Next Article in Journal
3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability
Previous Article in Journal
Suppressing Paired Echoes Caused by Stair-Step Antenna Steering in TOPS SAR Imaging
Open AccessArticle

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

1
Department of Geosciences, Environment & Society, Université Libre de Bruxelles (ULB), 1050 Bruxelles, Belgium
2
Institute of Life, Earth and Environment, University of Namur, B-5000 Namur, Belgium
3
Department of Geography, University of Namur, B-5000 Namur, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2543; https://doi.org/10.3390/rs11212543
Received: 23 September 2019 / Revised: 22 October 2019 / Accepted: 25 October 2019 / Published: 29 October 2019
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. View Full-Text
Keywords: wealth index; DHS; very-high-resolution remote sensing; interpolation; machine learning; poverty wealth index; DHS; very-high-resolution remote sensing; interpolation; machine learning; poverty
Show Figures

Graphical abstract

MDPI and ACS Style

Georganos, S.; Gadiaga, A.N.; Linard, C.; Grippa, T.; Vanhuysse, S.; Mboga, N.; Wolff, E.; Dujardin, S.; Lennert, M. Modelling the Wealth Index of Demographic and Health Surveys within Cities Using Very High-Resolution Remotely Sensed Information. Remote Sens. 2019, 11, 2543.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop