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

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 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.

COVID-19 and Slums: A Pandemic Highlights Gaps in Knowledge About Urban Poverty

“…Analyses of slum populations and their surroundings. Recent publications have repeatedly pointed out that it is necessary to identify and classify both individual households and larger low-resource urban areas using uniform frameworks [ 23 ]. For this purpose, in addition to the large number of studies that have been conducted in different locations, it is necessary to establish common databases to collect information on the population and spatial characteristics of these settlements.…”

Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda

“…Also, as urban settlement classification becomes increasingly possible [ 60 ], survey designers need to understand how within-urban stratification affects the various sample designs used in gridded population, and other, surveys. With no way to stratify urban populations, all surveys are at risk of under-sampling or omitting slums and other vulnerable populations [ 61 , 62 ]. This threat to survey accuracy and social equity will only grow as LMIC urban population continue to expand in the coming decades.…”

The Spatial Dimension of COVID-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil

“…The strengths shown here also answer the questions posed in Section 2.2. In addition, EO-based products could be reasonably aggregated to different levels, for instance, from household level to community level, and thus are also suitable for area-level mapping, and, therefore, are very relevant to support area-based intervention which should address key problems related to health, environmental, and socio-economic conditions [81]. Such area-based mapping is complementing household-level information about deprivation (poverty), e.g., captured by surveys.…”