Predicting Deprivations in Housing and Basic Services from Space A Pilot Study in Slums of Dhaka, Bangladesh
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Acknowledgements
The work presented in this report was conducted as a collaboration between the World Bank’s Global Water Practice (Luisa M. Mimmi and Christian Borja-Vega), the University of Massachusetts Boston (Prof. Amit Patel, Tanushree Bhan, Hyun Jung Lee and Marcia Mundt) and GiSat/ESA-EO4SD (Tomas Soukup and Jan Kolomaznik). The team worked under the guidance of R. Mukami Kariuki and William Kingdom with support from Amanda J. McMahon Goksu, Swati Sachdeva, Lizmara Kirchner and Clementine Stip. Crystal Fenwick edited the final paper. The team is especially thankful to George Joseph, Sabrina Haque and Sophie Ayling for sharing survey data collected in Dhaka. Throughout the design and implementation stages, the study benefited from comments and feedback from Victor Vergara, Gayatri Singh, Ellen Hamilton, Phoram Shah, Nancy Lozano Gracia, Nagaraja Rao Harshadeep, Chris Heymans, Oscar A. Ishizawa, Aleix Serrat-Capdevilla and David Locke Newhouse. Alvaro F. Barra, Jon Exel, and Balakrishna Menon Parameswaran greatly supported the ongoing dialogue with other Global Practices, while Christoph Aubrecht helped establishing the collaboration with the European Space Agency (ESA). We are also grateful for comments received from participants at the presentation session held on September 14th, 2017 in the World Bank in Washington D.C. Advanced expertise in satellite-based urban geospatial products, image processing and GIS analysis for the study was provided under the ongoing partnership between the World Bank and the European Space Agency on Urban Development within the EO4SD (Earth Observation for Sustainable Development) initiative. EO4SD is the ESA initiative for large-scale exploitation of satellite data in support of international sustainable development activities in selected thematic areas. The EO4SD Urban Development Service Cluster, focused on utilization of EO-based urban products and services for sustainable urban planning, is composed of the following European companies: GAF (lead), GISAT, SIRS, EGIS, DLR, NEO, JOANNEUM RESEARCH and GISBOX. © 2018 International bank for Reconstruction and Development/International Development association The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: ww.worldbank.org
This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.
Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to the Office of the Publisher, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202- 522-2422; e-mail:
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Photo Credits Photos have been sourced from the following locations with full rights: World Bank Flickr Website
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Contents ACKNOWLEDGEMENTS ........................................................................................................ 2 CONTENTS ............................................................................................................................ 3 LIST OF ACRONYMS .............................................................................................................. 4 EXECUTIVE SUMMARY .......................................................................................................... 6 1. INTRODUCTION .............................................................................................................. 15 2. PREDICTING HOUSING DEPRIVATION FROM SPACE ........................................................ 20 3. GOALS AND OBJECTIVES ................................................................................................. 26 4. CASE STUDY OF DHAKA: CONTEXTUAL OVERVIEW ......................................................... 29 5. DATA .............................................................................................................................. 33 6. METHODOLOGY.............................................................................................................. 39 7. ANALYSIS AND RESULTS .................................................................................................. 49 8. PLANNING AND POLICY IMPLICATIONS........................................................................... 53 9. LIMITATIONS AND WAY FORWARD................................................................................. 55 10. CONCLUSION ................................................................................................................ 57 ADDITIONAL GRAPHS ......................................................................................................... 58 ANNEXES ............................................................................................................................ 60 Annex A: Housing Deprivation Measurements ....................................................................................... 60 Annex B: Earth Observation Data on Slums ............................................................................................ 66 Annex C: Regression Modeling Results ................................................................................................... 79
ANNEX D: WORLD BANK PROJECTS USING EO DATA ANALYSIS........................................... 89 REFERENCES ....................................................................................................................... 93
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List of Acronyms AGWG
Analytics and Geospatial Working Group
BBS
Bangladesh Bureau of Statistics
BUISBS
Bangladesh Urban Informal Settlements Baseline Survey
BWPD
Bangladesh WASH Poverty Diagnostic
CUS
Centre for Urban Studies
DEM
Discrete Element Method
DN
Digital Number
DNB
Day-Night Band
DWASA
Dhaka Water Supply and Sewerage Authority
EAP
East Asia Pacific
EIU
Economist Intelligence Unit
EO
Earth Observation
EO4SD
Earth Observation for Sustainable Development
ESA
European Space Agency
GeoCoP
Geospatial Community of Practice
GIS
Geographic Information Systems
GoB
Government of Bangladesh
GOST
Geospatial Operational Support Team
GP
Global Practice
GWPD
Global WASH Poverty Diagnostic
HIES
Household Income Expenditure Survey
HR
High-Resolution
IT
Information Technology
IFI
International Financial Institution
LULC
Land Use / Land Cover 4|Page
MDGs
Millennium Development Goals
MMU
Minimum Mapping Unit
NGO
Non-governmental Organization
NIPORT
National Institute for Population Research and Training
OBIA
Object-Based Image Analysis
OSM
OpenStreetMap
PCA
Principal Component Analysis
PSU
Primary Sampling Unit
PUMA
Platform for Urban Management and Analysis
RS
Remote-Sensing
SDGs
Sustainable Development Goals
SMA
Spectral Mixture Analysis
SMS
Short Message Service
SSI
Slum Severity Index
UN-Habitat
United Nations Human Settlements Program
UNICEF
United Nations International Children's Emergency Fund
VHR
Very High-Resolution
VIIRS
Visible Infrared Imaging Radiometer Suite
WASH
Water, Sanitation, and Hygiene
WBG
World Bank Group
WHO
World Health Organization
WSS
Water Supply and Sanitation
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Executive Summary 1.
Background
Setting One of the most pressing development challenges identified by the Water Global Practice (GP) is how to respond to the unmet demand for water supply and sanitation services in the context of rapid urbanization. Half the world’s population currently lives in cities and close to one billion live in slums or informal settlements. Moreover, megacities in developing countries have been growing at a faster pace than ever, mostly in an unplanned manner, and the proliferation of slums is the visible manifestation of this rapid, yet ungoverned urban growth. Against such a daunting backdrop, cities have been struggling to deal with the pressures that rising populations pose on infrastructure, basic services, land, and housing–not to mention the environment. More so than in other regions, South Asia’s urban areas fail to adequately cope with their rising populations. According to the Global Livability Ranking1 that evaluates cities from around the world across five dimensions–stability, healthcare, culture and environment, education, and infrastructure–many of South Asia’s large cities (such as New Delhi, Mumbai, and Karachi) ranked within the bottom 20 in 2016. Dhaka, the focus of this project’s case study, ranked 137 out of 140 cities (only ahead of Lagos, Tripoli, and Damascus), largely due to its congestion, poor infrastructure, and provision of basic services. The burden of such poor living conditions is particularly heavy for the most vulnerable, namely minorities, migrants, low-income, and near-water residents. The sheer magnitude and complexity of the problem is not easy to tackle. Many of the interventions for upgrading slums reviewed for this project, including a number of World Bank Group (WBG) initiatives, appeared either too small or too narrowly defined to identify areas where basic services were unmet and failed to yield “citywide” livability improvements. In the Water GP, the notion of citywide has been central to recent work on sanitation. In this context, citywide has come to mean stretching across different spatial locations and different dimensions of urban life (and consequently deprivation), such as housing, land, services, security of tenure, and urban planning, especially for large metropolitan cities.
Dhaka, Bangladesh–Despite a significant reduction of poverty over the past years, Bangladesh remains one of the poorest countries in Asia2. Bangladesh has a national
1 Published annually by the Economist Intelligence Unit (EIU), http://www.eiu.com/topic/liveability. 2 Immediately following the war for independence in 1971, the country was confronted with dire development challenges. Nonetheless, the country has made remarkable improvements: Between 2000 and 2010, Bangladesh’s GDP grew by about 6 percent per year, and the country saw steady decline in its national poverty rates. Despite
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poverty headcount rate of 31.5 percent and an extreme poverty headcount rate of 18.5 percent3. The country has one of the world’s highest population densities wherein 161 million people live in less than 150,000 sq km. Additionally, Bangladesh is vulnerable to flooding, earthquakes, and cyclones4. The latest slum census (conducted in 2014) and a few other empirical studies on the slums of Dhaka suggest large disparities exist in access to and quality of basic services when compared to non-slum urban areas. For instance, access to water in slums is similar to coverage in other urban areas, but the percentage of shared water sources is almost double that of a non-slum urban area. Similarly, most residents in the slums of the Dhaka City Corporation (DCC) share sanitation facilities with over 10 households. Key Challenges One of the key challenges facing cities today is the lack of data on the dynamics of urbanization in general and on the dynamics of slums in particular. As pointed out by Ellis & Roberts (2016, p. 25), in many South Asian cities, even the administrative boundaries of a city or the spelling of the name of neighborhoods are far from clearly and consistently defined, let alone slums and informal settlements that are even more elusive. For example, for most cities, a comprehensive list of slum communities does not exist: the very definition of slum–as a geographical entity that could be covered by a census–is very subjective and context-specific. Even in the rare cases when a population census explicitly includes slum dwellers (Bangladesh is one such country), the resulting limited and sometimes arbitrarily selected samples can make inferred population parameters somewhat problematic. For example, several NGOs in Bangladesh contested the figures reported in the latest census of slums and floating populations conducted by the Government of Bangladesh (GOB) in 2014 on the basis of definitions used5. Lack of a consistent definition and enumeration of slums could have serious implications for urban planning and policymaking in general and designing specific slum improvement projects in particular. Another problem relates to moving beyond the slum/non-slum dichotomy to identify the most deprived slums to ensure interventions target the most underserved households. Furthermore, two slums, a 30-year-old, quasi-formal urban settlement and a newly formed settlement with no services at all, are likely to be very different in terms of their deprivations and yet differences between slum communities are rarely evaluated despite the serious implications for slum improvement projects, urban planning and policymaking.
population growth, the absolute number of Bangladeshis living below the national poverty line has decreased dramatically from 64 million to 48 million. 3 2010 Household Income Expenditure Survey (HIES). 4 World Development Indicators, (2015). See annex for additional statistics. 5 See a story about this in The Daily Observer (2015), “BBS census of slum dwellers under fire from NGOs” available at: http://www.observerbd.com/2015/07/09/98880.php.
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Finally, a significant fraction of slum upgrading6 initiatives implemented over the last two decades across different countries were found to have mixed results or to have failed to scale up from pilot projects to the intended level of citywide impact. A detailed review of these initiatives7 found identifying the most deprived slums and targeting interventions in these areas were most often the weakest links. In most cases, it was hard to pinpoint the explicit reasoning and justification, much less develop a convincing case, driving the choice of beneficiary communities for the interventions. Site selection and prioritization appeared to be conducted without a systematic process or proper justification. These choices should be driven by evidence, especially when resources are limited as is the case in developing countries. New Approaches In the face of such an extremely complex problem, there are significant gaps in data that make it difficult to design projects and develop targeted strategies that are systematic, comprehensive, and transparent. Such data are essential to any city wanting to formulate effective interventions, programs, and policies, and especially so in resource poor countries. Understanding slum deprivation on a household scale would normally require resource-intense, cost-prohibitive fieldwork. However, there is a growing potential for some of these gaps to be filled by leveraging more cost-effective Earth Observation (EO) datasets and advanced remote sensing and GIS techniques, in particular, Very High-Resolution (VHR) satellite imagery. VHR EO imagery has been extensively used in the last fifteen years, and image classification algorithms are now capable of identifying unique areas within a city, such as slums, and capturing their morphology with impressive accuracy. However, only a few studies have used EO data to predict access and quality of basic services such as water and sanitation in slums. Furthermore, a 15-year review (2000-2015) of slum mapping using remote sensing information conducted by Kuffer et al. (2016) found a strong need to identify “contextual slum typologies” in addition to mapping slums. One approach to identifying slum areas and contextualizing slums is through the integration of “imagebased information with socioeconomic characteristics” which they suggest, “may ultimately lead to a better targeting of pro-poor policy interventions” (p. 474).
6
In this report, ‘slum upgrading’ refers to any measure that improves the physical, economic, and social environment of slum dwellers as a way to integrate informal areas into the city. Approaches span upgrading the physical infrastructure in slums to addressing social and economic problems to regularizing land tenure of the settlements and associated land parcels. 7 A recent World Bank paper on the “Inclusive Cities Approach” reviews and discusses the portfolio of slum upgrading operations supported by the World Bank–corresponding to an estimated total lending of US$2.5 billion from 19722013 (GSURR, 2015). The World Bank also compiled a multimedia sourcebook (Mehta & Dastur, 2008) and an eLearning course on Upgrading Urban Informal Settlements (Project P133055) that offer a rich and pragmatic discussion on a broad array of approaches to slum upgrading and inclusive city planning. Both sources emphasize the importance of acknowledging the complexity of slum approaches and give pragmatic suggestions to adhere to the local institutional framework and capacity to ensure all stakeholders are involved.
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This study adopts a contemporary approach to these problems by using image-based spatial data to predict housing and basic services deprivation in urban slums. Although addressing slum deprivation remains highly context-specific, this analysis offers a conceptual framework to explore and compare deprivation within and across cities. In addition, the multi-sector nature of this endeavor creates promising opportunities for collaboration among the WBG GPs and with external partners. This project aligns well with the WBG’s recent efforts to increase resources devoted to developing knowledge and practical expertise in geospatial applications for development and is built on lessons learned from several analytical and operational initiatives at the intersection of geospatial data and slum infrastructure upgrading.8 Specifically, WBG projects focused on slum-related initiatives have used VHR satellite imagery to identify slum areas and monitor development patterns, use land cover to distinguish between formal and informal settlements, and classify slums by household and neighborhood characteristics. EO-derived findings from these projects have been used to inform, optimize and enrich subsequent surveys and support decision-making. This project progresses the current agenda by uniquely integrating survey data with EO data to predict slum deprivation from EO extracted variables through statistical analysis. 2.
Specific Objectives
The overriding goal of this project is to create an innovative, analytical tool to support decision-making leading to improved pro-poor policy interventions and comprises two phases. The objective of the first (concluded) phase of this project was to devise and test a novel, predictive model that combines spatial characterization analysis with statistical modeling to identify informal/slum areas and characterize slum deprivation according to housing infrastructure and existing access to basic services, including those that are informally supplied, through a case study of Dhaka, Bangladesh. To this end, this project seeks to move beyond the slum/non-slum dichotomy by measuring deprivation at the household scale, identifying and delineating slums spatially in a metropolitan area using EO data and importantly predicting the level of deprivation in housing and basic services from integrated geographical and household survey data. In the second phase, the case study will be extended and a user-friendly web application will be created to allow for interactive analysis of the resulting integrated spatial data on a web-based GIS platform. Finally, the results from both phases will help inform the development of a conceptual framework for evidence-based slum identification and characterization designed to guide policy-makers and other key stakeholders through the implementation process, from the development and application of the model through to policy setting and execution of strategic interventions. 3.
Methodology
8 See Annex D for a detailed summary of WBG projects using EO data analysis.
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The methodology used in this project integrates data from different sources in an innovative model offering an objective and comprehensive method for measuring deprivations in housing and basic services through a case study of Dhaka, Bangladesh selected following extensive internal consultation with the Water, Urban, Poverty and Energy GPs. Dhaka, the capital city of Bangladesh, has experienced rapid urbanization accompanied by inadequate and spatially uneven water supply and sanitation services9. Moreover, Bangladesh had previously been evaluated under the WASH (Water, Sanitation, and Hygiene) Poverty Diagnostic initiative thus there was a natural synergy between the two projects in addition to readily available baseline data. Importantly, the GOB had an existing mandate targeting improved water and sanitation services and demonstrated a distinct willingness to collaborate. Two sources of data were combined for this project: 1) an in-depth household survey conducted in 2016 as part of the WASH Poverty Diagnostic initiative on a sample of 63 slum communities, and 2) VHR EO data and analytics of informal areas/slums for the whole Dhaka Metropolitan Area produced by GISAT10 under the guise of the EO4SD Urban project11, a European Space Agency (ESA) initiative run under the ESA-WBG partnership established to demonstrate the capabilities of EO data applied to real urban development challenges. Household survey data included questions on access and quality of water and sanitation services, adequacy of living space, type of housing structure, security of tenure, and access to electricity. The survey was designed to collect data from 600 slum households across 63 slum communities in the DCC. To develop the statistical model with predictive capabilities and move beyond the conventional slum versus non-slum dichotomy, the response (dependent) variable was first modeled as a Slum Severity Index (SSI). This index, which builds upon the UN-Habitat’s definition of “slum household”,12 comprises six binary variables measuring different facets of shelter deprivation: (i) access to drinking water, (ii) access to sanitation, (iii) adequate living space, (iv) permanent structure, (v) security of tenure, and (vi) access to electricity, combined to assess the complexity and diversity of problems faced by either an individual household or a group of nearby households in a “slum-like” area. The SSI ranges from zero to six; a score of zero indicates the household is not deprived based on the measures described above, and a score of six indicates the household is deprived of all six housing elements. 9 Dhaka continues to experience substantial urbanization with population growth at a rate of more than four percent a year. 10 A Czech-based company providing EO services ranging from satellite data and geomatics software distribution, through specialized image and GIS data processing and analysis, up to advanced geoinformation products and services. 11 EO4SD (Earth Observation for Sustainable Development) is a new ESA initiative, which aims to increase the uptake of satellite-based environmental information in the regional and global programs of international financial institutions (IFIs). It follows a systematic, user-driven approach in order to meet longer-term, strategic geospatial information needs in the individual developing countries, as well as international and regional development organizations (http://eo4sd.esa.int). 12 UN-Habitat defines slums as areas with households that are deprived of access to safe water, acceptable sanitation, durable housing structures, adequate living space, and secured land tenure (See Box 1).
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Community level measures of housing deprivation were created based on the total percentage of deprivation for each slum community, e.g. % households deprived of water, etc. Similarly, household level SSI was aggregated at slum community level by taking a simple average of households within a given slum13. Slum delineation was undertaken using VHR (with pixel spacing in the range of 0.5 – 0.6 m) multispectral satellite images acquired for two reference years (2006 and 2017) in addition to open source spatial datasets, such as OpenStreetMap. Slum-like characteristics were prepared primarily directly from VHR imagery, interpreted EObased products from the EO4SD urban portfolio, e.g. land cover/ land use and sealing density (imperviousness), other open source data, e.g. OSM, or any of these datasets in combination. Several dimensions related to housing that could be extracted from EO data and may explain variation in housing deprivation in slums were then derived. These dimensions included measures such as proximity/distance to Central Business Districts (CBDs), proximity/distance to arterial roads, hazard susceptibility, morphology, spatial relationships, e.g. built-up density, and structural measures, e.g. dwelling size. These measures were then used as explanatory (independent) variables for the statistical model. Regression analysis was performed using aggregate measures of SSI (dependent variables) and slum-like characteristics (independent or explanatory variables) to predict likely areas of deprivation and identify high priority areas in need of intervention (see Section 6g for additional details). 4.
