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Cited by 24Impact of Light on Environment and HealthRemote Sensing and Land UseRemote-Sensing Image Classification

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Modelling the Wealth Index of Demographic and Health Surveys within Cities Using Very High-Resolution Remotely Sensed Information

Open access
Georganos, Stefanos, Gadiaga, Assane Niang, Linard, Catherine, Grippa, Tais, Vanhuysse, Sabine, Mboga, Nicholus, Wolff, Eléonore, Dujardin, Sébastien, Lennert, Moritz·2019·Remote Sensing
DOI 10.3390/rs11212543
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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 DHS Wealth 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.

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  • DHS survey cluster coordinates are randomly displaced by up to 2 km in urban areas (5 km in rural areas) before publication to protect the privacy of survey participants.

    “a well‐known issue with DHS epidemiological data is that survey cluster coordinates are randomly displaced by up to 2 km in urban areas (5 km in rural areas) before publication to protect the privacy of survey participants (Burgert et al., 2013; Corsi et al., 2012; Georganos et al., 2019; Ozodiegwu et al., 2021).”

    From Spatial Optimization Methods for Malaria Risk Mapping in Sub‐Saharan African Cities Using Demographic and Health Surveys (2023) · cites this paper as Georganos et al., 2019

    Section 2.3 (page unavailable) says DHS urban cluster coordinates are randomly displaced by up to 2 km before public release to protect privacy. The cited paper does not state the claimed 5 km limit for rural clusters. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • DHS sometimes have low spatial accuracy, partly due to instrumental biases (sampling errors in GPS receivers), but mainly due to the displacement of survey cluster coordinates by up to 2 km in urban extents for privacy protection.

    “Finally, DHS sometimes have low spatial accuracy, partly due to instrumental biases (sampling errors in GPS receivers), but mainly due to the displacement of survey cluster coordinates by up to 2 km in urban extents for privacy protection (Burgert et al., 2013; Corsi et al., 2012; Georganos et al., 2019; Ozodiegwu et al., 2021).”

    From Spatial Optimization Methods for Malaria Risk Mapping in Sub‐Saharan African Cities Using Demographic and Health Surveys (2023) · cites this paper as Georganos et al., 2019

    Section 2.3 (page unavailable) says urban DHS cluster coordinates are randomly displaced by up to 2 km before public release to protect privacy, and that this can create spatial mismatches. It records that GPS coordinates are collected but does not attribute any loss of accuracy to GPS receiver errors or establish the relative importance of the two causes. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Spatial optimization methods based on duplicating published DHS cluster coordinates in the four cardinal directions have been proposed to adjust for displacement when modeling the DHS wealth index at the intra-urban scale.

    “Some spatial optimization methods have recently been proposed to adjust for this displacement effect when modeling the DHS wealth index at the intra‐urban scale (Georganos et al., 2019). These are based on the duplication of the published DHS cluster coordinates in the four cardinal directions (N, S, E, W) to enrich or refine the contextual spatial feature extraction (Georganos et al., 2019).”

    From Spatial Optimization Methods for Malaria Risk Mapping in Sub‐Saharan African Cities Using Demographic and Health Surveys (2023) · cites this paper as Georganos et al., 2019

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Metadata

Authors
Georganos, S., Gadiaga, A. N., Linard, C., Grippa, T., Vanhuysse, S., Mboga, N., Wolff, E., Dujardin, S., & Lennert, M.
Affiliations
  • Université Libre de Bruxelles (BE)
  • University of Namur (BE)
  • Department of Geosciences, Environment & Society, Université Libre de Bruxelles (ULB), 1050 Bruxelles, Belgium
Corresponding author
Georganos, Stefanos
ORCID
Venue
Remote Sensing
Publisher
MDPI AG
Published
2019-10-29
Volume
11
Issue
21
Pages
2543
ISSN
2072-4292 (ISSN-L)
DOI
10.3390/rs11212543
OpenAlex
W2985179920
Cited by
24
References
1
Citations by year
2026: 1 · 2025: 5 · 2024: 4 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 4
FWCI
1.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • Repository of the University of Namur (submittedVersion, other-oa)
  • Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Belgian Federal Science Policy Office (SR/00/337)
  • ARES-CDD (ASSESS)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Impact of Light on Environment and Health
  • Social Sciences › Geography, Planning and Development › Remote Sensing and Land Use
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
Keywords
wealth index, very high resolution satellite imagery, land use/land cover, Demographic and Health Surveys, random forest regression, Dakar, urban resource allocation

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