Preprint Essay Version 2 Preserved in Portico This version is not peer-reviewed

Critical Commentary: Need for an Integrated Deprived Area “Slum” Mapping System (IDeAMapS) in LMICs

Dana Thomson
* ORCID logo ,
Monika Kuffer
ORCID logo ,
Gianluca Boo
,
Beatrice Hati
,
Tais Grippa
,
Helen Elsey
,
Catherine Linard
ORCID logo ,
Ron Mahabir
ORCID logo ,
Catherine Kyobutungi
,
Joshua Maviti
,
Dennis Mwaniki
,
Robert Ndugwa
,
Jack Makau
,
Richard Sliuzas
ORCID logo ,
Salome Cheruiyot
,
Kilion Nyambuga
,
Nicholus Mboga
,
Nicera Wanjiru
,
Joao Porto De Albuquerque
ORCID logo ,
Caroline Kabaria
Version 1 : Received: 18 October 2019 / Approved: 21 October 2019 / Online: 21 October 2019 (06:40:29 CEST)
Version 2 : Received: 22 October 2019 / Approved: 24 October 2019 / Online: 24 October 2019 (10:57:05 CEST)
Version 3 : Received: 14 April 2020 / Approved: 15 April 2020 / Online: 15 April 2020 (10:19:27 CEST)

A peer-reviewed article of this Preprint also exists.

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.; 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. 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.; 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.

Abstract

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 silo-ed, and each fall short of producing accurate, timely, comparable maps that reflect local contexts. The first approach, classifying “slum households” in census and survey data and aggregating to administrative areas, 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 interpretation and machine classification of satellite, aerial, or drone 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 basic public services. The latter, machine classification of imagery, can be automated and extended to incorporate new and multiple sources of data. This diverse collection of authors represent experts from these four approaches to neighborhood deprivation mapping. We summarize common areas of understanding, and present a set of requirements to produce maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making.

Keywords

satellite imagery; social indicator; urban; poverty; SDG

Subject

Social Sciences, Geography, Planning and Development

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Comments (1)

Comment 1
Received: 24 October 2019
Commenter: Dana Thomson
Commenter's Conflict of Interests: Author
Comment: Corrected a co-author's name and email address, and shortened the article to meet a journal's word limit.
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