Summary of Results
EO-derived geospatial variables such as morphology, proximity to services, accessibility, and building density were shown to predict multi-dimensional deprivation in a given neighborhood. Notably, in this case study, distance to CBDs, proximity to roads and heavy industry, density and land use and land cover were statistically significant predictors of housing deprivation and access to basic services, such as water and sanitation and electricity. In fact, multiple measures derived from EO data were shown to be statistically significantly associated with measures of deprivation in the expected direction. Specifically, distance to the CBD, arterial roads, major road junctions, railroads, average dwelling size, and percentage of informal, local, primary, secondary and tertiary streets within slums were found to increase the relative risk of overall deprivation. To the contrary, proximity to heavy industry and shoreline, building density, winding index for informal streets, low-level node connectivity ratio and proximity to various social amenities were found to decrease the relative risk of overall deprivation.
13 For a more detailed discussion of indicators, see Section 6.
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More opportunities exist to exploit the model even further and identify additional characteristics that could contribute to determining specific planning parameters and rolling out interventions in an effective way. The results from these models also have the potential to predict the location and scale of future changes, which could support the creation of inclusive policies and targeted planning interventions to help marginalized, poor and vulnerable populations. Moreover, the outputs of the analysis could be used to conduct effective slum analysis and predict future scenarios engaging multiple sectors and actors in a meaningful dialogue. 5.
Limitations and Recommendations
Subsequent versions of the model should be constructed at the household level to increase statistical power. Precise location data were only available at the slum community level and only 5 to 10 households had been surveyed in each slum, significantly reducing the available dataset and statistical power of the model. This is especially true for a count model such as Poisson regression that usually requires a larger sample size. The model’s specification (OLS, Poisson or Negative Binomial as well as inclusion or exclusion of certain variables) produced very different results in terms of coefficients, directions, and significance for some of the explanatory variables. Consequently, results should be interpreted with caution at this stage. Household level analysis will enable more targeted slum policies and improve slum interventions. Household level data are often aggregated to the neighborhood level to match the scale of analysis at which EO indicators were meaningful, resulting in a loss of heterogeneity between households. While such aggregations are useful for community level interventions and city level comparisons, they are less useful for household level interventions. A household level approach in the second phase in identifying and characterizing slums will allow planners and policy makers to design more targeted slum policies and improve slum interventions. A multi-tiered approach to access to water and sanitation will better align future models with the Sustainable Development Goals. The model’s current definitions and assumptions surrounding access to water and sanitation do not provide the level of detail required to monitor the Sustainable Development Goals (SDGs). For example, questions pertaining to access to water, may not accurately capture information needed to monitor the SDGs since the question does not focus on quantity or quality of water, time spent gathering water, etc. The second phase of the project will utilize a multitiered approach (presented in section 6d) to capture various aspects of access and quality for water and sanitation and construct nuanced versions of the dependent variables including an enhanced SSI. A similar approach will be adopted for other services where applicable. Local expertise and high quality geo-referencing data increase the certainty of slum identification. Remote sensing identifies probable informal settlements by localizing 12 | P a g e
areas with visible physical characteristics typical of slums in the area and is capable of recognizing patterns of deprivation in areas that have yet to be formally identified. However, local expertise and/or field validation are needed to confirm the results. Further, the accuracy of matching EO-derived characteristics with household survey data depends on the quality of geo-referencing data. While buffer zones can help limit these uncertainties, ideally household surveys should strive to include high quality georeferencing data at the outset. The impact of vertical infrastructure growth on slum deprivation should be analyzed in detail. Certain slums, especially in the Eastern Dhaka and Southern Dhaka near the river, underwent transformation of their morphology over time: from quite homogeneous into more complex patterns. For example, a substantial proportion of mostly multi-story high-rise buildings were erected within the original slum neighborhoods, mostly in a scattered, spatially inconsistent, and organic manner. Rather than reflecting a change in soil sealing or structural patterns (which could be reflected by standard variables derived from optical EO data), this process is attributed by change in 3D zoning–the average height of the building blocks increases. Still, the prevailing major proportion of built-up area retains its slum character. The image analysis used in this initial pilot project did not fully reflect these internal processes and will be investigated in detail from both a socioeconomic and remote-sensing perspective in order to propose appropriate indicators and monitoring measures in the second phase of the project. 6.
Moving Forward
Current planning and policy approaches rely on household surveys to estimate demand for urban services such as water and sanitation. However, these surveys are often expensive and time-consuming. Consequently, they are not conducted on a regular basis. On the other hand, resolution, availability, and affordability of earth observation data have improved drastically in last two decades. This approach moves beyond a narrow sector focus, combining spatial analysis with different measures of basic deprivation to assess the diversity of problems faced by slum households and has considerable potential for replication in other cities and sectors. Specifically, this model provides a pathway to estimate demands for urban services by leveraging readily available EO data and providing almost real-time estimates that could be especially useful in rapidly growing slum areas in the developing world to design effective programs and policies providing an efficient way of allocating resources and a better way to engage various stakeholders—namely slum-dwellers, developers, civil society, and local governments—in discussions about policy options based on evidence rather than politically charged grounds. Collecting and integrating multidimensional data that can be spatially overlaid and intuitively visualized and explored in web-based maps can provide an invaluable tool for planning with more transparency and accountability. With the proliferation of open source software for crowdsourced data collection, cloud-based storage, and free 13 | P a g e
software for spatial data analysis, the urban poor are organizing themselves to seek solutions to their own needs and to the extent possible, maps and data generated from the second phases of this project will be open and accessible to all stakeholders. These findings are particularly relevant to the WBG at a time when the emphasis on citywide and multi-dimensional approaches is being recognized not only for overarching urban program design, but also within specific sectors. In particular, this project will benefit the Water, Energy, and Urban GPs by providing a viable methodology to help improve project design and develop systematic targeted interventions implemented in informal urban settlements and slums and the WBG is uniquely positioned to leverage its resources and convening power to ensure the continuation and application of this innovative research.
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1. Introduction With the global population of slum dwellers exceeding one billion today and discouraging population projections for the coming decades, our rapidly urbanizing world needs to address the challenges facing slums in a much more effective way than ever before. There is an urgent need for slum improvements to move beyond a few isolated success stories and adopt a programmatic approach to the challenges facing slums. Furthermore, cities need to be proactive in providing affordable housing and basic services to avoid the formation of new slums. However, designing effective projects and policies requires information on slums and the cities in which they are embedded. And yet, national and municipal governments rarely have access to up-to-date, reliable information on the number and characteristics of slums in a given city. Such evidence is critical for cities to formulate effective interventions, programs, and policies. Most importantly, information that could help identify the most deprived slum populations in a reliable and timely manner would be very useful in prioritizing interventions in resource scarce settings such as those found in developing countries. Thus, a framework that could provide rapid with minimal cost is essential for targeting limited resources in a more effective way. The framework developed in this report provides a unique means for identifying deprivations in housing and basic services in slums that could be useful in designing effective programs and policies. Moreover, this framework offers an alternative approach for engaging stakeholders—namely slum-dwellers, developers, civil society, and local governments— in evidence-based policy discussions instead of politically charged territory. 1a. Background: The Challenge of Unmanaged Urbanization Close to 52 percent of the world’s population lives in urban areas, about a quarter of which lives in slums, mostly in less developed countries (UN-Habitat, 2012; 2015). UNHabitat defines slums as areas with households that are deprived of access to safe water, acceptable sanitation, durable housing structures, adequate living space, and secured land tenure (See Box 1). Slums are a major part of the urban housing stock and an important part of the urban economy in many cities of the global South yet these areas also have a high concentration of urban poor (Sliuzas & Kuffer, 2008). UN-Habitat (2012) estimates most of the urban population growth over the next three decades will occur in cities of less developed countries, which means the pressure on sub-national governments to deliver basic services like water and sanitation will continue to rise. Recognizing these challenges, the international development community has begun to focus on slums, notably through the inclusion of specific targets in the Millennium Development Goals (MDGs) at the dawn of this century, and more recently in the Sustainable Development Goals (SDGs) (United Nations, 2016). Despite these efforts, affordable housing and basic services remain a distant goal for most developing
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countries. Part of the problem is that a significant fraction of slum upgrading 14 initiatives implemented over the last two decades across different countries showed mixed results or failed to scale up from pilot projects to the intended level of citywide impact. A review of these initiatives, some of which were supported by the World Bank 15, showed identifying the most deprived slums and strategies for targeting interventions in these areas were often the weak link in these initiatives. In fact, in most cases, it was hard to pinpoint the explicit reasoning and justification, much less develop a convincing argument, driving the choice of beneficiary communities. The site selection and prioritization appeared to be conducted without a systematic process or proper justification. When resources are limited; these choices should be driven by evidence to prioritize the most deprived slum areas. Box 1: UN-Habitat: A universal definition of “slum”? The definition of the term ‘slum’ includes the traditional meaning —high population densities (i.e., limited living space per person and high cohabitation rates in single-room units) and low standards of living as reflected in housing deprivations such as limited or deteriorated basic services like water and sanitation. UN-Habitat (2010) reviewed slum definitions used by various national governments, statistical agencies, and institutions working in slums to arrive at the following comprehensive set of slum characteristics: Lack of basic services: Poor access to improved sanitation facilities and improved water sources is the most important feature, sometimes supplemented by absence of waste collection systems, electricity supply, surfaced roads and footpaths, street lighting, and rainwater drainage. Substandard Housing: Slum areas are associated with a high number of substandard housing structures, often built with non-permanent materials unsuitable for housing given local conditions of climate and location. Factors contributing to a structure being considered substandard are, for example, earthen floors, mud-and-wattle walls or straw roofs. Various space and dwelling placement bylaws may also be extensively violated. Overcrowding: Many slum dwelling units are overcrowded, with five and more persons sharing a one-room unit used for cooking, sleeping, and living. Settlement size: Many slum definitions also require some minimum settlement size for an area to be considered a slum, so that the slum constitutes a distinct precinct and is not a single dwelling. For example, the 2011 Census of India defines a slum in terms of 60-70 households living in poorly built and congested tenements in a cluster. Insecure tenure: Lack of any formal housing document entitling the resident to occupy the land or housing structure is considered prima facie evidence of illegal occupation. Additionally, informal settlements are considered vulnerable living arrangements that are defined in terms of noncompliance with planning and land-use regulations.
1.
2.
3. 4.
5.
14
In this report, ‘slum upgrading’ refers to any measure that improves the physical, economic, and social environment of slum dwellers as a means to integrate informal areas into the city. Approaches span from upgrading the physical infrastructure in slums, to addressing social and economic problems, to land tenure regularization of the settlements and associated land parcels. 15 A recent World Bank paper on the “Inclusive Cities Approach” reviews and discusses the portfolio of slum upgrading operations supported by the World Bank–corresponding to an estimated total lending of US$2.5 billion from 19722013. (GSURR, 2015). The World Bank also compiled a multimedia sourcebook (Mehta & Dastur, 2008) and an eLearning course on Upgrading Urban Informal Settlements (Project P133055) that offer a rich and pragmatic discussion on a broad array of approaches to slum upgrading and inclusive city planning. Both sources emphasize the importance of acknowledging the complexity of slum approaches and give pragmatic suggestions to adhere to the local institutional framework and capacity to ensure all stakeholders are involved.
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The experience of ‘living in a slum’ often consists of a combination of these multiple dimensions. Many slum areas may show only a few of these negative attributes, while the worst may be affected by all of them.
In fairness, the challenge facing policymakers is the lack of information and/or means to identify the most deprived areas. In addition, the reality of fast-growing slums and unplanned urban pockets is a phenomenally intricate bundle of problems that involves a multiplicity of stakeholders with different needs, constraints, and agendas. Even defining and detecting a “slum” can be quite challenging (especially for outsiders), and the nature and severity of the deprivation endured by residents is highly heterogeneous within cities16. While the number of poor people moving to big cities keeps growing, the provision of adequate housing and basic infrastructure services is far behind and is often stalled by uncoordinated city governance, poorly functioning land and housing markets, vulnerability to environmental hazards, etc. In addition, the residents of slums and unplanned areas are highly mobile and the available socio-economic data on this population group are lacking or highly fragmented. Consequently, it is challenging to plan effective and sustainable development policies for neighborhoods that are (literally) not on the map or inadequately represented in census data. As the above discussion illustrates, there are multi-faceted challenges that explain why many housing and citizen inclusion interventions seem poorly planned, leading to modest outcomes. Their targeting is often driven by arbitrary choices or political economy agendas without proper consideration for all the factors involved (geography, urban governance, tenure security, socio-economic and demographic characteristics) that are likely to affect the project’s success and beneficiaries’ response. 1b. A New Methodology This project proposes an analytical tool to identify, prioritize and target slums for interventions. Building on the potential of Very High Resolution (VHR) remote sensing data and advanced image processing this approach provides a holistic strategy for understanding the needs of slum-dwellers based on the geography and spatial dynamics of each area. Remote sensing is already being used to map the number and extent of slums and a multiplicity of extraction methods for slum mapping have been developed in the last fifteen years ranging from classical visual image interpretation to Object-Based Image Analysis (OBIA) to machine learning, or a combination of methods (Kuffer et al., 2016). The main challenge remains how to translate a relevant set of slum characteristics into 16
A paper by Kohli et al. (2012) offers a particularly interesting discussion on how to conceptualize slums. Using input from 50 domain-experts covering 16 different countries, the authors define a generic slum ontology (GSO), a comprehensive framework that includes all potentially relevant indicators that can be used for image-based slum identification (at the object level, slum settlement level, urban environment level). The generic ontology model is then transformed into an implemented model providing one example of local adaptation in the Kenyan city of Kisumu. (2011, December 15). An ontology of slums for image-based classification – ScienceDirect. Retrieved June 7, 2017, from http://www.sciencedirect.com/science/article/pii/S0198971511001128
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robust indicators (e.g. developing a slum ontology) for image-based slum mapping that would ultimately allow for developing a global slum inventory (Kuffer et al., 2016, p. 5). Furthermore, it is important to identify the worst conditions among slums for prioritization purposes since different slums face different levels of housing deprivation (Patel et al., 2014). Based on the premise that integrating multi-source, spatial data with locally collected data has the potential to improve the design and targeting of slum infrastructure upgrading policies, this methodology builds on two main considerations: (i) the growing potential in the increasing availability, quality, and accessibility of spatial data being collected formally and empirically on slums, and (ii) the growing number of household surveys collecting GPS coordinates, and the advances in mobile telecommunication and social media generating data from communities on a regular basis. Collecting and integrating multidimensional data that can be spatially overlaid and intuitively visualized in web-maps can provide an invaluable tool for planning with more transparency and accountability. With the proliferation of open source software for crowd sourced data collection, cloud-based storage, and free software for spatial data analysis, the urban poor are organizing themselves to seek solutions to their own needs. Further, new grassroots technologies are enabling many communities to express their aspirations and needs as well as engage in slum upgrading initiatives. As a result, Geographic Information Systems (GIS) tools have been created to represent previously unmapped areas. Integrating these data further leverages the complementarity of insights obtained from sophisticated EO data image processing algorithms linked to the invaluable knowledge that can only be captured in close interaction with local residents of slums. Finally, identifying contextual slum typologies is also an important research direction and integrating image-based spatial data with socioeconomic characteristics can help respond to this need. The proposed methodology aims to support the policy design stage by providing a comprehensive and objective framework for prioritizing and sequencing slum rehabilitation work. Recent literature and case studies indicate advances in information technology and the growth of innovative data collection tools, including remote sensing imagery, and the use of aerial photography and drones to obtain VHR images suitable to detect informal settlements, observe spatial-temporal growth patterns, and map morphological characteristics. Through its global operational links to city-level policy makers and their recipient communities, the World Bank is uniquely positioned to leverage its resources and convening power to harness the most innovative research in the field of remote sensing data analysis today. 1c. Project Scope, Timeline, and Partners This activity will be completed in two stages. In the first (concluded) phase, EO-derived data are used to predict six measures of housing deprivation, and a comprehensive and 18 | P a g e
combined measured called the Slum Severity Index (SSI), for a case study in Dhaka, Bangladesh, through regression analysis. In the second phase, the case study will be further extended and a user-friendly web application will be created to allow for interactive analysis of the spatial data integrated on a web-based GIS platform. To the extent possible, maps and data generated from this project will be open and accessible to all stakeholders. In addition to facilitating infrastructure planning for poor urban neighborhoods, these maps could be used to engage communities and non-governmental organizations (NGOs) by giving them a way to hold local government agencies accountable as well as providing feedback for an incremental and sustainable policy plan. In the long-term, the framework will be tested and validated in more cities. Finally, the results of both phases will be used to lay the foundations of a conceptual framework to improve the planning and targeting strategies for interventions in cities with large populations living in slums. 1d. Report Organization The report is divided into ten sections. The next section provides a comprehensive review of literature related to spatial analysis of slums alongside the World Bank efforts on slum mapping. Section 3 outlines the projects’ goals and objectives. Section 4 provides background and context on Dhaka, the case study city. Section 5, describes the data, whereas Section 6 describes the methodology. Section 7 illustrates the results of the analysis while Section 8 presents planning and policy implications drawn from the case study analysis. Section 9 outlines study limitations and proposes ways to overcome them in the next phase. Finally, Section 10 concludes the report.
