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

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.

Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators

“…Finally, we used 1-kilometer buffers around each geolocated survey to extract aggregated values for each predictor mentioned previously in Table 1 (i.e. proportions for categorical features, mean values for continuous ones and the mean distance to the “Wetland”, “River” and “Stream” classes), similar to research employing survey data and geographical variables [ 6 , 64 ]. Even though there was a temporal mismatch between the satellite imagery and the malaria data, we presume a degree of stationarity across the main urban extent as most of the LU changes in SSA cities are characterized mostly by expansion rather than transition, and that the malaria data are likely to be representative of land use/ecology in the period of the satellite imagery, as done in similar studies [ 6 ].…”

Land-Use Dynamics Analysis Based on Spatial Interactions Models and Satellite Images Time-Series

“…This work reaches several fields of research, such as Land-Use Transport Interactions (LUTI) models, or also methodological approaches that intend to extract indirect information from satellite images, as for example the works developed for predicting socio-economic variables (Jean et al, 2016;Georganos et al, 2019). This exploratory research aims in particular to enrich the knowledge automatically extracted from series of satellite images.…”

Is It All the Same? Mapping and Characterizing Deprived Urban Areas Using WorldView-3 Superspectral Imagery. A Case Study in Nairobi, Kenya

“…International efforts to improve the well-being of the most vulnerable urban residents, such as the United Nations (UN) Sustainable Development Goal 11 (SDG11), require a large amount of information to be regularly assembled and analyzed for adequately monitoring progress towards their targets. To address the issue of data gaps, Earth observation (EO) has been proposed as a way to map various aspects of DUAs, such as the physical environment, socio-economic status, human population counts, and health risk, among others [13][14][15]. Nonetheless, the majority of EO-based studies on DUAs focus on mapping their location and extent within a city's boundaries but not their inter-or intra-DUA variations, which is a necessary prerequisite towards evidence-based policy making [16].…”