool – indicated by the small hammer symbol. The data was joined using suitable id for the items in the attribute table. By running the model, the resulting attribute table was updated with the new data. Connect button is another of the useful tools in the modelbuilder menu, which was used to link different processes in the model. By right clicking on the model output and choosing Add to Display option has displayed the newly created layer in the table of contents. New Models were saved by using the catalogue windows for choosing the appropriate place for the save. The Newly formed LSOA.shp layer was further updated with Living_Environment_Score and Living_Environment_Rank with another join function.

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By using calculating Geometry tools, area column was added to the attribute table of the main LSOA_2012.shp file. Later on Population density was added using field calculator function. At this time some of the field names in the attribute table were already truncated into shorter versions. Sunderland district was first selected for further analysis following the unit notes. The LSOA_2012.shp file provided the bases for further analysis. Sunderland was selected by using the Select Features by Rectangle and Identify function. After the data export for Sunderland district, raster images (NDVI, NDBI, and colour_stack) were added to the display. In the modelbuilder window Extract by Mask function was added. At this stage some of the processes were carried out outside the model as per recommendations for this unit. Using the Sunderland district shape the road and river network data was clipped to the LSOA_2012.shp layer and was later used to generate raster surfaces showing (i) population density, (ii) living environment quality, (iii) distance from roads, and finally (iv) distance from rivers. Reclassification of the data sets were carried in order to get a synchronised classification of the data for all the layers. The Reclassify (Spatial Analyst) tool was used in the model for a quick reclassification process. The 10 class categories and the Natural Breaks classification method was employed and the values were set according to the Table 1.3. The data sets for Living environment, Population Density were transformed into raster values by using Feature to Raster tool in the Model window. And finally all the input rasters were added to the Index raster output which was in fact the Urban Environmental Quality Index. The same reclassification procedure was applied for this output which is : all the raster grids will be converted into a common classification system of 10 class categories using the Natural Breaks (Jenks) classification system. A value of 10 should indicate the best or ideal characteristics of that particular variable or theme. Table 1.3 Classification categories for the urban environmental quality indicators Variable Classification

Indicator NDVI

Description 1 = Minimum NDVI value 10 = Maximum NDVI value The higher the value the greater the amount of greenness available and therefore a healthier and better environment

NDBI

1 = Maximum NDBI value 10 = Minimum NDBI value The higher the value the greater the density of the built-up environment and the poorer the potential environmental quality

Population Density

1 = Maximum Population Density value 10 = Minimum Population Density value The higher the population density the greater the number of people per household and with a greater potential for overcrowding the poorer the potential environmental quality

Living Environment

10 = Minimum Living Environment Deprivation value 1 = Maximum Living Environment Deprivation value The greater the deprivation of the living

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Assignment TAA2

Kishwar Ali_ Student ID:@00349476 environment for households the poorer the potential environmental quality

Distance from Roads

1 = Minimum Distance from A Roads 10 = Maximum Distance from A Roads The closer to roads the greater the likelihood of noise pollution and traffic accidents and therefore the poorer the potential environmental quality

Distance from Water

1 = Maximum Distance from Rivers 10 = Minimum Distance from Rivers The closer to rivers the better for providing healthy recreational space and therefore the better the potential environmental quality

The Index was actually the result of an Overlay (tools). For this exercise the Weighted Sum (Spatial Analyst) process was chosen and added to the model. The Final Model and output Index map looked liked as follows:

Results and Discussion The resulted maps provide a good insight of the environmental quality assessment. The areas with hot colours are under greater anthropogenic stresses (see figure

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Figure 1.1


Environmental Quality assessment Index for Sunderland

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Figure 1.2


Full extent of Environmental Quality Assessment Model for Sunderland

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Figure 1.3


Sunderland: Population Density

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Figure1.4

Gateshead: Full extent of the Model

Figure 1.5

Gateshead NDBI (Reclassified)

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Assessed Task Using a modified model based on the work of Gunawan & Armitage (2011) you have generated a quantitative Environmental Quality Assessment index for the district of Newcastle upon Tyne. You will now recreate the index for the districts of (i) Gateshead, and (ii) Sunderland. Complete the following activities: Build and implement a model to quantitatively evaluate the environmental quality in both Gateshead and Sunderland districts. Keep the outputs from each district separate like the Newcastle upon Tyne exercise completed above. Export the ModelBuilder output(s) from either the Gateshead or Sunderland tasks (only one is necessary to remove any repetition in the report write up) as a cartographic model illustrating the different stages of geoprocessing and workflows to complete this exercise. Create a map presentation for each separate district (Gateshead and Sunderland) showing the output of the quantitative urban environmental quality assessment including appropriate cartographic elements. Critically reflect on the model you have created and its outputs, making sure to address the following points: Can you see any room for improvements? Are there any redundant or repetitive elements to the model? What other datasets do you think would have added value in such an assessment and why? What advantages might weighted overlay offer over the Boolean overlay methods used in this assessment?

Acknowledgements For the datasets: The boundary data was provided by EDINA’s UKBORDERS data service. The following statement must appear on any maps or documents which use this boundary dataset, which is derived from the 2001 Census digital boundary data: This work is based on data provided through EDINA UKBORDERS with the support of the ESRC and JISC and uses boundary material which is copyright of the Crown. The census data have been captured from the CASWEB website. The following statement must appear on any maps or documents which use this boundary dataset. Census output is Crown copyright and is reproduced with the permission of the Controller of HMSO and the Queen's Printer for Scotland Source: 2001 Census; District Boroughs of Newcastle and Sunderland. The chronic heart disease data have been captured from the NWPHO health profiler website The OS Strategi 1:250,000 scale data was provided by EDINA’s DIGIMAP data service. Use of this data is made possible through the OS OpenData product service. Use of OS OpenData is subject to the terms at http://www.ordnancesurvey.co.uk/oswebsite/opendata/docs/os-opendatalicence. pdf 21

Assignment TAA2


The English Indices of Deprivation 2010 were constructed by the Social Disadvantage Research Centre at the Department of Social Policy and Social Work at the University of Oxford. The Landsat TM data have been downloaded from the GLOVIS website (http://glovis.usgs.gov/). The following statement must appear on any maps or documents which use this imagery. Landsat TM 5 imagery courtesy of the U.S. Geological Survey

References Brunner, E. & Marmot, M. (2006) Social organization, stress, and health. In M. Marmot & R.G. Wilkinson (Eds.) Social Determinants of Health. Second edition pp:630. OUP: Oxford. Gatrell, A.C. & Elliott, S.J. (2009) Geographies of Health: An Introduction. Second edition. Wiley-Blackwell: Oxford. Marmot, M. (2006) Introduction. In M. Marmot & R.G.Wilkinson (Eds.) Social Determinants of Health. Second edition pp:1-5. OUP: Oxford. Wilkinson, R.G. (2005) The Impact of Inequality: How to make sick societies healthier. Routledge: Abingdon. Allen, D.W. (2011) Getting to Know ArcGIS: ModelBuilder. Redlands: ESRI Press. Gunawan, O. & Armitage, R.P. (2011) Measuring Urban Environmental Quality across Salford using an integrated Geographic Information Systems and Remote Sensing Approach. Proceedings of the 19th GIS Research UK Annual Conference, 27-29 April, Portsmouth, UK.

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