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2. Predicting Housing Deprivation from Space In order to establish a justification for pursuing the unique research agenda proposed in this report, we explored literature that substantiates the possibility and value of predicting housing deprivation from space. The review included academic articles as well as practitioner reports from the World Bank. Section 2a focuses on the academic literature, and section 2b provides a review of the World Bank projects that are relevant to this study. 2a. Literature Review As EO data became more widely available at the turn of the century, scholars began exploring how geospatial data could be used to map the location, extent, and various types of deprivations within urban areas, particularly slum settlements. A subset of studies focus on simply finding slums and informal settlements by identifying changes in Land Use / Land Cover (LULC). Three studies that fall into this category are detailed below, highlighting the value and applicability of these early techniques and findings to this study. Scholarship has also been devoted to mapping the extent and growth of informal settlements; a topic touched upon by two studies below with relevance to the present case regarding the vulnerability of slums due to their inhabitants’ transitory nature. Finally, various researchers have taken an interest in mapping the more nuanced changes in levels of deprivation and socio-economic characteristics within slums. Only three studies, however, have explored the use of remote-sensing data for this purpose so far. This technique is applied to the current case study using new data and modeling techniques to further advance this area of knowledge. The following section places this study within this wider body of literature. One of the earliest studies using geospatial data to map land use in cities established a standardized classification of LULC, similarly referenced by this study. Rashed et al. (2001) argued that land use planning at local (within-city) or regional (between-cities) levels requires a standardized classification. RS imagery can only record land cover, which describes the physical state of features in urban lands (e.g. vegetation types, water bodies). Thus, land cover is a more objective measure than land use, which cannot directly be linked to RS data and is prone to interpretation error because different users will have different perspectives on the classification procedure (Rashed et al., 2001, p.6). They found the Spectral Mixture Analysis (SMA) fractions of vegetation, impervious surface, bare-soil, and water/shade end members provided a measure of the physical properties of the dominant classes in the urban landscape, helping to reveal the morphological patterns of the Cairo metropolitan area and its surroundings. The baseline classification system developed in this study has since been further refined by many subsequent studies. Various forms of geospatial mapping and classification have now been applied to identify urban areas from space. Shi et al. (2014, pp.359) used the Visible Infrared Imaging Radiometer Suite (VIIRS) day-night band (DNB) carried by the Suomi National Polar Orbiting Partnership (Suomi NPP) satellite to extract the location of built-up areas in 12 cities of China. They argued that since the built-up areas are illuminated artificially 20 | P a g e
at night, their corresponding pixels in nighttime light images have larger digital number (DN) or radiance values than the surrounding dark areas. More relevant still, researchers are even going so far as to isolate slums and informal settlements within those urban areas17. Rhinane et al. (2011) used an object-oriented classification approach to map slums in Casablanca, Morocco. This approach was carried out in two steps: segmentation and classification. Segmentation involved subdividing an image in the visible range into homogeneous regions. The researchers then grouped objects and assigned them a probability or degree of belonging to a given class from the robust rules of region recognition (Rhinane et al., 2011, p.220). The binary image resulting from the classification was then exported to ArcGIS, and slums in the city of Casablanca were mapped. However, authors cautioned that strong heterogeneity of the urban environment resulted in misclassified pixels that were corrected using mathematical morphology. This object-oriented approach resulted in a mapping accuracy of 85% (Rhinane et al., 2011, p.221). Several RS approaches have also been utilized to obtain up-to-date and faster access to information on the location and growth of slums. For example, Kit et al. (2012) proposed using a lacunarity method to extract slum pockets from QuickBird satellite images for the case of Hyderabad, India. Lacunarity analysis is a multi-scaled method of determining the texture associated with patterns of spatial dispersion. The lacunarity algorithm is capable of identifying the core of a slum; the areas with very dense housing that are particularly vulnerable to natural and socio-economic hazards (Kit et al., 2012, p. 666). Amorim et al. (2014) offered an approach integrating lacunarity and texture based analysis to better understand complex intra-urban socio-spatial patterns for urban planning and land-use management. This method allowed them to describe the spatial arrangement of surfaces and built materials on Very High Resolution satellite images. There is growing interest among researchers to devise methods for time- and costefficient collection of social and spatial indicators to help policymakers and practitioners, measure the extent of poverty, and locate the hotspots of deprivation for targeted investments in infrastructure development and improvements in basic services. Researchers like Duque et al. (2015) and Hofmann (2001) argue that poverty measures like individual level income and consumption require a heavy investment of time and money to conduct large-scale surveys. Thus, they often fail to capture intra-urban variation in deprivation among slums, a gap that can be fulfilled by spatial mapping of slums. In order to advance geospatial mapping techniques to the level at which they are operational for this purpose, GIS data must be integrated with statistical databases of socio-economic indicators and field surveys to evaluate the environmental and related socio-economic impacts of informal settlements.
17
For a complete and up-to-date review of identifying slums from space, see Kuffer et al. (2016).
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For example, in a study integrating statistical databases with geospatial data conducted within 10 districts in the Brazilian city of Cuiaba (Zeilhofer & Topanotti, 2008), the authors extracted (and scored) 35 indicators of environmental degradation pertaining to vegetation cover, street network, land ownership, and water and sanitation infrastructure from government statistical databases, field surveys, and GIS. The highest indicator scores were observed for vegetation cover and water supply. The former was impacted by the growth of informal occupation, while water pollution of urban streams was characterized by accumulation of solid waste and raw sewage. The latter points to the poor coverage of sanitation infrastructure in sampled areas (Zeilhofer & Topanotti, 2008, p.10). Higher degrees of degradation were seen in younger settlements due to a lack of infrastructure and public services. Settlements in districts with elevated declivity suffered from erosion processes due to vegetation clearing or inadequate orientation of street network. Using night-time images, Zhuo et al. (2009) have shown that the distribution of light intensity can be used to further estimate the density of energy consumption, greenhouse gas emissions, and economic activity. Some innovative statistical approaches to slum mapping and deprivation variability have combined RS imagery with mobile phone usage data to provide a more up-to-date measure of poverty (compared to census data) in low- and middle-income countries. Steele et al. (2017) conducted such a study for mapping spatial distribution of poverty in rural and urban Bangladesh. Night-time lights and transportation time to the closest urban settlement were important variables to predict poverty in both rural and urban areas. Higher cell phone usage, generally, was associated with lower poverty rates in both rural and urban areas. The combination of spatial data and more frequent cell phone usage data may distinguish the transitorily poor from the chronically poor (Steele et al., 2017, p.8). This integrated approach offers the potential for a fuller characterization of the spatial distribution of poverty and the findings and lessons learned from this research could be applied for mapping electricity deprivation in slums and informal settlements in less developed countries. Spatial mapping of slum morphology has the benefit of identifying locational characteristics like proximity to landfill sites and the presence or type of sanitation infrastructure that may have consequences for health and social outcomes of residents living in such communities. Rahman & Alam (2015, p. 4) found that 76.4 percent of children in three Bangladeshi slums were suffering from diseases because of poor hygiene resulting from inadequate sanitation services. In conjunction with poor access to nutritious food, these children had high rates of morbidity that can have grave consequences for future cognitive development and socio-economic mobility. The absence or inadequate levels of other basic services in slums and informal settlements, like water supply, also have an adverse impact on school completion rates among children. In a study of 33 public schools in Delhi, India, Chugh (2013, p. 17) found that children living in slums and resettlement colonies were at a higher risk of dropping out before reaching secondary level. One of the major factors underlying this educational outcome was poor infrastructure in these communities. 22 | P a g e
In order to spatially target poverty alleviation policy interventions to address these socio-economic impacts related to slum deprivation, disaggregated contextual spatial information (i.e. location characteristics of slums such as access to roads, density, building structures, proximity to hazardous zones, etc.) is needed. Such data can provide a multidimensional assessment of the challenges and needs of the urban poor. Baud et al. (2010, p. 360) highlight the potential of remote-sensing methods for mapping multiple deprivations by analyzing spatial variation within Delhi such as patterns of green spaces, the structure of building layouts, density of built-up areas, and urban structure (i.e. arrangement of land cover). RS can play a key role in analyzing space-time dynamics such as monitoring densification and expansion of settlements or assisting with the implementation of slum improvement policies (Patino & Duque, 2013). A number of VHR RS products (such as IKONOS, QuickBird or Pleiades) from numerous satellite data providers are now readily available to the public. New emerging missions (e.g. Planet) make image data even more accessible and actionable. All of these resources can be employed more frequently for extracting information about physical deprivations across different residential areas. This urban morphology data can be then linked with socio-economic parameters (e.g. access to potable water supply, sanitation, household size, and tenure status) obtained from census or household surveys to design urban development plans for a city or region. Recently, a few researchers have attempted to predict outcome indicators obtained from a pre-existing census or survey as a function of remote-sensing-derived variables (Stoler et al., 2012; Taubenböck et al., 2014; Duque et al., 2015). In both Duque et al. (2015) and Stoler et al. (2012), the authors model a Slum Index (computed from survey and census data respectively) that measures various degrees of household deprivation. Conversely, Taubenböck et al. (2014) are mostly interested in how to delineate and define slum areas from a morphological point of view using remotely sensed data with a selection of explanatory variables. This study builds on both of these works to develop a model that could be used to predict deprivations at household and slum levels. In sum, spatial imagery is a powerful tool to evaluate the extent and variability of deprivation in slum settlements but has thus far been rarely used for that purpose. Early attempts have focused on LULC mapping and change with respect to informal settlements. Beyond mapping slums, researchers make the case for identification of contextual slum typologies—integrating image-based information with socioeconomic characteristics—which may ultimately lead to better targeting of pro-poor policy interventions (Kuffer et al., 2016). However, only a few studies have used EO data to predict access and quality of basic services such as water and sanitation in slums. This study attempts to devise a methodology for this purpose, to support planners and policymakers in the decision-making and implementation process. 2b. Review of Relevant World Bank Projects 23 | P a g e
The World Bank Group (WBG) has recently increased the amount of resources and effort devoted to developing knowledge and practical expertise in geospatial applications for development. Various steps were taken internally18 to consolidate a geoportal to store and share spatial data, facilitate the exchange of ideas and lessons learned across sectors, and offer technical support on implementing spatial applications for projects. Several external partnerships have been established, including the aforementioned WBG-ESA collaboration, to foster innovative research and explore potential applications using Earth Observation for Sustainable Development (Soukup, 2015). This study builds on lessons learned from several analytical and operational initiatives pursued within various WBG Global Practices (Water GP, Urban GP, IT solutions, etc.) at the intersection of geospatial data and slum infrastructure upgrading. In preparation of the first World Bank ‘Geospatial Day’ held on November 1st, 2016, the Geospatial Community of Practice (GeoCoP) compiled a list of over 140 projects to showcase how the WBG, its partners, clients, and the private sector leverage geospatial analytics. Some involved RS and applications spanned all possible sectors, including: climate change; fragility, conflict, and violence; transport; urban development; and water, to name a few. Among those, some initiatives are particularly relevant to this study, either due to their specific focus on slum identification and characterization or for the methods and technologies they adopted (see Annex D for a detailed discussion). Beyond the support for participatory slum mapping and ‘data for resilience’ initiatives, examples in which slum-related WBG initiatives used VHR satellite imagery were reviewed. The intended goals of those projects included: identification of slum areas and monitoring their development patterns, distinction of land cover types between formal and informal or irregular residential areas (a proxy for slum), and further classification of slums by type reflecting household and neighborhood characteristics. In terms of how the RS data interacted with census or survey data, most of the reviewed examples used EO-derived findings to inform subsequent surveys, by way of defining a stratification layer for a survey sampling strategy. Some provided inputs to be validated and enriched through ground observation or to perform inter-census estimates of slum inventory. These are all worthwhile applications and this study similarly has the potential to open up future applications such as support in census questions or suggesting patterns or outliers to be investigated on the ground. Notwithstanding, none of the reviewed projects appear to have used data from a preexisting survey in a similar manner as this study, i.e. as outcome (or dependent variables) that can be predicted as a function of RS-derived variables. From the academic literature reviewed in section 2a, it is clear this is a very promising approach, and via the 18
At the institutional level, the mandate of expanding geospatial analysis for development is led by the Development Data Council’s Analytics and Geospatial Working Group (AGWG), mapped under the office of the Chief Economist for Sustainable Development. This unit is supported by the created Geospatial Operational Support Team (GOST) and Geospatial Community of Practice (GeoCoP).
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advantageous position of the World Bank, it is possible to generate, or closely interact with the providers to collect both the survey data as well as the EO data. Integrating the two sets of data in a coordinated way is a more effective and efficient approach to this line of inquiry. In the machine learning domain, the core of this project’s method would be categorized as “supervised” learning—wherein the available data, in this case provided by the Bangladesh Urban Informal Settlements Baseline Survey (BUISBS) survey, contain the response variable being modeled. So the obtained learning algorithm is a function of inputs (EO data), which can be used to predict the same response value (or class) for a different dataset (e.g. un-surveyed slum areas in Dhaka). Conversely, the initial RS analysis of the slums in Metro Manila (see Annex C and Tigny, 2016) illustrates an “unsupervised” learning approach. In fact, leveraging the availability of a wealth of physical attributes obtained from advanced semi-automated OBIA, the authors identified robust discrimination criteria to automatically classify the detected informal settlements according to the different types that were observed visually. In other words, they obtained a classifier model capable of finding otherwise unknown patterns in data. Both of these approaches are equally valid and the choice is necessarily dictated by the context, research goals and, obviously, the available data. This project’s method takes advantage of the fortunate circumstance of having access to the BUISBS survey, which focused on the response variables for 63 of Dhaka’s slums. Nonetheless, having a preexisting representative survey is not a strict requirement. While establishing a systematic relationship between higher-level image elements and slum characteristics is essential, this can be done with much smaller samples, by envisioning an iterative process in which RS analysis and socio-economic surveys create a feedback loop that incrementally enhances the accuracy of the model. Remote sensing may not replace, but certainly can enhance, by informing or validating, expensive data collection undertaken on the ground.
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3. Goals and Objectives 3a. Overarching goals The key objective of this project is to devise and test a targeting method based on a combination of spatial characterization analysis and in-depth household surveys to visualize and categorize informal/slum areas according to existing access to basic services, including those that are informally supplied and off the city network. The ultimate goal is create an innovative analytical tool that could help the World Bank improve the design of infrastructure interventions to tackle the complex challenges of providing infrastructure services in urban slums. In particular, the project will benefit the Water, Energy, and Urban GPs by piloting a methodology for the design and systematic targeting of interventions implemented in informal urban settlements and slums around the world. The proposed evidence-based planning methodology will: a.
b.
c.
Identify gaps in basic infrastructure service provision for slum dwellers by documenting areas in which the basic conditions needed for human dignity are lacking19; Integrate geographic and spatial data visualization (particularly at the project design stage) to better exploit multi-dimensional data needed for slum upgrading projects and interventions; Offer a robust conceptual framework for selecting cities that could benefit from a similar systematic analysis of slum-related data.
The outputs of the overall activity (upon completion of Phase I and Phase II) will benefit teams working in slums by collecting and integrating multi-dimensional data in an open, accessible GIS platform for visualizing and analyzing multi-faceted datasets available in slums. This will help the Bank to improve planning and designing targeted activities in slums with transparent and accountable information. Finally, these will help inform the development of a conceptual framework for evidence-based slum identification and characterization designed to guide policy-makers and other key stakeholders through the implementation process, from the development and application of the model through to policy setting and execution of strategic interventions. Additional secondary objectives include: 1.
Promoting the adoption of standards and norms for maps and other geospatial data sharing within and outside the World Bank in support of cross-sector collaboration and data sharing;
19
“The United Nations Committee on Economic, Social and Cultural Rights has underlined that the right to adequate housing should not be interpreted narrowly. Rather, it should be seen as the right to live somewhere in security, peace, and dignity” (UN-Habitat, 2009, p. 1). This broader definition of “adequate housing” adopted by UN-Habitat in several statements and project documentation includes the “Availability of services, materials, facilities, and infrastructure”.
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2.
3.
Assessing the potential of a multi-layer georeferenced data platform as a tool to stimulate policy dialogue and build stakeholder consensus on strategic, longterm decisions about urban planning and slum upgrading; Elaborating an innovative and customizable approach for sharing and maintaining high quality multilayer information, while promoting transparency and accountability of slums data.
3b. Specific Objectives The proposed analytical tool has the following overarching objectives: (i) to identify the main factors associated with low access to “improved” and/or “safe” drinking water, sanitation services, electricity, and other basic services in urban slums and informal settlements in the observed city, and; (ii) to explore how a combination of spatial analysis and statistical modeling might help classify all urban slums and informal settlements of the observed city using a consistent typology to predict the level of available services in each sub-area. Specifically, the tool aims to: 1. 2. 3.
Shift the discourse beyond slum/non-slum dichotomy to include inter-slum deprivation in housing and basic services; Identify and delineate slums spatially in a metropolitan area using EO data; Predict deprivations in housing and basic services as a function of factors from EO data.
3c. Case Study Identification To validate how well the proposed method can respond to these objectives, the methodology was piloted using a case study of Dhaka, the capital city of Bangladesh. The identification of the city for the case study was reached through an iterative consultation process within the Water GP, as well as the Urban, Poverty, and Energy GPs and their external partners to leverage existing data collection efforts. The selection process began by looking at those cities in which rapid urbanization is accompanied by inadequate and spatially uneven water supply and sanitation services20. The Water GP recommended selecting one of the countries already engaged in the larger Global WASH Poverty Diagnostic (GWPD) initiative21 whose task is to identify the WASH service delivery constraints and potential solutions to improving services to the poor. In addition, the Water GP has explicitly committed to improving the planning and implementation of Water Supply and Sanitation (WSS) services in peri-urban areas of rapidly growing cities through various coordinated activities under the ‘Rapid Urbanization’ analytic activity within the WSS GSG.
20
Dhaka continues to experience substantial urbanization with population growth at a rate of more than four percent a year. 21 A coordinated and extensive effort that is currently being implemented in 18 countries across regions.
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Another important criterion—especially in view of future uses of the analytical tool by policy makers—was to focus on locations in which there was an existing mandate and/or openness to collaboration of city officials serving urban slums and informal areas. In this respect, The Government of Bangladesh (GoB) has demonstrated its commitment to the Sustainable Development Goals (SDGs), including SDG 6 – ensuring availability and sustainable access to water and sanitation for all. The SDGs were taken into consideration while developing the country’s seventh Five-Year Plan for the period of 2016-2020. In addition, the Dhaka Water Supply and Sewerage Authority (DWASA) has shown a significant commitment to accommodate this rapid pace of urbanization and is now capable of providing water to all of 15 million residents under its jurisdiction.22 Lastly, the choice of pilot study site was driven by the availability of spatial data. Extensive research was conducted by cross-checking available sites against the World Bank priority areas. The aim was to find existing and readily available census or survey data covering the pilot city’s informal areas and slums (which proved difficult). Two timely and relevant data sources were available, first, the above-mentioned BUISBS slum survey, part of the WASH-Poverty diagnostic global initiative, and second, spatial data for the VHR RS imagery and slum characterization analysis. Thanks to the invaluable support of the World Bank’s GeoCoP, the proposed pilot was included in the portfolio of projects supported under the ongoing WBG-ESA memorandum of understanding23. Dialoguing with the coordinators of the Earth Observation for Sustainable Development (EO4SD) revealed the ESA’s Urban Development Service Cluster would be supporting the World Bank in a few cities (Dar es Salaam, Dhaka, Durban, and Lima). Hence, Dhaka was selected for Phase I and the EO4SD project’s continued support is foreseen for Phase II. Although this pilot project is limited to one case study site at this time, the conscientious effort put forth in the site selection ensures the methodology and findings will be immediately useful for practitioners in Bangladesh. In fact, the ongoing preparation of a new project “Dhaka Sanitation Improvement” (Moulik, 2017) demonstrates the continued attention to WASH improvement in Dhaka, and corroborates this study’s relevance to the Water GP to contribute significantly in designing effective and sustainable improvements of the water, sanitation, and drainage network in the city.
