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Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs)

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Department of Social Statistics and Demography, University of Southampton, Southampton SO17 1BJ, UK
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Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
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WorldPop Research Group, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
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Institute for Housing and Urban Development Studies, Erasmus University Rotterdam (EUR), 3000 Rotterdam, The Netherlands
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Institute for Environmental Management and Land-Use Planning, Université Libre de Bruxelles, 1050 Bruxelles, Belgium
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Department of Global Health, University of York, Heslington YO10 5DD, UK
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Department of Geography, Université de Namur, 5000 Namur, Belgium
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Department of Computational and Data Sciences, George Mason University, Fairfax, VA 22030, USA
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African Population and Health Research Center, Kitisuru Nairobi, Kenya
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Participatory Slum Upgrading Team, UN-Habitat, Gigiri Nairobi, Kenya
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Global Urban Observatory, UN-Habitat, Gigiri Nairobi, Kenya
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Slum Dwellers International, Kilimani Estate, Nairobi, Kenya
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Institute for Global Sustainable Development, University of Warwick, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
Soc. Sci. 2020, 9(5), 80; https://doi.org/10.3390/socsci9050080
Received: 8 April 2020 / Revised: 4 May 2020 / Accepted: 7 May 2020 / Published: 13 May 2020
Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The first approach, classifying “slum households” in census and survey data, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human (visual) interpretation and machine classification of air or spaceborne imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of public services. We summarize common areas of understanding, and present a set of requirements and a framework to produce routine, accurate maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making across Low- and Middle-Income Countries (LMICs). We suggest that machine learning models be extended to incorporate social area-level covariates and regular contributions of up-to-date and context-relevant field-based classification of deprived urban areas. View Full-Text
Keywords: urban; poverty; SDG; slum; deprivation, spatial model urban; poverty; SDG; slum; deprivation, spatial model
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Thomson, D.R.; Kuffer, M.; Boo, G.; Hati, B.; Grippa, T.; Elsey, H.; Linard, C.; Mahabir, R.; Kyobutungi, C.; Maviti, J.; Mwaniki, D.; Ndugwa, R.; Makau, J.; Sliuzas, R.; Cheruiyot, S.; Nyambuga, K.; Mboga, N.; Kimani, N.W.; de Albuquerque, J.P.; Kabaria, C. Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs). Soc. Sci. 2020, 9, 80.

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