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

Critical Commentary: Need for an Integrated Deprived Area “Slum” Mapping System (IDeAMapS) in LMICs
Critical Commentary: Need for an Integrated Deprived Area “Slum” Mapping System (IDeAMapS) in LMICs
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.
Thomson, Dana R.
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Kuffer, Monika
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Boo, Gianluca
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Hati, Beatrice
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Grippa, Taïs
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Elsey, Helen
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Linard, Catherine
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Mahabir, Ron
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Catherine, Kyobutungi
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Maviti, Joshua
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Mwaniki, Dennis
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Ndugwa, Robert
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Makau, Jack
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Sliuzas, Rechard
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Cheruiyot, Salome
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Nyambuga, Kilion
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Mboga, Nicholus
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Wanjiru, Nicera
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Porto De Albuquerque, Joao
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Kabaria, Caroline W.
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Thomson, Dana R.
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Kuffer, Monika
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Boo, Gianluca
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Hati, Beatrice
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Grippa, Taïs
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Elsey, Helen
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Linard, Catherine
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Mahabir, Ron
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Catherine, Kyobutungi
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Maviti, Joshua
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Mwaniki, Dennis
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Ndugwa, Robert
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Makau, Jack
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Sliuzas, Rechard
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Cheruiyot, Salome
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Nyambuga, Kilion
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Mboga, Nicholus
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Wanjiru, Nicera
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Porto De Albuquerque, Joao
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Kabaria, Caroline W.
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Thomson, Dana R., Kuffer, Monika, Boo, Gianluca, Hati, Beatrice, Grippa, Taïs, Elsey, Helen, Linard, Catherine, Mahabir, Ron, Catherine, Kyobutungi, Maviti, Joshua, Mwaniki, Dennis, Ndugwa, Robert, Makau, Jack, Sliuzas, Rechard, Cheruiyot, Salome, Nyambuga, Kilion, Mboga, Nicholus, Wanjiru, Nicera, Porto De Albuquerque, Joao and Kabaria, Caroline W. (2019) Critical Commentary: Need for an Integrated Deprived Area “Slum” Mapping System (IDeAMapS) in LMICs. PeerJ PrePrints. (doi:10.20944/preprints201910.0242.v2).

Record type: Article

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.

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More information

Accepted/In Press date: 21 October 2019
Published date: 24 October 2019

Identifiers

Local EPrints ID: 435373
URI: http://eprints.soton.ac.uk/id/eprint/435373
PURE UUID: 91540d21-3030-4c1b-a8e4-901103ceede3
ORCID for Gianluca Boo: ORCID iD orcid.org/0000-0002-4078-8221

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Date deposited: 01 Nov 2019 17:30
Last modified: 16 Mar 2024 04:52

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Contributors

Author: Dana R. Thomson
Author: Monika Kuffer
Author: Gianluca Boo ORCID iD
Author: Beatrice Hati
Author: Taïs Grippa
Author: Helen Elsey
Author: Catherine Linard
Author: Ron Mahabir
Author: Kyobutungi Catherine
Author: Joshua Maviti
Author: Dennis Mwaniki
Author: Robert Ndugwa
Author: Jack Makau
Author: Rechard Sliuzas
Author: Salome Cheruiyot
Author: Kilion Nyambuga
Author: Nicholus Mboga
Author: Nicera Wanjiru
Author: Joao Porto De Albuquerque
Author: Caroline W. Kabaria

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