22
More details can be found in the project paper on “Bangladesh Performance assessment progress: 2007-2016” (Joseph, 2017), detailed in the internal documentation package of BWPD Background Documents. 23 Alvaro Federico Barra (who leads the GeoCoP) and Christoph Aubrecht (Seconded from the European Space Agency (ESA/ESRIN)) were instrumental in establishing this partnership.
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4. Case Study of Dhaka: Contextual Overview Being the principal city of Bangladesh, higher population growth and inner migration from rural areas is resulting in an unprecedented level of urbanization in Dhaka. Planners and city managers are hence facing more complex spatial and socio-economic challenges to deal with the rapidly expanding urban footprint. Updating the knowledge and evidence-base of Dhaka’s urban growth dynamics becomes increasingly crucial for better functioning of its strategic urban planning and management of the city. To address these complexities, Dhaka has recently collected updated and extensive data that allows hybrid spatial modeling frameworks to be incorporated with statistical models and EO data to cultivate a better understanding of the dynamism of rapid urban growth in the city. 4a. Housing and Basic Service Deprivations in Bangladesh This overview of Bangladesh’s access to safe water and sanitation and living conditions affecting the urban poor borrows mainly from the extensive work conducted under the Bangladesh WASH Poverty Diagnostic (BWPD) initiative (World Bank Group, 2017). Despite a significant reduction of poverty over the past years, Bangladesh remains one of the poorest countries in Asia.24 Per the latest data from the 2010 Household Income Expenditure Survey (HIES), Bangladesh has a national poverty headcount rate of 31.5 percent and an extreme poverty headcount rate of 18.5 percent. The country has one of the world’s highest population densities wherein 161 million people live in less than 150,000 sq. km. Additionally, Bangladesh is vulnerable to flooding, earthquakes, and cyclones (World Development Indicators, 2015)25. Most Bangladeshis have access to Tier 1 and 2 water services, improved and basic water respectively, and close to 98 percent of the population now has access to some kind of technologically improved water source. The MDG water target to halve the proportion of people using unimproved water sources by 2015 was achieved in Bangladesh. Still, higher tiers remain elusive for most; for instance, of the people classified as having access to improved water, about a quarter must still go off-premise to collect it. Moreover, having access to improved water sources does not ensure that water is clean. When considering E. coli and arsenic contamination, only 52 percent of the population has access to a clean and improved water source. Figure 1 below provides a depiction of these statistics.
24
Immediately following the war for independence in 1971, the country was confronted with dire development challenges. Nonetheless, the country has made remarkable improvements: Between 2000 and 2010, Bangladesh’s GDP grew by about 6 percent per year, and the country saw steady decline in its national poverty rates. Despite population growth, the absolute number of Bangladeshis living below the national poverty line has decreased dramatically from 64 million to 48 million. 25 See annex
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Figure 1. National drinking water access by tier (in millions of people), Demographic and Health Survey 2014 and World Development Indicator population 2014 data Source: (World Bank Group, 2017, p. 26)
Though many gains were made in water in the new millennia, Bangladesh failed to achieve the MDG sanitation target due to the high level of sharing between households. However, both urban and rural areas have witnessed significant improvements over time. Between 2006 and 2014, access in urban areas improved from 59 to 84 percent and doubled in rural areas from 32 to 64 percent. When excluding shared facilities coverage falls to 48 percent at the national level and to 53 and 46 percent in urban and rural areas, respectively. Furthermore, more than a quarter of households use rudimentary sanitation facilities that deposit human excreta and black water in open arenas. Only about 11 percent of the urban population is connected to a sewer system (Figure 2).
Figure 2. Access to sanitation by tier (in millions of people), Demographic and Health Survey 2014 and World Development Indicator population 2014 data Source: (World Bank Group, 2017, p. 39)
While in absolute terms, the bulk of the poor still live in rural areas (nearly half of all workers are in agriculture), rapid urbanization is increasing the number of poor in urban areas, commonly referred to as urbanization of poverty. As of 2011, only 29.4 percent of the population is classified as urban. However, based on UN population projections, Bangladesh is expected to be over 50 percent urban by 2047. Although national statistics show the urban population is better off than their rural counterparts, slum populations are often worse off. Urban populations face heterogeneous living conditions and slum environments are an example of extreme intra-city inequality in terms of access to housing and basic services. 30 | P a g e
4b. Housing Deprivation in Dhaka Dhaka's recent history has witnessed an overwhelming growth of its population, chiefly through migration. In 1872, at the time of the first census, Dhaka had a population of 69,212. After the Partition of 1947, the population increased steadily with the arrival of migrants from India. In 1951, the population jumped to 336,000 (Lipu, Jamal, & Miah, 2013). This growth spiked dramatically after Bangladesh gained independence in 1971, practically doubling every decade. Dhaka’s current population is estimated at around 15 million residents and maintains a growth rate of four percent per year. Because of the pressure from rapid and poorly managed urbanization, Dhaka faces many challenges in terms of its livability. The EIU’s livability ranking that examines cities in terms of population density, housing, pollution, affordability, and basic services placed Dhaka among the least livable cities, 137 out of 140 (EIU, 2016). This lower ranking of Dhaka was mostly due to the poor quality of its infrastructure. Due to the city’s topography and constrained land management, Dhaka’s fast growing population has resulted in unplanned urban expansion and the proliferation of slums and squatter settlements. Unauthorized shanty housing has developed on abandoned private or government land, along the highway or on the side of railway tracks and industrial belts. As a result of the government increasing its guardianship over public lands, private property squatting has become more prevalent (CUS et al., 2006, p. 21). Hence, the territorial expansion of the city has been accompanied by internal physical transformations. Encroachment on marginal lands, overuse of water bodies and chaotic waste disposal have largely influenced the natural flow and function of the rivers. All these factors have contributed to environmental degradation and a significant increase of flood risk (Lipu, Jamal, & Miah, 2013). The Bangladesh Bureau of Statistics (BBS) conducted a slum census in the Dhaka City in 1985, followed by another, extended, Census of Slum Areas and Floating Populations in 1997 in all cities and municipalities of the country. The most recent census (in 2014) estimates that over 2.2 million Bangladeshis (or 592,998 households) live in 13,938 slums across the country26. Among them, over 1.6 million reside in Dhaka City Corporation slums. Dhaka division27 alone has nearly half the national slum population (1,061,699), and Dhaka City Corporation accounts for 643,735 slum dwellers. The number of slums in Dhaka has been steadily increasing over the last two decades, from 1,579 communities in 1997 to 3,394 in 2014. In terms of the number of slum households, the 2014 census registered an increase of 77 percent over the 17 years 26
In 1997 only 2,991 slums were recorded and the increase can be attributed to administrative boundary changes and the fact that many big slums in the cities of Dhaka, Chittagong, Khulna, and Rajshahi were evicted in the intervening period scattering inhabitants into many smaller slums. 27 Bangladesh is divided into regional political territories akin to Divisions (large metropolitan areas) and other administrative boundaries (City Corporations, Municipalities and Other Urban granular divisions). The Dhaka division has the largest population of all divisions. It contains four city corporations (Gazipur City Corporation, Dhaka North City Corporation, Dhaka South City Corporation and Narayangonj City Corporation) with many mills and industries located within these cities.
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since the 1997 census. Slum size ranges from less than ten households to well over 100 households. The majority of slums in Dhaka are home to fewer than fifty households; slums cover 12.6% of Dhaka in the northern part of the city and 11.8 percent in the south. Box 2 illustrates the BBS’ definition of “slum”, which—contrary to the UN-Habitat household-level definition—describes characteristics pertaining to a settlement that can vary in size. Box 2: Concepts and Definitions adopted by the Census of Slum Areas and Floating Population 2014 A slum is a cluster of compact settlements of 5 or more households which generally grow very unsystematically and haphazardly in an unhealthy condition and atmosphere on vacant government and private land. Slums also exit on owner-based household premises. Generally, a slum has the following six characteristics: 1. Structures Structures of slums are generally very small such as jhupri, tong, chai, tin shed, semi-pucca structures and dilapidated buildings. Structures of slums are built of low quality materials. 2. Density Population density and the concentration of structures are very high in a slum area. Density can be looked at in two ways: i) Rooms are crowded: Generally, all members of the household live in one room; ii) Structure density: Three or more structures are situated in one decimal of land. 3. Land Ownership Slums generally grow on government and semi-government land, vacant private land, abandoned building/houses, and hillsides or rail lines and roadsides. 4. Water supply and sanitation In slum areas, water supply is insufficient and unsafe. Sanitation systems are quite inadequate (i.e. 15 or more people share one toilet). Overall, a very unhygienic environment exists in slum areas. 5. Lighting and road facilities Lighting and road facilities are very inadequate or non-existent in the slum areas. 6. Socio-economic conditions Socio-economic status of slum dwellers is very low. Slum dwellers are generally engaged in informal non-agricultural jobs. Only a few of them who are living in the district or Upazila level, might own a small parcel of agricultural land. Source: Adapted from Bangladesh Bureau of Statistics (2015, p. 14)
The census and a few other empirical studies on the slums of Dhaka suggest the existence of large disparities in access to and quality of basic service provisions in slums when compared to urban non-slum areas. For instance, access to water in slums is similar to coverage in other urban areas, but the percentage of shared water sources is almost double that of urban non-slum areas. Similarly, most residents in the slums of DCC share sanitation facilities with more than 10 households (see BUISBS survey results discussed in Section 5a). According to CUS (2006), only 10 percent of slums had sufficient drainage to avoid water-logging during heavy rains. Over half were fully or partially flooded at times when the country experienced general flood conditions. Another revealing figure indicates that under-5 child mortality and stunting is consistently higher in urban slums (~ 50%) than in any other stratum (World Bank Group, 2017, p. 85). These findings indicate the existence of increased vulnerability of slum inhabitants due to the risk of contamination and related adverse health effects. 32 | P a g e
5. Data Two types of data were utilized for this study: household survey data (Section 5a) and Earth Observation (EO) data (Section 5b). Household survey and EO data form the dependent and independent variables respectively for the regression model. 5a. Household Survey Data on Slums This study relies upon an existing dataset, the Bangladesh Urban Informal Settlements Baseline Survey (BUISBS), collected for the Bangladesh WASH Poverty Diagnostic in May 201628. The survey included questions on access and quality of water and sanitation services, adequate living space, type of housing structure, security of tenure, and access to electricity. The survey was designed to collect data from 600 slum households across 63 slum communities in the DCC. The sampling was done in two stages. In the first stage, slums were selected as the Primary Sampling Unit (PSU) using probability proportion to size (PPS) and in the second stage households were selected from those slums using systematic equal probability sampling. The sampling frame for the first stage was generated from the 2014 BBS Census of Slums and Floating Population, which consisted of 3,360 slums. The BBS Census classified slum communities into three strata: (1) small slums (5-10 households); (2) medium slums (11-200 households); and (3) large slums (more than 200 households). The survey team decided to sample five households from each small slum (strata 1), and 10 households from each medium (strata 2) and large (strata 3) slum. The team also decided 50 percent of all household samples would come from large slums and the remaining 50 percent from small and medium slums combined. Table 1 provides the details for the sampling frame from the BBS Slum Census 2014 and BUISBS sample.
Table 1. Sampling frame and sample for BUISBS Source: internal project preparation documentation
The BUISBS survey consisted of five modules: i) roster and individual level characteristics; ii) household characteristics; iii) housing conditions; iv) water and sanitation; and v) consumption of food, non-food, and durables. Relevant data were extracted from the first four modules to construct housing deprivation measures and indices as described in Section 6.
28
This section largely borrows information from the internal project preparation documentation of Bangladesh WASH Poverty Diagnostic and an unpublished manuscript shared by the project team.
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Data were collected over a period of 16 days between May 12 and May 28, 2016 with 30 teams of two interviewers. Teams were assigned slum communities based on practical considerations (e.g. distance to informal settlements). The completed survey was representative of all slums in the DCC and the final sample included a total of 588 slum households (98 percent response rate). The sample distribution included 30 households from small slums, 259 households from medium slums and 299 households from large slums. The original dataset did not include the geographic locations of the surveyed households (required to link household survey data with the EO data). These data were collected separately in June 2017 for all 63 slums. However, it was no longer possible to collect household level locations due to time and resource constraints. Consequently, most of the analysis relies on aggregate measures at the slum level for this pilot study. In the second phase, all household surveys will be geo-referenced providing more nuanced versions of the models presented in this report. Table 2 summarizes the profile of slum households and residents. A typical slum household has 4.3 members, household heads are on average 39 years old and have been living in the slum for around 8.6 years, suggesting housing deprivation is rather permanent. Most adult members in the household are income-earners, with some gender imbalance in work participation with 89 percent of males and 50 percent of females engaging in economic activities. Characteristic
Small
Medium
Large
Total
Number of slum residents
167,488
2,907,968
4,140,496
7,215,952
Number of slum households
37,947
686,637
959,623
1,684,207
2.3
40.7
57.0
100
3,956
4,723
4,208
4,410
Household size
4.3
4.2
4.3
4.3
Dependency ratio (%)
80.8
76.1
68.9
72.1
Age of household head (in years)
45.2
38.1
39.2
39.0
Female household head (%)
0.0
4.6
4.7
4.5
Duration of residence household head (years)
7.5
5.7
10.7
8.6
Literacy rate for adults (%)
65.3
46.9
46.6
47.2
Completed primary school for adults (%)
29.3
16.5
17.8
17.5
School attendance children 7-12 years (%)
94.1
81.4
82.1
82.0
Monetary poverty Distribution of sampled slum households (%) Monthly per capita expenditure (in Taka) Demographics and human capital
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Household received relief program (%)
3.4
3.8
5.0
4.5
Share of male adults who are earners (%)
94.4
89.9
88.2
89.0
Share of female adults who are earners (%)
40.9
52.8
48.7
50.1
Main source of income from industry (%)
6.9
4.2
15.1
10.5
Main source of income from services (%)
93.1
95.8
84.9
89.5
Per capita rooms
0.32
0.32
0.33
0.33
Room size (in sq. meters)
12.1
11.8
11.7
11.8
Share of renters (%)
96.6
82.2
61.2
70.6
Monthly rent (in Taka)
2,849
2,692
2,390
2,548
Has separate kitchen (%)
55.2
87.9
63.4
73.2
Government owned land (%)
17.2
25.5
88.0
60.9
-
1.15
1.7
1.4
Private owned land (%)
82.8
71.8
7.0
35.1
Other owned land (%)
-
-
0.3
0.2
Do not know land ownership (%)
-
1.6
3.0
2.4
Fear of eviction (%)
17.2
21.8
71.6
50.1
Permanent dwelling structure (%)
24.1
12.0
11.4
11.9
Semi-permanent dwelling structure (%)
37.9
36.7
22.8
28.8
Tin-shed dwelling structure (%)
34.5
51.2
65.8
59.2
Jhupri dwelling structure (%)
3.4
0.0
0.0
0.1
Access to improved drinking water (%)
100.0
95.4
98.3
97.2
Access to improved sanitation (%)
100.0
93.9
80.5
86.4
Electricity is main source of light (%)
100.0
99.2
93.6
96.1
Labor markets
Housing
Non-government owned land (%)
Access to basic services
Table 2: Basic profile of slum households and slum residents (Source: unpublished manuscript titled “The Informal Markets for Water and Sanitation Services in Urban Slums: A case study of the Dhaka City Corporation in Bangladesh” by Arias-Granada, Y., Haque, S., Joseph, G., Yanez-Pagans, M.”
Overcrowding, poor housing conditions, and fear of eviction are salient features of lives in slums. One room is normally shared by three people and rooms are on average 12 square meters in area. Housing structures are largely constructed using low quality materials including tin-sheds (59 percent of housing structures) and semi-pucca (29 percent of housing structures). Only 12 percent of housing units are constructed with 35 | P a g e
permanent construction materials. Over half of slum residents live under fear of eviction. Slums are also dynamic rental housing markets with more than 70 percent of slum residents reporting renting their dwelling. Around 61 percent of slums in Dhaka are located on land reported to be government owned, while 35 percent are located on privately owned land. Interestingly, access to public services such as water and sanitation in slums is very high. 5b. Earth Observation Data on Slums The remote sensing part of the study was completed under the EO4SD-Urban program (2016) and showcased one of the products provided by the EO4SD-Urban consortium called “Extent and Type of Informal Settlements/Slum Areas.” This analytical product targets the spatial location, extent, characteristics and spatial-temporal development of building agglomerations that can be classified as informal settlement/slum areas. These areas share a number of physical and morphological characteristics, patterns and contextual parameters (compactness, patch size, density and size housing structures, land cover share, proximity to infrastructure, etc.) that allow their discrimination from other residential areas using Very High Resolution satellite imagery. Considering these areas are often found in specific locations like river banks, close to transport networks or next to industrial or commercial units, this analysis is based on the premise that the physical appearance of a human settlement consistently reflects certain socioeconomic and demographic characteristics. For details on the analytical process used for this purpose by GISAT, see Annex A. The study area corresponds to the administrative boundary of the Dhaka Metropolitan Area, which is a police jurisdiction area that comprises forty-one “Thanas” (the lowest among four administrative tiers in Bangladesh) with an area of about 300 km2 and a population of 8,906,039 (BBS Census, 2011, p. xi). EO data were obtained via analysis of Very High Resolution (with pixel spacing in the range of 0.5 – 0.6 m) multispectral satellite images acquired for two reference years (2006 and 2017). In addition to EO data, open data spatial datasets, such as OpenStreetMap, were also used. The initial slum identification and delineation step was facilitated using historical datasets obtained and shared by a research consortium led by Columbia University29 (data and methodology are described in Gruebner et al., 2014). The final shapefiles (polygons) show all the slum neighborhoods and patches the researchers detected in Dhaka in either 2006 or 2010. 1. 2.
GISAT experts visually inspected the polygonal results of the study using QuickBird (2006) imagery and Google Earth timescale. After validating the 2006 GIS output for spatial consistency, the identified polygons were used as samples to train a semi-automatic object-based (OBIA)
29
The study “Mapping the Slums of Dhaka from 2006 to 2010” was done by researchers from Columbia University, Tulane University, Humboldt-Universit¨at zu Berlin, University of Bielefeld. The accompanying paper (Gruebner et al., 2014) describes the background, methods and outputs of the research on slums and urban expansion in Dhaka.
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3.
classification system of multispectral Pleiades imagery (2017) to follow a similar approach to identify and delineate built-up areas of the same characteristics (in terms of morphology and structure) in the 2017 slum status product. The Gruebner GIS output from 2010 was then used as a baseline to check and enhance the 2017 slum polygons by means of computer-assisted visual inspection and interpretation also using Pleiades imagery (2017). Subsequently, GISAT derived a number of physical, morphological and contextual characteristics for identified slum areas using automatic object-based (OBIA) techniques, focusing on an extended list of measures (see Table 6) for the 63 slum communities for which location data were available from BUISBS survey.
