modelling the wealth index of demographic and health surveys within cities using very high-resolution remotely sensed information

Stefanos Georganos, Assane Niang Gadiaga, Catherine Linard, Tais Grippa, Sabine Vanhuysse, Nicholus Mboga, Eléonore Wolff, Sébastien Dujardin, Moritz Lennert

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Abstract

A systematic and precise understanding of urban socio-economic spatial inequalities in developing regions is needed to address global sustainability goals. At the intra-urban scale, access to detailed databases (i.e., a census) is often a difficult exercise. Geolocated surveys such as the Demographic and Health Surveys (DHS) are a rich alternative source of such information but can be challenging to interpolate at such a fine scale due to their spatial displacement, survey design and the lack of very high-resolution (VHR) predictor variables in these regions. In this paper, we employ satellite-derived VHR land-use/land-cover (LULC) datasets and couple them with the DHSWealth Index (WI), a robust household wealth indicator, in order to provide city-scale wealth maps. We undertake several modelling approaches using a random forest regressor as the underlying algorithm and predict in several geographic administrative scales. We validate against an exhaustive census database available for the city of Dakar, Senegal. Our results show that the WI was modelled to a satisfactory degree when compared against census data even at very fine resolutions. These findings might assist local authorities and stakeholders in rigorous evidence-based decision making and facilitate the allocation of resources towards the most disadvantaged populations. Good practices for further developments are discussed with the aim of upscaling these findings at the global scale.

Original languageEnglish
Article number2543
JournalRemote Sensing
Volume11
Issue number21
DOIs
Publication statusPublished - 1 Nov 2019

Keywords

  • DHS
  • Interpolation
  • Machine learning
  • Poverty
  • Very-high-resolution remote sensing
  • Wealth index

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