Once the slum polygons were identified and delineated, several dimensions related to housing that could be extracted from EO data that may explain variation in housing deprivation in slums were derived. These dimensions included measures on locational aspects (e.g. proximity/distance to central business district), access (e.g. proximity/distance to arterial road), hazard susceptibility (e.g. terrain slope), morphology (e.g. built-up area’s homogeneity), relational measures about space (e.g. built-up density), and structural measures (e.g. dwelling size). Several measures within each of these categories were used as explanatory variables for the statistical models. These measures were extracted for the 63 slum communities for which location was available.
Figure 3. Example of location of slum communities as derived from WASH-POV. Slum areas (in pink) and buffers around slum communities (in purple).
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Figure 4. Example of LU/LC classification within slum and surrounding areas used for LULC structure indicators
Figure 5. Example of quad-tree segmentation within built-up mask used for an estimation of area homogeneity indicator
Figure 6. Example of mean dwelling size derived comparing units with different size settings
These measures could also be extracted at the household level by creating a buffer of adequate dimension, an approach that will be used in the second phase of this project.
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6. Methodology The goal of the proposed statistical model is to predict housing and basic services deprivation in slums using EO data requiring measures of housing and basic deprivation. Section 6a describes how these were derived and presents a comprehensive measure of overall deprivation that combines multiple indicators into a single index, the Slum Severity Index (SSI)30. 6a. Measures of Deprivation in Housing and Basic Services
Six variables from the household survey were used to derive measures of housing deprivation: i) access to drinking water, ii) access to sanitation, iii) adequate living space, iv) permanent structure, v) security of tenure, and vi) access to electricity. All the outcome variables were negatively coded and dichotomized: 1 for lack of access, and 0 otherwise. These criteria build on three UN-Habitat definitions described in Box 1. The UN-Habitat definition, however, does not explicitly state what constitutes lack of access to water and sanitation. Security of tenure, adequate structure, and adequate living space are equally ambiguous. The following assumptions were therefore made to operationalize each criterion: Lack of Access to Drinking Water: The World Health Organization (WHO) and the United Nations International Children's Emergency Fund (UNICEF) developed a guideline on water and sanitation for household surveys in 2006. The guideline was followed in the BUISBS survey. For access to drinking water, WHO and UNICEF (2006) classify water sources into improved and unimproved wherein improved sources include piped water to the dwelling, piped water to the yard, public tap/standpipe, and tubewell or borehole. Unimproved access includes tanker truck and bottled water. We took access to improved sources as equivalent to having access to water whereas access to unimproved sources were considered as equivalent to lacking access to water, as suggested by UN-Habitat (2006b). Unimproved access was coded as 1, and improved access was coded as 0. Lack of Access to Sanitation: Similarly, WHO & UNICEF (2006) also classified sanitation facilities into improved and unimproved. Improved facilities include flush to piped sewer systems, flush to septic tank, flush to pit latrine, flush to somewhere else, flush to unknown outlets, and pit latrine with slab, while unimproved facilities are comprised of pit latrine without slab/open pit and no facility/uses bush/field. We assumed improved sanitation as equivalent to having access to sanitation, and unimproved sanitation as a lack thereof. Unimproved sanitation was coded as one, and improved sanitation was coded as zero. Lack of Adequate Living Space: This indicator is based on the UN-Habitat (2006) guideline that suggests a threshold of three people per room to determine overcrowding. The questionnaire had two separate questions on this topic: one on the 30
A detailed description of the index and its implications for urban planning and policy could be found in Patel et al. (2014).
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number of rooms in the house and another on the number of people living in the house that was derived from the household roster. These questions allowed us to calculate the number of people per room for each household. Greater than or equal to three persons per room is coded as one to indicate lack of adequate living space, and less than three persons per room is coded as zero. Lack of Permanent Structure: The Bangladesh Census classifies housing structures into three categories: 1) jhupri/kachcha/tin shed, which includes houses made with temporary materials; 2) semi-pucca, which encompasses houses made with semipermanent materials; and 3) pucca, which indicates houses constructed with permanent materials. BUISBS used the same approach to classify housing structures. We took a conservative approach to assume the first category of temporary dwellings lacks permanent materials and thus coded jhupri/ kacha/tin shed as one and semipucca/pucca as zero. Lack of Secure Tenure: This indicator is based on a question in the BUISBS survey that captures the occupant’s perceived security of tenure. The approach is deemed appropriate especially in the Bangladeshi context where the property rights regime with respect to land is fragile. Lack of perceived security of tenure was coded as 1 and perceived security was coded as 0. Lack of Access to Electricity: This indicator is based on a question in the BUISBS survey that captures a household’s main source of light. Access to electricity has been coded as zero and all other sources of energy i.e. Kerosene, Solar Electricity and Others are coded as one. Housing Deprivation
% Households
Lack of Access to Water
3.6%
Lack of access to sanitation
12.8%
Lack of adequate space
74.5%
Lack of Durable House Structure
58.2%
Lack of secure tenure
46.6%
Lack of Access to Electricity
3.6%
Table 3. Types of Housing Deprivation in Dhaka slums according to the BUISBS (2016)
Table 3 provides summary statistics for each of the measures discussed in this subsection for the sampled households in Dhaka. Lack of adequate space is the most prevalent problem in slums whereas access to water and electricity seems almost universal. These decomposed measures provide a better understanding of housing deprivations compared to a simple slum/non-slum dichotomy for urban planners and policymakers. In addition, they provide readily useful inter- and intra-slum information 40 | P a g e
for policymakers by rapidly identifying which dimension of housing and basic services requires attention. 6b. Slum Severity Index: A Comprehensive Measure of Deprivations
The six binary variables of housing deprivation described above were used to construct a Slum Severity Index (SSI) proposed by Patel et al. (2014) to derive an aggregate measure of housing deprivation. The original index is an aggregate measure of housing deprivation across five aspects of housing as proposed by the UN-Habitat’s (2010) definition of slums. In this project access to electricity has been included as the sixth housing dimension to construct a more comprehensive SSI. The SSI ranges from 0 to 6; a score of 0 indicates the household is not deprived of the measures described above, and a score of 6 indicates the household is deprived of all six elements.
Figure 7: Distribution of Households by Slum Severity in Dhaka, according to BUISBS findings in 2016 survey.
Figure 7 provides the distribution of households by slum severity. While no households in the surveyed sample lack all six elements of housing and basic services, one quarter are deprived in three or more areas. Interestingly, seven percent of households are not deprived in any of the six areas despite being located in official a slum neighborhoods. This exemplifies how this comprehensive measure could help policymakers identify the most deprived slums in the city. 6c. Community level aggregation
Individual measures of deprivation as well as the SSI are flexible in the sense they could be aggregated at slum level. Slum level measures of housing deprivation were created by calculating the percentage for each slum (e.g. percent of households deprived of water). Similarly, household level SSI was aggregated at the slum level by taking a simply averaging the household scores within a given slum. 6d. Tiered Approach to Access and Quality of Water and Sanitation Services
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The BWPD classification (which builds on JMP31 definitions) of levels of access and quality of water and sanitation services was used and access and quality were divided into four tiers each. A four-level variable, measuring service levels in addition to the simple binary variable described in section 6a, was also created. The SSI could potentially be created using these measures. Such an index would provide more nuanced measures of deprivation and capture the heterogeneity across slums in a greater detail. Access and quality of water in the slums of Dhaka, Bangladesh is reported in Table 4. It is important to note that while only 3.6 percent of households had a complete lack of access to improved water, when considering tier 4 (which represents piped water supply on premise), 37.2 percent of households are deprived. A tiered approach adds quality to the simple binary access measurement and hence is important for policymaking that aims to focus on improving quality of service. Levels of Access and Quality of Water
% Households
BWPD classification
Total Unimproved (T0)
3.6
Unprotected springs, unprotected dug wells, cart with small tank/drum, tanker-trunk, surface water, bottled water
Total improved (T1)
96.4
Piped water to yard/plot, public taps or standpipes, tube wells or boreholes, protected springs, rainwater
Improved + 30 min (T2)
92.4
Satisfies JMP “Improved” technology and w/in 30 min round trip collection (improved and proximal)
Improved on premises (T3)
70.1
Satisfies JMP “Improved” on premise
Piped on premises (T4)
62.8
Satisfies JMP “piped water” on premise
Table 4. Access and quality of water in slums of Dhaka, Bangladesh.
Access and quality of sanitation is reported in Table 5. The vast majority of households (92.6 percent) have not achieved the highest level of access and quality, tier 3 (which represents private sewer connection and private unshared sanitation facility). Levels of Access and Quality of Sanitation Open Defecation
% Households
BWPD classification
2.9
31
The Joint Monitoring Programme for Water Supply and Sanitation is hosted by WHO and UNICEF and is the official UN mechanism for monitoring MDG and now SDG progress in WASH.
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Unimproved (T0)
12.8
No facilities/bush or field use of bucket, hanging toilet/hanging latrine, flush/pour to elsewhere
Improved (including shared) (T1)
78.7
Shared “improved” facilities including flush toilet to piped sewer system, septic tank, or pit latrine; ventilated improved pit latrine (VIP), pit latrine with slab, composting toilet
Improved (excluding shared) (T2)
8.5
Unshared “improved” facilities
Private Sewer connection (T3)
3.4
Unshared improved sanitation facility that is connected to sewer
Table 5. Access and Quality of Sanitation in slums of Dhaka, Bangladesh.
6e. Sustainable Development Goals Indicators
A similar approach to that described above but using additional dimensions was used to assess the SDGs (Table 6). Similar trends were observed in SDG indicators; lack of access to water and sanitation were high and comparable to the highest tier of services. Since SDG focuses on both quality and access of these services, it is worthwhile to note that Dhaka is far from providing services to meet these goals for slum-dwellers. SDG Indicator
% Households
Lack of Access to Water
35.5
Lack of Access to Sanitation
78.7
Table 6. Access and Quality of Water and Sanitation by SDG definition in slums of Dhaka, Bangladesh.
6f. Explanatory Variables
After identifying and classifying “slum-like” neighborhoods, a list of indicators (Table 7) was compiled to characterize informal settlements/slum areas at neighborhood and/or household level. These are based primarily on VHR imagery, interpreted EO-based products (e.g. EO4SD LULC, EO4SD Flood) or other open data (e.g. OSM) or these any of these datasets combined. Indicator Group / Indicator
Indicator type
Input Data
Location type
Qualitative
Imagery, LULC
Distance to paved road
Quantitative
OSM, LULC
Distance to railroad
Quantitative
OSM, LULC
Distance to center/CBD
Quantitative
LULC
Neighborhood locational
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Distance to nearest important connectivity node
Quantitative
OSM, LULC
Distance to (heavy) industry
Quantitative
LULC
Distance to shoreline (river, canal, lake or sea)
Quantitative
LULC
Distance to any user provided feature set (water distribution points, sewage, etc.)
Quantitative
Field survey / GPS
Distance to arterial (capacity) road
Quantitative
OSM, LULC
Distance to (selected) public services
Quantitative
OSM / GPS
Occurrence of feature within X meters
Qualitative
OSM, LULC
Road network "winding index"
Quantitative
OSM
Density of road network
Quantitative
OSM
Structure of road network typology
Qualitative
OSM
Road connectivity – end point nodes
Quantitative
OSM
Road connectivity – junction point node weights
Quantitative
OSM
Road connectivity – junction point nodes
Quantitative
OSM
Terrain slope
Quantitative
DEM
Geomorphology of terrain
Qualitative
DEM
Landslide risk
Qualitative
DEM, soils
Flood inundation risk (river, tsunami, storm surge)
Qualitative
EO4SD Flood product
Distance to technological hazards
Quantitative
LULC, GPS
Open sewers
Qualitative
GPS
On dump and hole garbage filling
Qualitative
GPS
Area
Quantitative
GIS
Perimeter
Quantitative
GIS
Shape compactness
Quantitative
GIS
Neighborhood accessibility
Neighborhood vulnerability to environmental hazards
Neighborhood shape morphological characteristics
Neighborhood LULC proportional characteristics
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LULC structure in surroundings
Quantitative
GIS
Built-up homogeneity
Quantitative
OBIA
Built-up density
Quantitative
OBIA
Open spaces density
Quantitative
OBIA
Greenness density
Quantitative
OBIA
Mean dwelling size
Quantitative
Imagery
Main direction
Quantitative
Imagery
Compactness
Qualitative
Imagery
Mean dwelling separation
Quantitative
Imagery
Mean estimated dwelling height
Quantitative
Imagery
Roof heterogeneity
Quantitative
Imagery
Roof & masonry material
Qualitative
Imagery
Estimated dwelling age
Qualitative
LULC
Lacunarity of housing structures
Quantitative
Imagery
Neighborhood internal structure
Neighborhood dwelling characteristics
Table 7. List of candidate characteristics (indicators) derivable for informal settlements/slum areas
6g. Regression Modeling
Slum level models were created using aggregate dependent (household survey) variables. Since most aggregate measures can be treated as count variables (e.g. total number of households lacking a housing element in a slum), Poisson regression modeling was applied. In the case of overcrowding, assumptions for Poisson regression did not hold hence an Ordinary Least Square (OLS) regression model was constructed using the percent of households deprived as an outcome measures. There are seven models in total (six for each of the measures of deprivation in housing and basic services, and the seventh for the aggregate SSI); six of them used Poisson regression and one of them (overcrowding) used OLS regression. Equation 1 presents a generic equation for all six Poisson regression models.
Y = eln(E)+βX
(1)
where Y represents the expected count of deprivation in a slum. X represents explanatory variables derived from EO data (e.g. locational aspects, access, hazard 45 | P a g e
susceptibility, morphology, relational measures about space, and structural measures) and E represents the number of household surveys. Equation 2 presents the equation for the OLS regression model.
Y= β0 + β X + ε
(2)
where Y represents a dependent variable (overcrowding) in slums and X represents the explanatory variables derived from EO data. Explanatory variables were selected using the standard stepwise regression procedure that involved selecting the variables that minimize the model’s AIC value through progressive steps, wherein improvements are made to the fitness of the model as each variable is included (forward option) or excluded (backward option) from the initial model. All stepwise regressions were run using Stata 15 software. Over-dispersion in count data was verified and the likelihood ratio test of alpha used to check if Poisson regression was appropriate or if negative binomial regression should be considered to tackle potential over-dispersion. The percent of households with a lack of access to a particular housing element in each slum could have been calculated and used as dependent variables. Similarly, the average SSI for each slum could have been calculated. Since both percentages and average SSI could be treated as continuous variables, Ordinary Least Square (OLS) regressions may have worked for each of the indicators. However, estimations from percentages and averages based on a sample of only five to ten households per slum would have likely suffered as a result of the small sample size. In addition, the survey dataset includes a relatively small sample of 63 slums, which provides limited statistical power for estimating a model. This is especially true for a count model such as Poisson regression that usually requires a larger sample size. Different model specifications (OLS, Poisson and Negative Binomial as well as inclusion or exclusion of certain variables) produced very different results in terms of coefficients, directions, and significance for some of the explanatory variables. Therefore this project seeks only to demonstrate the approach and models have been constructed primarily for illustrative purposes. This is not to say the results will not hold with a larger sample (as intended in second phase of this project), however, given the limited statistical power due to small sample size they should be interpreted with caution. The approach will nonetheless remain the same for larger sample sizes, which are expected to improve estimations. 6h. A framework from data to evidence-based policymaking
As noted earlier, the goal is to provide a framework that serves as a basis for designing appropriate policies for slums and targeting interventions in the most efficient manner possible. For example, some slums may require improved sanitation while others may 46 | P a g e
need interventions to improve the quality of water services. For specific slum policies such as in-situ slum upgrading, it may be important for policymakers to identify slums where secured tenure may be a challenge since it is generally the first step for in-situ upgrading. Even if slum upgrading may not be a possibility, a map indicating various levels of services could benefit local governments and utilities to identify areas where they could find opportunities to provide new services or improve existing services such as for water and electricity. However, estimating these needs in a timely manner and in a cost-effective way is not an easy task. As outlined in Figure 8 below, evidence for the entire city could be developed with a comparatively small number of data points from existing households surveys by integrating those data with data obtained from satellite images and employing regression analysis. Using the resulting regression models, deprivation measures could be predicted for all the slums in the same city. Ultimately, a series of maps could be generated to provide an overall picture of slum severity in the study city.
Figure 8. A framework for evidence-based slum policy-making.
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This report does not advocate modeling for the sake of modeling. Nor that models and procedures must be advanced and complex to be of value. Rather, a lot could be gained by simply combining existing datasets in the proposed analytical framework at a much lower cost with minimal time to inform policy making as well as to conceive new interventions.
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7. Analysis and Results The regression modeling with housing and basic service deprivation measures as a function of EO based variables revealed important relationships as originally hypothesized. Table 8 provides a summary of each of the seven multiple regressions. Detailed regression results with coefficients, incidence rate ratios, and statistical significance along with robustness checks are provided in Annex C. This section briefly summarizes significant results from this analysis.
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Table 8. Multivariate Regression Results Summary for Deprivation measures as a function of EO data. Earth Observation Variables Locational Characteristics Distance to Central Business District (in km) Distance to Paved Road (in km) Distance to Arterial Capacity Road (in km) Distance to Railroad (in km) Distance to Heavy Industry (in km) Distance to Shore Line (in km) Distance to Major Road Junction (in km) Neighborhood Characteristics within Slums Percentage Built-up Area Mean Dwelling Size (in sq m) Mean Dwelling Separation Built-up Homogeneity Accessibility and Connectivity Characteristics Distance to the Nearest Hospital Distance to the Nearest Place of Worship Distance to the Nearest School Distance to the Nearest University Land Use Land Cover Characteristics within Slums Percentage High Density Continuous Residential Urban Fabric Percentage Commercial and Industrial Non-residential Urban Fabric Percentage Urban Green Space and Sports Facilities Percentage Water Bodies Street Pattern Characteristics within Slums Low-level Connectivity Node Ratio 100 m buffer Road connectivity - junction point node weights Total Number of Junction Nodes Total Number of End Point Nodes Percentage Road Typology – Informal Roads Percentage Road Typology – Local Roads Percentage Road Typology – Primary Roads Percentage Road Typology – Secondary Roads Percentage Road Typology – Tertiary Roads Road Winding Index – Informal Roads Road Winding Index – Local Roads Road Winding Index – Primary Roads Road Winding Index – Secondary Roads Road Winding Index – Tertiary Roads Road Network Density – Informal Roads Road Network Density – Local Roads Road Network Density – Primary Roads Road Network Density – Tertiary Roads
SSI
Water
Sanitation
é
é ê é
ê
é é ê ê é ê é
é ê ê ê ê ê
Outcome Variables Overcrowding
Tenure
Electricity é
é ê ê é ê
é é é ê ê é
ê
ê é
é ê é é
é ê é é
ê
é ê ê
é
ê
ê é
ê ê ê ê
ê
ê é ê
ê é ê
ê
é
ê
é é é é é é ê
Structure
é ê é é é é é ê
é é ê
ê ê
é
é
ê é ê ê
ê
ê ê
ê
é ê é ê é ê ê
ê é
ê
é ê ê ê ê ê é ê
ê ê ê ê ê ê ê
é ê
Note: All results are based on multivariate regressions. and denote a statistically significant increase and decrease in deprivation respectively.
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7a. Locational Characteristics as Predictors
Many of the locational characteristics of slums derived from EO data were statistically significant predictors of housing and basic services deprivation. Of note is the distance to Central Business District (CBD). With increasing distance, slums were more deprived of services water and electricity, whereas it had no association with sanitation, overcrowding, or structure. With the exception of sanitation, this finding concurs with what is generally referred as “peripheralization of slums”, indicating decreasing access to services as new slums form in the peripheries of a city. Another interesting set of indicators was related to proximity to transportation networks and employment opportunities such as heavy industries. Generally, deprivation in slums was lower for slums close to roads, in particular water and electricity services improved for those who were closer to arterial roads. Interestingly, as slums moved farther away from railroads or major road junctions, their housing deprivation increased. Proximity to heavy industry also resulted in higher housing and services deprivation indicating a potential trade-off between residing near a workplace at the cost of lower quality housing, a phenomenon that is generally observed. 7b. Neighborhood Characteristics within Slums as Predictors
All indicators of density were associated with an increase in housing and basic services deprivation. Average dwelling size and average distance between two dwellings essentially measure housing density. In addition, percentage of built-up area in any slum neighborhood is another direct measure of overall density. Effectively, the more crowded the slums were, the more deprived they were. This is not surprising since residential density is often a signature manifestation of living conditions in slums. 7c. Accessibility and Connectivity Characteristics as Predictors
Distance to social amenities such as schools, universities, hospitals, and places of worship could be important predictors of housing conditions. The hypothesis here was that proximity to social amenities is a desirable characteristic that might be associated with improved housing conditions yet the results were mixed. As the distance from amenities increased, some housing conditions improved while others worsened. The relationship between distance to amenities and housing conditions might not be straightforward and may require additional data to make plausible sense of these findings. 7d. Land Use and Land Cover Characteristics as Predictors
Various land use and land cover characteristics were significant predictors of housing and service deprivations in expected directions. For example, as the percentage of highdensity residential urban fabric increased for a slum, housing deprivation decreased. Similarly, commercial and industrial land use and percentage of water bodies also 51 | P a g e
reduced housing and basic service deprivation. Finally, housing deprivation also decreased with increasing percentage of green space. 7e. Street Pattern Characteristics within Slums as Predictors
Several measures of street patterns within slums were associated with housing and basic services deprivation. As expected, higher percentages of informal roads and winding roads were associated with higher service deprivations. Slums with road networks that had lower level accessibility were also more deprived in terms of services. There were some mixed results in this category that may require further investigation with larger datasets. 7f. Overall Predictive Power of Models
Despite several limitations of sample size and data, these models demonstrated a high degree of goodness of fit and indicators derived from EO data successfully predicted housing and basic service deprivation at the aggregate level (e.g. SSI) as well as the decomposed level (e.g. individual elements such as water and sanitation). This promising approach could provide useful insights for planning and policymaking. Further, using only a small subset of data, these models could predict deprivation as a function of EO data for the entire city.
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8. Planning and Policy Implications Current planning and policy approaches rely on household surveys to estimate demand for urban services such as water and sanitation. However, these surveys are often expensive and time-consuming. Consequently, they are not conducted on a regular basis. On the other hand, resolution, availability, and affordability of EO data have improved drastically in the last two decades. This model provides a pathway to estimate demands for urban services by leveraging this readily available EO data. This approach also provides almost real-time estimates that could be especially useful given slums in the developing world are rapidly growing and constantly changing. For national governments, this methodology provides an efficient and cost-effective way to estimate household deprivation and demand for city services nationwide. Given that remote-sensing images are available for entire nations simultaneously, there is far less time lag between data acquisition and use in decision-making than traditional surveybased methods of data collection and analysis. Additionally, due to the growing availability of technologies like OpenStreetMap that allow residents to contribute data, geospatial analysis can also take place in real-time using updated information. The possibilities of targeting public expenditure and development on the communities and regions that most need assistance is close at hand. International development organizations, likewise, can now use this methodology to inform both national and global interventions. By estimating global housing deprivation in real-time, without conducting large-scale surveys, funding can be directed to the nations with the most pressing need for infrastructure development. These types of approaches also contribute to increased knowledge transfer capabilities amongst countries and sector’s institutions to further strengthen urban monitoring systems and the evidence presented should not be seen only as a slum mapping exercise but rather as slum characterization, which is useful to identify pockets of poverty, spaces where development is lagging and areas with scarce provision of basic services. Finally, although this study covered a single city, Dhaka, the approach is applicable to other cities in developing countries since similar surveys are routinely collected around the world and remote sensing data are increasingly become available for all these cities. While a survey alone cannot support citywide program implementation, this approach provides a framework that could be applied to predict citywide deprivation once a model is constructed. It is clear that decision-making should be neither driven by data availability nor by statistical techniques. It is important to first develop a clear model. Data on deprivation could be collected and statistical techniques applied only after a clear model is established. To this end, this study contributes to the development of such a model in the context of measuring housing and basic services deprivation from
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space, which could precede data collection on deprivation at the household level for future surveys and censuses in any city where slums are prevalent. In an era of increasing urbanization, this study and the use of EO data for exploring housing deprivation have particular relevance and life-changing implications for residents of slums and informal settlements. Given it has been traditionally challenging to chart even the location and growth of slums due to their transient nature, this method will greatly increase the efficiency in designing and implementing, prioritizing or even expanding, basic services in impoverished informal settlements. Continued investment in the advancement of this technology and methodology will, in the longterm, open new doors to improving the quality of life for over a quarter of the world’s urban population that live in slums today
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9. Limitations and Way Forward While the results presented here are very promising, there are some limitations in this pilot analysis that are worth noting. Most of these arose from the lack of an enhanced dataset limiting the benefits that could be derived from the proposed framework. In response, improvements to the dataset have been identified for the second phase. This section briefly describes first, some of the data specific limitations and second, general limitations of the approach while highlighting opportunities to overcome them in the second phase. Aggregated slum level data limited the regression models’ statistical power and future applications for policy making. While the original sample was a sizeable 588 households, location data were only available at the slum level, which reduced the aggregated sample to 63 slums. In addition, only five to ten households had been surveyed in each slum and aggregate measures (e.g. percentage of households with water deprivation) suffered from generalizations as a result of the small sample size. As a result of the aggregation and subsequent smaller sample size Poisson regression was employed as discussed in Section 6g. However, any regression specification that is meant for count data such as Poisson regression requires a much larger sample size. While the results may not be inaccurate with a sample size of 63, they will be much more robust with a sample size greater than 100. In fact, a sample size over 500 is desirable for Poisson regressions. Furthermore, policy responses differ based on the type of deprivation (Nandi and Gamkhar, 2013). Often, household level data are aggregated to the neighborhood level, resulting in a loss of heterogeneity among households. While such aggregations are useful for community level interventions and city level comparisons, they are less useful for household level interventions. Moving forward, regression models will be constructed using household level data increasing the sample size and the statistical power significantly. The second round of BUISBS has already been concluded and includes household level GPS locations for all 600 households surveyed. Future model specifications will be logit and probit for binary deprivation measures (e.g. a household’s access to water) and ordered logit for the SSI (that will range from 0 to 6 for each household). This approach will overcome the aggregation bias, which is inevitable in slum level analysis using household survey data and allow planners and policy makers to design and implement more targeted slum policies. Actual deprivation may be underrepresented owing to narrow definitions and inherent assumptions used when measuring deprivation. The UN-Habitat (2010) definition of slums upon which this model was based is conservative in the sense that it underestimates the deprivation of slum households. This is primarily because the definition is restricted to the physical and legal characteristics of slums and eschews the 55 | P a g e
social dimensions (e.g. concentrated poverty) that are often difficult to measure (Davis, 2006). Nonetheless, we can expect that the underestimation is partially corrected as physical deprivation is highly correlated to social and economic marginalization (Arimah, 2001; Begum & Moinuddin, 2010). In addition, when a slum dweller responds, “yes” to a water availability question, it may not accurately capture access to water since the question does not focus on quantity or quality, time spent collecting water, etc. The second phase of the project aims to utilize the multi-tiered approach presented in section 6d that captures various aspects of access and quality for water and sanitation. The details for each measure captured (e.g. levels of access and quality for electricity) will be used to construct nuanced versions of the dependent variables including an enhanced SSI. Limited local expertise and low quality geo-referencing data may increase the uncertainty of slum identification. Remote sensing identifies probable informal settlements by localizing areas with visible physical characteristics typical of slums in the area and is capable of recognizing patterns of deprivation in areas that have yet to be formally identified. However, local expertise and/or field validation are needed to confirm the results. Further, the accuracy of matching EO-derived characteristics with household survey data depends on the quality of geo-referencing data. While buffer zones can help limit these uncertainties, ideally household surveys should strive to include high quality geo-referencing data at the outset. The impact of vertical infrastructure growth on slum deprivation was not analyzed in detail. The morphological patterns of some slums (especially in Eastern Dhaka and Southern Dhaka near the river) underwent transformation from an originally quite homogeneous state to a more complex state. For example, a substantial proportion of mostly multi-story high-rise buildings was erected within the original slum neighborhoods, mostly in a scattered, spatially inconsistent, and organic manner. Rather than reflecting a change in soil sealing (imperviousness) or structural patterns (which could be reflected by standard variables derived from optical EO data), this process is attributed to a change in 3D zoning–the average height of building blocks increased over time. Still, the prevailing major proportion of built-up retains its slum character. For the time being, the image analysis used for this report does not fully reflect this internal process in slum areas. To propose appropriate indicators and monitoring measures this will be investigated further in the second phase, from both a socio-economic and remote-sensing point of view in order.
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10. Conclusion Kuffer et al. (2016) conducted a review of 15 years (2000-2015) of slum mapping using remote sensing information. One of the main contributing aspects highlighted from this review is the need to identify the “contextual slum typologies as an important research direction” in addition to slum identification. They argued, combining “image-based information with socio economic characteristics may ultimately lead to a better targeting of pro-poor policy interventions” (p. 474). Policy makers and city planners need the appropriate tools and evidence to provide full standards of basic services in changing environments of urban settings. The success of upgrading services in slums is directly linked to how well the project is planned and executed and how well the interventions are operated and maintained. The process of implementing a slum upgrading project or expansion of basic services, like water supply and sanitation, depends on the quality of information supporting the activities, the analysis of factors that impede households to access services, and the analysis of all possible options of the intervention, prioritization, and sequencing. All of these aspects have been addressed in this report for the case of Dhaka, Bangladesh. Still, there are more possibilities of exploiting the model to identify characteristics that contribute to determine specific parameters for planning and rolling out interventions in an effective way. The results from these models also have the potential to anticipate locations and scale of future change which can help in making more inclusive policy or planning interventions targeted to the marginalized poor, often the majority of the city. This tool may also provide useful data to the Dhaka City Government, specifically the use of improved slum analysis techniques to generate different scenarios and improve coordination between multiple sectors to increase the coverage of basic services. The outputs of the analysis can be spatially assessed to manifest different stakeholders’ interest for collaborative planning and management of the wider Dhaka metropolitan area. Finally, EO data offer a distinctive source of commensurable information that can be further combined with administrative, social and economic information at multiple scales for in-depth policy analysis.
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Additional Graphs Slum-like areas in the metropolitan area of Dhaka – Dhaka Citi Corporation –captured an identified via VHR satellite imagery over various years.
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Recent regional trends in urban poverty rates (a) and share of urban population living in slums (b)
Livability index in comparator cities with Dhaka, 2016
Source: Leveraging Urbanization in South Asia: Managing spatial transformations. World Bank, 2016.
Annexes Annex A: Housing Deprivation Measurements 60 | P a g e
A1. Six Dependent Variables We used six variables of housing elements as follows: access to drinking water, access to sanitation, adequate living space, permanent structure, security of tenure, and access to electricity. All the outcome variables are negatively coded and dichotomized one for lack of access to resources, as zero otherwise. 1. Lack of Access to Drinking Water: “What is the main source of drinking water?” Tube well and tap are coded as zero, while other is coded as one. 2. Lack of Access to Sanitation: “What kind of toilet facility do members of your household usually use?” Flush to piped sewer system, flush to septic tank, flush to somewhere else, pit latrine ventilated improved pit latrine, and pit latrine with slab are coded as zero. Pit latrine without slab, open pit, hanging toilet, and hanging latrine are coded as one. 3. Lack of Adequate Living Space: “How many rooms does your household occupy? (excluding rooms for business)” For this indicator, household size is divided by the number of rooms. Less than three persons per room is coded as zero. Greater than or equal to three persons per room is coded as one. 4. Lack of Permanent Structure: “Type of structure” Semi-pucca and Pucca are coded as zero. Jhupri, Kacha, and tin shed are coded as one. 5. Lack of Security of Tenure: “Does the household fear any risk of eviction from this slum dwelling?” No is coded as zero, and Yes is coded as one. 6. Lack of Access to Electricity: “What is the main source of light?” Electricity is coded as zero and Kerosene, solar electricity, and others are coded as one. A2. Water and Sanitation Tier Framework utilized in the Bangladesh WASH Poverty Diagnostic Quality and Access to Drinking-water 1. 2. 3. 4. 5.
Tier 0: No Service and unimproved tiers Tier 1: Improved Tier 2: Improved taking into account access to improved water sources within 30 minutes or more, round trip Tier 3: On-premise improved, taking into account access to improved water sources on premise Tier 4: On-premise piped water
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Variable name
Description
survey item number
water_t0
• Other source (neither tube well nor tap)
3_33A
water_t1
• Piped water, public tap, standpipe, tubewell, borehole
3_33A
water_t2
• Piped water, public tap, standpipe, tubewell, borehole • Less than 30min to water
3_33A, 3_36A
water_t3
• Piped water, public tap, standpipe, tubewell, borehole • Less than 30min to water • In own dwelling/yard/plot
3_33A, 3_36A, 3_33B
water_t4
• Piped water • Less than 30min to water • In own dwelling/yard/plot
3_33A, 3_36A, 3_33B
Table 9. Mapping of Survey Instrument Questions with Definitions of Tiers in Levels of Water Service
Questions in Household Survey Data are as follows: 2_20: “What is the main source of drinking water?” • Tube well • Tap • Other (specify) 3_36A. How long does it take to go to your main drinking water source, get water, and come back? Minutes 3_33B. Where is that water source located? • In own dwelling..............................................1 • In own yard/plot ........................................... 2 • Elsewhere – outside slum............................. 3 • Elsewhere – somewhere inside slum............ 4 • Elsewhere – other.......................................... 5
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Quality and Access to Sanitation 1. 2. 3. 4.
Tier 0: (Unimproved Sanitation): Open Defecation and Unimproved Tier 1: Improved Sanitation (including shared) Tier 2: Improved Sanitation (excluding shared) Tier 3: (Private Sewage Connection)
Variable name
Description
Household survey number
sanit_t0
• Pit latrine without slab/open pit/hanging toilet/hanging latrine
3_02
sanit _t1
• Flush to piped sewer system/flush to septic tank/flush to somewhere else/pit latrine ventilated improved pit latrine/pit latrine with slab • Sharing
3_02, 3_03A
sanit _t2
• Flush to piped sewer system/flush to septic tank/flush to somewhere else/pit latrine ventilated improved pit latrine/pit latrine with slab • No sharing
3_02, 3_03A
sanit _t3
• Flush to piped sewer system/flush to septic tank/flush to somewhere else/pit latrine ventilated improved pit latrine/pit latrine with slab • No sharing • Directly to piped sewer system/septic tank connected to drain/ Septic tank connected to open ground/water body/septic tank connected to piped sewer system
3_02, 3_03A, 3_06
Table 10. Mapping of Survey Instrument Questions with Definitions of Tiers in Levels of Sanitation Service
Questionnaire in Household Survey Data are as follows: 3_02. What kind of toilet facility do members of your household usually use? • Flush to piped sewer system • Flush to septic tank 63 | P a g e
• Flush to somewhere else • Pit latrine Ventilated improved pit lat • Pit latrine with slab • Pit latrine without slab/open pit • Hanging toilet/hanging latrine 3_03A. Do you share this toilet facility with other households? Yes/no 3_06. Where do contents of this toilet empty to? • Directly to piped sewer system.................................1 • Septic tank connected to drain .................................. 2 • Septic tank connected to open ground/water body...3 • Septic tank connected to piped sewer system ...........4 • Septic tank with no outlet.......................................... 5 • Septic tank to don't know where .............................. 6 • Lined pit with no outlet ............................................ 7 • Lined pit with overflow to drain elsewhere .............. 8 • Unlined pit ................................................................9 • Directly to drain/ditch ............................................ 10 • Directly to lake, pond, river, sea ............................. 11 • Directly to open ground .......................................... 12 • Other ....................................................................... 13 • Don't know.............................................................. 14 A3. Water and Sanitation in the Sustainable Development Goals Report Drinking-water: % population using safely managed water 1. In the first case, households are considered to have access to safely managed drinking water service when they use water from a basic source on premises. 2. A basic drinking water source is a source or delivery point that, by nature of its construction or through active intervention, is protected from outside contamination with fecal matter. 3. Basic drinking water sources can include: piped drinking water supply on premises, public taps/stand posts, tube well/borehole, protected dug well, protected spring, rainwater, and bottled water. 4. Basic drinking water indicator is defined as the percentage of population using a basic source with a total collection time of 30 minutes or less for a round trip including queuing
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Questionnaire in Household Survey Data 3_62 What are top three problems you find in your drinking water service quality? • Select three responses – do not need to rank • Bacterial contamination ........................................... 1 • Arsenic or heavy metal contamination .................... 2 • Salinity contamination ............................................. 3 • Funny smell, taste, or color...................................... 4 • Not available throughout the year ........................... 5 • Not enough water to all the demand........................ 6 • Too expensive ......................................................... 7 • No challenges.......................................................... 8 • Don't know................................................................ 9 • Other (specify) .........................................................10 Sanitation: % population using safely managed sanitation Safely managed sanitation services are those that effectively separate excreta from human contact and ensure that excreta do not re-enter the immediate environment. Household excreta are contained, extracted, and transported to designated disposal or treatment site, or, as locally appropriate, are safely re-used at the household or community level. This indicator also includes provision for hand-washing (with soap) facilities at homes/schools. However, this measure does not fully measure the quality of services, i.e. accessibility, quantity, and affordability, or the issue of facilities for adequate menstrual hygiene management. Questionnaire in Household Survey Data 3_30 OBSERVATION ONLY, Please show me where members of your household most often wash their hands. • Observed ...........................................................1 • Not observed not in dwelling/yard/plot2> >3_33A • Not observed no permission to see........ 3>3_33A • Not observed other reason...................4>3_33A A4. Comparison of BWPD the SDG Definitions of Access to Water and Sanitation Drinking-water: The SDGs definition is similar with the “tier 3” approach of BWPD. Tier 3 of access to water is defined as access to ‘improved water source’ ‘on premise’, ‘within 30 min’,. The SDGs adds one more condition, which is whether water is contaminated with fecal matter. Sanitation: SDGs definition of access to sanitation is similar with the BWPD but SDGs adapt whether household has a hand washing facility. 65 | P a g e
Annex B: Earth Observation Data on Slums B1. Context Remote sensing part of the study has been done in the frame of the activities under the EO4SD-Urban project (2016) initiated by the European Spatial Agency in 2016. Based on the positive experience from the smaller piloting ‘eoworld’ projects (ESA, 2011), ESA has scaled up the support for its programs that aim to mainstream EO data into the work practices of the International Financial Institutions/Multilateral Development Banks (IFIs/MDBs) and developing countries. The main goal of the EO4SD-Urban program is to bring to the forefront the utility of EO-based urban related products and services which can be used for sustainable development activities within the IFIs/MDBs, the governments of the Client States CS as well as other stakeholders in the urban domain. Utilizing the European heritage from Copernicus program32, the EO4SD-Urban project has developed number of standard EO based products and services, which are currently actively promoted and integrated into the regular IFIs/MDBs activities and programs. First service EO4SD-Urban implementation in Phase 1 has been showcased in Dhaka Metropolitan Area, Bangladesh. B2. Informal settlements mapping – general approach Extent, location, characteristics, and temporal development of informal settlements represents an important information source for a sustainable urban planning. One of the products in the service portfolio of EO4SD-Urban consortium is the “Extent and Type of Informal Settlements/Slum Areas” derived by analysis of current and historical EO data. Typically, the informal settlements mapping workflow consists of the following components: Processing of optical satellite data – dependent on satellite data product level (geometric, atmospheric and radiometric corrections, and enhancements including color optimization, mosaicking, and tiling) Collection and integration of available ancillary data – geographically specific: different data are available for different cities and different geographic locations - Minimum set comprise from: updated road network from OpenStreetMap or extractable from EO imagery), typology of site-specific urban classes historic or up-to-date city plans, land use plans, and reports Extraction of informal settlements extent (for one or more points in time): - Semi-automatic object-based classification of multispectral imagery utilizing spectral, spatial, and contextual signatures 32Copernicus is a European Union Program aimed at developing European information services based on satellite Earth Observation and in situ (non-space) data. The Program is coordinated and managed by the European Commission. It is implemented in partnership with the Member States, the European Space Agency (ESA), the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), the European Centre for Medium-Range Weather Forecasts (ECMWF), EU Agencies and Mercator Océan. For more information see http://www.copernicus.eu
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- Generalization – application of site-specific rulesets including application of minimum mapping unit (MMU) shape and artefacts cleaning - Manual corrections and refinements of output layers by visual photo-interpretation
Figure 9. Example of slum areas identification (Surabaya, Indonesia). Source: GISAT, 2015
Calculation of typological attributes derivable from EO imagery including shape, size, density, compactness, etc. Analysis of ancillary data sources to derive additional attributes based on spatial context, such as road networks, infrastructure, digital surface model (DSM), hazard maps, etc.
Figure 10. Example of slum areas characterization (Surabaya, Indonesia). Source: GISAT, 2015
Calculation of change metrics in case of assessment of settlement evolution (optional). Following principles of land accounting assessment, similar to LULC products
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Figure 11. Example of slum areas changes monitoring (Surabaya, Indonesia). Source: GISAT, 2015
1.
Quality control and accuracy assessment - Based on common principles and methodology dependent on availability of ground-truth or pseudo-ground-truth reference data - Optional stratification based on attributes compatible with reference data or derivable by re-interpretation - Proportional random selection of polygons to be checked - Production of error matrix and accuracy measures Available local datasets, local imagery (e.g. StreetView) or crowd-source information (Open Mapping initiatives or mobile application) can be used for QC/QA purposes
Figure 12. Example of potential slum areas verification (Surabaya, Indonesia). Source: GISAT, 2015
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B3. Informal settlements mapping in Dhaka This product has been delivered for purpose of this study in Dhaka, where it targeted spatial location and extent of building agglomerations classified as informal settlement/slum areas. Furthermore, the product emphasizes physical patterns and key characteristics of informal settlements such as growth (in terms of location and area), and type (phase of development of settlements such as established/permanent or new developments). All variables have been derived from EO or open source data (OpenStreetMap) on semi-automated basis. Spatial metrics and indicators such as compactness, patch size and density of buildings, spatial patterns such as built-up density and proximity to infrastructure assisted in definition and delineation of these settlements. The spatial context of the settlement with respect to hazard and risks (such as flood risk) derived EO imagery or spatial analysis of additional geospatial products, support planning appropriate risk reduction and mitigation measures. Metrics and typologies attributing detected slum patches provide means to define custom thematic classes either from individual variable or by combination of multiple variables. As these informal settlements are dynamic environment with frequent population fluctuations, temporally and spatially, VHR EO data were used to map evolution of changing locations and attributes of informal settlements. Products provided for Dhaka represent an information source, which can be integrated into urban planning processes by city planners. Generally, satellite data enable consistent and recurrent mapping of large areas (at city or regional level) and therefore represent source of information, which would be difficult to obtain by other means. Especially in developing countries with complicated accessibility of informal settlements areas, traditional large scale field mapping is difficult to implement and EO-based mapping represent considerable alternative with potential cost and time savings. Informal settlements (candidate features) in Dhaka were obtained by expert analysis of very high-resolution multispectral satellite images acquired for two points in time (2006 and 2017). Analysis utilized knowledge-based labelling of classes by classification and interpretation similar to procedures utilized for Land Use / Land Cover (LULC) urban mapping production workflow. and the product is attributed to subset of LULC nomenclature. B4. EO Data Used in Dhaka study The study area corresponds to the administrative boundary of the Dhaka Metropolitan Area, which is a police jurisdiction area that comprises of forty one “Thanas” (the lowest administrative unit among four administrative tiers of Bangladesh) with an area of about 300 km2 and according to BBS Census from 2011 population of 8,906,039. EO satellite imagery acquired for two reference years (2006 & 2017) represented the key input datasets for the study. Very high-resolution satellite imagery (with pixel spacing 69 | P a g e
ranging from 0.5 – 0.6 m) provided sufficient resolution to identify various features and characteristics relevant to informal settlements/slum areas detection, characterization, and monitoring at appropriate level of detail and accuracy. Thanks to archives of commercial VHR data, which store images dating back up to 1999, analysis of historic changes from the last decades could be implemented, too. Beside EO data, some open data datasets e.g. OpenStreetMap are also used. Table 7 below provides overview of main input datasets utilized in the study.
Figure 13. Images from QuickBird (DigitalGlobe) left and Pleiades (AIRBUS) right.
Acquisition date
Satellite
Resolution
Satellite Operator
Procurement mode
Pléiades 1B
0.5m (pan-sharpened)
AIRBUS
Commercial purchase
QuickBird
0.6m (pan-sharpened)
DigitalGlobe
Commercial purchase
T2: 2017 12/01/2017 T1: 2006
20/12/2006
Table 7. Baseline VHR EO coverage – 2006 and 2017.
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Data
Description
Source
Date of download
Points of interest
POI GIS data extracted from OpenStreetMap
OpenStreetMap
27.2.2017
Transport network
Transport network GIS data extracted from OpenStreetMap: - Manually corrected and updated - Classes: Motorway, Trunk, Primary, Secondary, Tertiary, Residential, Living street and Railway
OpenStreetMap
27.2.2017
Table 11. Additional data used to support thematic features extraction.
B5. Source of ancillary data supporting slum delineation in Dhaka In the context of this study, slum identification and delineation were facilitated by work accomplished by Gruebner et al. (2014): Mapping of urban slums in Dhaka using very high-resolution optical satellite images. Outputs of their research consist of two slum status products, which are freely accessible under CC license. Each result consists of polygons representing slum neighborhoods and patches detected from EO imagery as of 2006 or 2010 respectively. Details of methodology used to derive slum areas are described in Gruebner et al. (2014). In brief, steps conducted by their team included: Visual interpretation of QuickBird imagery in 2006 and 2010 Assessment of 2005 census and mapping of slums Incorporation of sample data from slums visited in 2007, 2008, and 2009, Interpretation of Google Earth timescale images Geolocated amateur photographs from “Panoramio” linked to Google Earth B6. Methodology applied for slum delineation in Dhaka The Informal Settlement/Slum Areas products were obtained via semi-automatic objectoriented analysis (OBIA) of very high resolution (with pixel spacing in range 0.5 – 0.6 m), multispectral satellite images acquired for two reference years (2006 and 2017). Beside EO data, some open data spatial datasets, such as OpenStreetMap, were also used. The initial slum identification and delineation step was supported by a prior dataset obtained and shared by a research consortium led by Columbia University (data obtained and methodology are described in Gruebner et al. (2014). As a first step, GISAT experts visually inspected the polygonal results of the Gruebner’s study using QuickBird (2006) imagery and Google Earth timescale. Products cover core city and most of sub-urban fringe areas in good level of detail and spatial consistency. Polygons were therefore used as samples to train semi-automatic object-based classification of multispectral imagery to follow similar approach and delineate built-up areas of the same characteristics (in terms of morphology and structure) in the 2017 slum status product. 71 | P a g e
After validating the 2006 GIS output’s detail and spatial consistency, GISAT team used the identified polygons as samples to train semi-automatic object-based (OBIA) classification of multispectral Pleiades imagery (2017) to follow similar approach to identify and delineate built-up areas of the same characteristics (in terms of morphology and structure) in the 2017 slum status product. The Gruebner's GIS output from 2010 has been then used as baseline to check and enhance 2017 slum polygons delineation by means of computer-assisted visual inspection and interpretation using Pleiades imagery (2017). Polygons were updated assuming presence of slum areas as visible in 2017 image: removed slums were subtracted and newly emerged slums added to the original polygons. MMU was not strictly applied, however slums smaller than 0.25 ha were typically not delineated. Single formal residential buildings were not subtracted from the resulting polygons if they had been erected (between 2010 and 2017) in scattered and non-contiguous manner in the original slum areas. If slum was replaced by a contiguous area (residential build-up with more formal patterns, industry, or other type of land use), the area was subtracted from the original polygon. B7. Limitations of approach and the output Product This approach results are influenced by accuracy and reliability of datasets taken as a baseline. However, according to the metadata, Gruebner’s team considered several previous studies (e.g. CMS census) and different approaches. Based on our inspection of their outputs we consider the quality and representability of the dataset as high and sufficient. It was noticed that slums especially in the Eastern Dhaka and Southern Dhaka near the river underwent transformation of their morphological patterns: from originally quite homogeneous into more complex. For example, a substantial proportion of mostly multi-storey high-rise buildings was erected within the original slum neighborhoods, mostly in a scattered, spatially inconsistent, and organic manner. Rather than reflecting a change in soil sealing or structural patterns (which could be reflected by standard variables derived from optical EO data), this process is attributed by change in 3D zonality–the average height of the building blocks increases. Still, the prevailing major proportion of built-up retains its slum character.
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Figure 14. Example of morphological changes within slum areas in Huzurpara, Dhaka.
For the time being, the delivered product does not fully reflect these considerations about slum areas. We recommend that this is further investigated both from a socioeconomic and remote-sensing point of view in order to propose appropriate indicators and monitoring measures. B8. Characterizations of Informal Settlements/Slum Areas Despite of the UN-Habitat definition of informal settlements or slums, there is no standard slum specifications or typology harmonized globally across all cities, ready to be implemented on technical level. Slums differ in their both de facto and de jure characteristics from country to country or even from city to city. Slum areas vary from shanty houses to professionally-built dwellings. Slum definition includes more important aspects then just physical characteristics of slums. Therefore, it should be noted that the assessment of slum areas with remote sensing needs also local expert knowledge engagement and field validation in order to provide high accuracy products.
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Figure 15. Example of different informal settlements/slum areas types in Dhaka.
B9. EO-based characteristic of Informal Settlements/Slum Areas Subsequently, GISAT team derived from imagery number of physical, morphological and contextual characteristics for identified slum areas using automatic object-based (OBIA) techniques, focusing on extended list of measures (see Table 9) for 63 slum communities for which location data was available from BUISBS survey.
Figure 16. Test slum areas locations derived from WASH-POV.
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Table 10. presents list of indicators selected as potential candidates for characterization of informal settlements/slum areas on neighborhood and/or household level. The indicators are based primarily on derivation from VHR imagery, interpreted EO-based products (e.g. EO4SD LULC, EO4SD Flood hazard & risk products) or other open data (e.g. OpenStreetMap), or combination of these datasets. In case of Dhaka mapping, some indicators could not be calculated due to unavailability of respective source reference layers (e.g. missing Digital Surface Model). Indicator Group / Indicator
Indicator type
Input Data
Location type
Qualitative
Imagery, LULC
Distance to paved road
Quantitative
OSM, LULC
Distance to railroad
Quantitative
OSM, LULC
Distance to center/CBD
Quantitative
LULC
Distance to nearest important connectivity node
Quantitative
OSM, LULC
Distance to (heavy) industry
Quantitative
LULC
Distance to shoreline (river, canal, lake or sea)
Quantitative
LULC
Distance to any user provided feature set (water distribution points, sewage, ...)
Quantitative
Field survey / GPS
Distance to arterial (capacity) road
Quantitative
OSM, LULC
Distance to (selected) public services
Quantitative
OSM / GPS
Occurrence of feature within X meters
Qualitative
OSM, LULC
Road network "winding index"
Quantitative
OSM
Density of road network
Quantitative
OSM
Structure of road network typology
Qualitative
OSM
Road connectivity - end point nodes
Quantitative
OSM
Road connectivity - junction point node weights
Quantitative
OSM
Road connectivity - junction point nodes
Quantitative
OSM
Neighborhood locational
Neighborhood accessibility
Neighborhood vulnerability to environmental hazards
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Terrain slope
Quantitative
DEM
Geomorphology of terrain
Qualitative
DEM
Landslide risk
Qualitative
DEM, soils
Flood inundation risk (river, tsunami, storm surge)
Qualitative
EO4SD Flood product
Distance to technological hazards
Quantitative
LULC, GPS
Open sewers
Qualitative
GPS
On dump and hole garbage filling
Qualitative
GPS
Area
Quantitative
GIS
Perimeter
Quantitative
GIS
Shape compactness
Quantitative
GIS
Quantitative
GIS
Built-up homogeneity
Quantitative
OBIA
Built-up density
Quantitative
OBIA
Open spaces density
Quantitative
OBIA
Greenness density
Quantitative
OBIA
Mean dwelling size
Quantitative
Imagery
Main direction
Quantitative
Imagery
Compactness
Qualitative
Imagery
Mean dwelling separation
Quantitative
Imagery
Mean estimated dwelling height
Quantitative
Imagery
Roof heterogeneity
Quantitative
Imagery
Roof & masonry material
Qualitative
Imagery
Estimated dwelling age
Qualitative
LULC
Neighbourhood shape morphological characteristics
Neighborhood LULC proportional characteristics LULC structure in surroundings Neighborhood internal structure
Neighborhood dwelling characteristics
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Lacunarity of housing structures
Quantitative
Imagery
Table 12. List of characteristics (indicators) derived for informal settlements/slum areas.
Provided EO-based and GIS characteristics (both on neighborhood and household level) represent crucial inputs into subsequent modelling step, but allows also creation of different user-defined typologies of informal settlements/slum areas (see figure 17). Finally, it helps in estimation of the vulnerability to natural hazards and assessment of related risks to the affected population. Based on the proposed product, a representative stratified sample of informal settlements can be also selected to conduct (or integrate existing) in-depth surveys capturing the existing more detailed characteristics for each informal settlement type identified.
Figure 17. Example of user-defined slum typology potential of slum characteristics database;“Slums Analysis in Metro Manila”- like typology applied for Dhaka.
B10. GISAT Service Outlook The workflow components are automated to maximum possible level. Information optionally derived by analysis of supplementary ancillary data (like height, statistics, risks etc.) could be incorporated into the final product. For informal settlements polygons structural / development indicators are derived up to building block level. Mapping outputs represent proxy to vulnerability and input to risk estimation mapping. Manual inputs (photo-interpretation, correction) depending on level of class complexity, 77 | P a g e
reliability, and accuracy requirements are still necessary. Main cost drivers are data acquisition costs of input HR/VHR images and efforts related to computer assisted photo-interpretation. The products are delivered in GIS-ready vector and raster data format, as digital or printable maps and online services. Harmonization spatial and thematic characteristics can be combined with customizable user-driven and tailored ones as required by specific city or activity needs. Workflows and their automation are continuously improved towards more and more automatic building extraction routines in complex slum environment (building compounds) in different geographic contexts. Improved availability of 3D information together with wealth of additional open data will further streamline the service execution and enhance capacity to provide regular monitoring of larger areas. Regular support with standard EO based products feeding from the Bank’s internal information management system delivered via online platform and integrated with other relevant datasets is ultimate goal of the EO4SD activity.
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Annex C: Regression Modeling Results C1. Prediction Model for Slum Severity Index Earth Observation Variables
Incidence Rate Ratios
Locational Characteristics Distance to Central Business District (in km)
1.072312**
Distance to Arterial Capacity Road (in km)
19.56075***
Distance to Railroad (in km)
1.156386*
Distance to Heavy Industry (in km)
0.0620684***
Distance to Shore Line (in km)
0.6369359***
Distance to Major Road Junction (in km)
3.179094***
Neighborhood Characteristics within Slums Percentage Built-up Area
0.2526052***
Mean Dwelling Size (in sq m)
1.004232***
Accessibility and Connectivity Characteristics Distance to the Nearest Hospital
1.000645***
Distance to the Nearest Place of Worship
0.9990021***
Distance to the Nearest School
0.9988284***
Distance to the Nearest University
0.9997247***
Land Use Land Cover Characteristics within Slums Percentage High Density Continuous Residential Urban Fabric
0.9787916***
Percentage Commercial and Industrial Non-residential Urban Fabric
0.9695883***
Percentage Water Bodies
0.7920074***
Street Pattern Characteristics within Slums Low-level Connectivity Node Ratio
0.3417476***
Percentage Road Typology – Informal Roads
1.075212***
Percentage Road Typology – Local Roads
1.083477***
Percentage Road Typology – Primary Roads
1.068668***
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Percentage Road Typology – Secondary Roads
1.099809***
Percentage Road Typology – Tertiary Roads
1.095451***
Road Winding Index - Informal Roads
0.3942884**
Unit of Analysis: Slum Communities N = 43 Pseudo R2: 0.4705 Akaike Information Criterion: 282.9432 Note: ***, **, and * indicates statistical significance at the 1, 5, and 10% level respectively. Table 13. Multivariate Poisson Regression model of Slum Severity Index as a function of EO data.
C2. Prediction Model for Lack of Access to Water Earth Observation Variables
Incidence Rate Ratios
Locational Characteristics Distance to Central Business District (in km) Distance to Paved Road (in km)
1.652232* 6.57E-66*
Distance to Arterial Capacity Road (in km)
2589329***
Distance to Heavy Industry (in km)
4.93E-07***
Neighborhood Characteristics within Slums Percentage Built-up Area
0.0000347***
Unit of Analysis: Slum Communities N = 52 Pseudo R2: 0.7540 Akaike Information Criterion: 41.59137 Note: ***, **, and * indicates statistical significance at the 1, 5, and 10% level respectively. Table 14. Multivariate Poisson Regression model of Water Deprivation as a function of EO data.
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C3. Prediction Model for Lack of Access to Sanitation Earth Observation Variables
Incidence Rate Ratios
Locational Characteristics Distance to Paved Road (in km)
4.96E-12**
Distance to Railroad (in km)
7.334596***
Distance to Shore Line (in km)
0.1141298**
Distance to Major Road Junction (in km)
10108.73**
Neighborhood Characteristics within Slums Built-up Homogeneity Index
1.02791***
Accessibility and Connectivity Characteristics Distance to the Nearest Hospital
0.9935028**
Distance to the Nearest Place of Worship
1.004079*
Land Use Land Cover Characteristics within Slums Percentage Commercial and Industrial Non-residential Urban Fabric Percentage Urban Green Space and Sports Facilities Percentage Water Bodies
0.4491051** 1.226204** 0.3272415***
Street Pattern Characteristics within Slums Low-level Connectivity Node Ratio 100 m Buffer
82.54076**
Total Number of End Point Nodes
1.822696***
Unit of Analysis: Slum Communities N = 51 Pseudo R2: 0.6882 Akaike Information Criterion: 101.7715 Note: ***, **, and * indicates statistical significance at the 1, 5, and 10% level respectively. Table 15. Multivariate Poisson Regression model of Sanitation Deprivation as a function of EO data.
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C4. Prediction Model for Lack of Access to Adequate Living Space Earth Observation Variables
Coefficients
Locational Characteristics Distance to Paved Road (in km)
255.4983**
Distance to Arterial Capacity Road (in km)
140.9615***
Distance to Railroad (in km)
7.326405***
Distance to Heavy Industry (in km)
-169.7748***
Distance to Shore Line (in km)
-50.88895***
Distance to Major Road Junction (in km)
50.76917***
Neighborhood Characteristics within Slums Percentage Built-up Area Built-up Homogeneity Mean Dwelling Size (in sq m)
-165.2276*** -0.4881278** 1.310244***
Accessibility and Connectivity Characteristics Distance to the Nearest Hospital
0.0602806***
Distance to the Nearest Place of Worship
-0.084013***
Distance to the Nearest School
-0.0732997***
Distance to the Nearest University
-0.0213383***
Land Use Land Cover Characteristics within Slums Percentage Commercial and Industrial Non-residential Urban Fabric
-0.8659405***
Percentage Urban Green Space and Sports Facilities
2.032982***
Percentage Water Bodies
-10.25311***
Street Pattern Characteristics within Slums Low Connectivity Node Ratio 100 m buffer Total Number of Junction Nodes Total Number of End Point Nodes
-56.65923** 5.303454*** -9.522083***
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Percentage Road Typology – Informal Streets
6.331574***
Percentage Road Typology – Local Roads
5.853884***
Percentage Road Typology – Primary Roads
8.302425***
Percentage Road Typology – Secondary Roads
7.700105***
Percentage Road Typology – Tertiary Roads
8.300965***
Road Winding Index - Secondary Roads
-181.3537***
Road Winding Index - Tertiary Roads
-329.5207***
Road Network Density - Informal Roads
-313.6983**
Road Network Density - Local Roads
-68.81282**
Road Network Density - Primary Roads
-2150.982**
Road Network Density - Tertiary Roads
-551.2211***
Unit of Analysis: Slum Communities N = 43 Adjusted R2: 0.7456 Akaike Information Criterion: 282.9432 Note: ***, **, and * indicates statistical significance at the 1, 5, and 10% level respectively. Table 16. Multivariate Ordinary Least Square Regression model of Overcrowding as a function of EO data.
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C5. Prediction Model for Lack of Permanent Structure Earth Observation Variables
Incidence Rate Ratios
Locational Characteristics Distance to Paved Road (in km)
2.83E-11***
Distance to Railroad (in km)
1.142857*
Distance to Heavy Industry (in km)
85.86319***
Distance to Shore Line (in km)
0.0491247***
Neighborhood Characteristics within Slums Built-up Homogeneity
1.037804***
Percent Built-up Area
1883.492***
Mean Dwelling Size (in sq m) Mean Dwelling Separation
0.9423839** 1.174473**
Accessibility and Connectivity Characteristics Distance to the Nearest Hospital
0.998256**
Distance to the Nearest School
0.9984567**
Distance to the Nearest University
1.001015***
Land Use Land Cover Characteristics within Slums Percentage High Density Continuous Residential Urban Fabric
0.9189564***
Percentage Commercial and Industrial Non-residential Urban Fabric
0.9434174***
Percentage Water Bodies
0.7138399***
Street Pattern Characteristics within Slums Road connectivity - junction point node weights Total Number of Junction Nodes Total Number of End Point Nodes
1.713155** 0.1558463*** 3.493766***
Percentage Road Typology – Informal Roads
0.8907283**
Percentage Road Typology – Primary Roads
46.34079*
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Percentage Road Typology – Secondary Roads
0.973891**
Road Winding Index - Informal Roads
0.0216707*
Road Winding Index - Primary Roads
1.28E-76**
Road Network Density - Informal Roads
4.90E+19***
Road Network Density - Local Roads
0.0021744***
Road Network Density - Primary Roads
0*
Unit of Analysis: Slum Communities N = 43 Pseudo R2: 0.4560 Akaike Information Criterion: 206.1409 Note: ***, **, and * indicates statistical significance at the 1, 5, and 10% level respectively. Table 17. Multivariate Poisson Regression model of Permanent Structure Deprivation as a function of EO data.
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C6. Prediction Model for Lack of Secure Tenure Earth Observation Variables
Incidence Rate Ratios
Locational Characteristics Distance to Arterial Capacity Road (in km)
0.0170969**
Distance to Railroad (in km)
0.6431594***
Distance to Major Road Junction (in km)
691.7015***
Neighborhood Characteristics within Slums Built-up Homogeneity
1.128152***
Percent Built-up Area
174.8398**
Mean Dwelling Size (in sq m) Mean Dwelling Separation
0.7955058*** 0.5261229***
Accessibility and Connectivity Characteristics Distance to the Nearest Hospital
1.004782***
Distance to the Nearest Place of Worship
0.9984303**
Distance to the Nearest School
0.9950998***
Land Use Land Cover Characteristics within Slums Percentage High Density Continuous Residential Urban Fabric
0.955032**
Percentage Commercial and Industrial Non-residential Urban Fabric
0.9594257***
Street Pattern Characteristics within Slums Low-level Connectivity Node Ratio 100m Buffer
2.33E-06***
Low-level Connectivity Node Ratio in Slum Polygon
334.4009***
Road connectivity - junction point node weights
1.211127***
Total Number of End Point Nodes
1.782158***
Percentage Road Typology – Informal Roads
0.5582851***
Percentage Road Typology – Local Roads
0.6950579**
Percentage Road Typology – Primary Roads
0.7073516**
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Percentage Road Typology – Secondary Roads
0.6901308**
Percentage Road Typology – Tertiary Roads
0.7210666**
Road Winding Index - Informal Roads
39377.05***
Road Winding Index - Local Roads
0.2811944*
Unit of Analysis: Slum Communities N = 43 Pseudo R2: 0.5746 Akaike Information Criterion: 173.4312 Note: ***, **, and * indicates statistical significance at the 1, 5, and 10% level respectively. Table 18. Multivariate Poisson Regression model of Secure Tenure Deprivation as a function of EO data
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C7. Prediction Model for Lack of Access to Electricity Earth Observation Variables
Incidence Rate Ratios
Locational Characteristics Distance to Arterial Capacity Road (in km)
391.8047**
Distance to Central Business District (in km)
1.453505***
Accessibility and Connectivity Characteristics Distance to the Nearest Place of Worship
0.9967407**
Street Pattern Characteristics within Slums Low-level Connectivity Node Ratio 100 m buffer
0.0013948**
Unit of Analysis: Slum Communities N = 53 Pseudo R2: 0.5411 Akaike Information Criterion: 84.63165 Note: ***, **, and * indicates statistical significance at the 1, 5, and 10% level respectively. Table 19. Multivariate Poisson Regression model of Electricity Deprivation as a function of EO data.
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Annex D: World Bank Projects using EO Data Analysis 1. One of the WB studies that inspired our research design was part of the project ‘Sustainable Housing Solutions for ISFs’ (P149905). Within the scope of the project, a very interesting study (Tigny, 2016) “Slums Analysis in Metro Manila” (SLAMM) was conducted with Earth Observation by the Belgian company GIM (also under the WB-ESA partnership). VHR satellite imagery extracted in 4 points in time revealed some trends and patterns in slums formation, enabling a highly accurate mapping of the 2500 informal settlements in Manila. The most notable contribution of this work was to show how the Object-Based Image Analysis applied on VHR imagery allowed the computation of a set of relevant housing structure attributes (e.g. amount of vegetation, proportion of large buildings, density of dwellings, etc.). In turn, the analysis informed the identification of various types of informal settlements that showed similarities in terms of their formation and could be stratified across various spatially-defined attributes into eight consistent typologies. The findings from the EO data analysis was later validated with an in-depth household survey (that used the slum classification for the sampling strategy)33. The results continue to be used to inform decision-making related to the design and implementation of important plans on housing improvement and flood management in Manila. 2. There is an ongoing Bank-supported activity ‘City Planning Labs and Spatial Planning’ project (P158752), which consists of an analysis of the development pattern of slums in Denpasar, Indonesia, to inform slum upgrading initiatives in the city. The challenge was developing a community level map of infrastructure accessibility in the slums. Using urban extent data from satellite imagery and slum locations, the City Planning Labs34 developed a new slum-level mapping and surveying procedure to map the Jematang slum. This project corroborates the importance of appreciating the uneven and sometimes unpredictable dynamics of slum formation for better urban planning. The outputs of the study, including a slum information database produced with community maps, have become a key building block in the decisions on investments in basic infrastructure and services. The results help devise appropriate interventions and target them to areas of greatest need. In addition, the study offers a tool for developing strategies to prevent the formation of new slums in areas of high risk or those reserved for public use. 3. While accurate spatial data are a crucial ingredient for smart land use and city planning, historically, shantytowns, and unplanned areas have often been omitted from city planning maps, making it difficult to address the improvements these neighborhoods need. A study linked to project ‘The Spatial Development of African Cities’ project (P148736) (Antos et al., 2016), produced and analyzed land cover maps 33The report (Singh, 2017 (forthcoming)) has been authored by Gayatri Singh (Urban Development Specialist, GPSURR) under the AAA on Making Cities Inclusive (TTL Judy Baker, TF017581). 34 See more information at the project link: http://cityform.mit.edu/projects/denpasar-cpl
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derived from high-resolution satellite imagery for five primarily African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar).35 While satellite imagery had been used to measure the footprints of cities, distinguishing urban vs non-urban areas, the high-resolution satellite imagery dug deeper to measure the various land cover types within the city, including the ‘irregular’ residential land cover type that serves as a proxy for slum areas. The methodology obtains information from the image’s texture (orientation of lines, density of rectangles, angles of road intersections) and produces an output layer giving a neighborhood-based classification. 4. A byproduct of the above effort was the study ‘Measuring Living Standards in Cities (MLSC)’ (World Bank, 2016). It geo-referenced household surveys built on an analysis conducted on satellite data to collect key information about living standards in cities. The use of satellite imageries for sampling design and field work increased representativeness within cities, allowing the research team to appreciate differences between regular and ‘irregular’ settlements (often excluded or poorly represented). 5. A similar project ‘6C-Central America Urbanization Review’ (P152713), was conducted by the same research group and captured by the report ‘Mapping land cover in Central America’s Secondary Cities’ in Central America (Augustin et al., 2017). This is one of the fastest urbanizing regions in the world, yet its secondary cities lack sufficient (up to date and spatially disaggregated) data. The work was conducted on nine cities and also allowed for benchmarking infrastructure (roads) across them. Again, high-resolution imagery was used to create detailed land cover maps and road networks. Those layers were then validated in the field and analyzed to determine emerging patterns such as mono-centricity and connectivity. The resulting layers and their analysis can help decision makers to prioritize urban investment and compare spatial trends in Central America’s secondary cities with other cities worldwide. In both regions (Sub-Saharan Africa and Central America), the EO-derived classification has improved sampling for surveys where no information was available on the location of slums. In fact, the imagery and the classification offered an alternative way to identify enumeration areas that were mostly irregular (shanty) areas and add a “shanty areas” stratum in the survey design. 6. The Global Facility for Disaster Reduction and Recovery (GFDRR) stands out for its innovative application of geospatial analytics to monitor and reduce vulnerability to natural hazards and climate change. The GFDRR team is especially committed to promoting open data literacy and usage to enable community involvement, which is a
35 This work (P148736, Urban GP) initially focused on ten cities which have been identified fin close collaboration with
staff in the World Bank’s AFRICA region (Nairobi, Dakar, Addis, Kigali, Lagos, Maputo, Accra, Kinshasa, Dar es Salam, and Durban (TBC)).
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fundamental step to ensure that geospatial data can ultimately be accessed and used by local decision makers. Examples of several initiatives and geodata portals are: o The ‘Open Data for Resilience Initiative’ (OpenDRI) to promote open mapping and data sharing applied to disaster risk management and emergency response. Several projects in developing countries can be found here https://opendri.org/project/. o Another initiative developed with several other partners is the ‘Open Cities Project’, which aims to catalyze the creation, management, and use of open data to produce innovative solutions for urban planning and resilience challenges across South Asia – including in Dhaka (www.opencitiesproject.org). o The “Dar Ramani Huria” (which is Swahili for “Dar Open Map”) project is a community mapping effort for urban flood risk in Dar Es Salaam, Tanzania (http://ramanihuria.org/about/). This successful project, brought about through the collaboration of international agencies, local university students, and community members with the aim of putting the city’s most flood-prone areas “on the map” for the first time, is an endeavor made possible by the coordinated adoption of a host of geospatial and open data tools. As outlined in an interview with one of the WB staff36 working on the project, there are ripple effects of such open data initiative for resilience. In fact, the wards covered by the project have been reported to benefit from digital maps in unexpected ways, such as promoting adhesion to the national addressing system, planning for better provision of services like health centers and schools, or informing the work of local NGOs. https://www.youtube.com/watch?v=WuEiZ_Mqi78. 7. Related to the power of maps to generate policy dialogue, the ‘Platform for Urban Management and Analysis’ (PUMA, http://puma.worldbank.org/), built by GISAT for the East Asia Pacific (EAP) Urban unit of GSURR, is a remarkable geospatial tool that adopts open-source software to allow users with no prior GIS experience to access, analyze, and share urban spatial data in an interactive and customizable way. PUMA exemplifies how GIS can become instrumental for dealing with complex issues by bypassing the technical skills barrier of handling spatial data. It is a user-friendly interface that has been used by practitioners in client countries and by World Bank staff for project analysis in the study cities of Chittagong, Colombo, Jalalabad, and Karachi. Incidentally, the EAP Urban team has extensively used satellite imagery to understand cities’ patterns of growth and spatial expansion in a consistent way. Satellite data (MODIS) were used with change-detection methods to create maps of built-up urban extents throughout East Asia between 2000-2010, creating a consistent methodology for measuring urban areas, population growth, and spatial expansion. The analysis has resulted in a comprehensive database of all cities with over 100,000 inhabitants in East 36 Mr. Edward Anderson, Senior Disaster Risk Management Specialist at the World Bank.
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Asia showing patterns of population growth, urban expansion, and administrative structures. 8. Another effort in the EAP region can be mentioned for its involvement of the community in dealing with disaster management and preparation. Two projects37 supported Community-based DRM Community Mapping with OpenStreetMap (OSM) in Indonesia and the Philippines, helping communities to map themselves for disaster preparedness and response planning, taking advantage of the OSM to integrate community mapped data on hazards and exposure into disaster preparedness planning. In the Philippines, maps were used to facilitate the response to Typhoon Glenda. The related training materials and tools have been scaled up through national programs. In Indonesia, a study of about 250,000 OSM mapped assets showed a 90% spatial accuracy at a ratio of 1:5,000 scale, which is a finer resolution that the currently available official maps. 9. A particularly innovative ongoing study ‘The Sensors are Here! A High-Resolution Application on Understanding Individual Travel Patterns in African Cities’ (P153698) lead by Talip Kilic, tackled the challenge of dealing with unequal transport policies and investments exploiting the potential of big data38. The initiative, which was a World Bank Innovation Challenge Winner in 2015, brought together a multidisciplinary team of economists, transportation specialists and computer scientists from the World Bank, the Massachusetts Institute of Technology, and other agencies. Its primary goal was to assess the feasibility of collecting objective big data through tools embedded in multitopic household survey data production so that the augmented, “smarter” dataset would increase the relevance and value of the multi-topic household survey data. While dealing with a different sector, this project illustrates the power of integrating data from different sources to enhance traditional socio-economic survey. 10. Lastly, moving to the Middle East and North Africa region, an effort on obtaining ‘Household Sampling without Census Data’ dealt with the problem of creating a sampling frame for a large household survey without access to reliable census data. A remote-sensing approach was applied to create a random sample of households within built-up areas, facilitating a household survey with 12,000 interviews covering every community in the West Bank and Gaza.
37 The ‘Disaster risk management program, urban drainage and flood’ (P156711) and ‘Mainstreaming disaster risk reduction in indonesia -Phase II’ (P122240). 38 This activity falls under the umbrella of the Big Data Challenge (P152579) and is one of the winning proposals of the Big Data Challenge currently in progress.
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