DEMO

Environment and population are closely linked, but their interactions remain challenging to assess. To fill this gap, modeling the environment at a fine resolution brings a significant value, if combined with population-based studies. This is particularly challenging in regions where the availability of both population and environmental data is limited. In low- and middle-income countries, many demographic and health data are from nationally representative household surveys which now provide approximate geolocations of the sampled households. In parallel, freely available remote sensing data, due to their high spatial and temporal resolution, make it possible to capture the local environment at any time. This study aims to correlate standard demographic and health information with a highresolution environment characterization derived from satellite data, encompassing both rural and urban areas in sub-Saharan Africa. We use the Malaria Indicator Survey (MIS) conducted in 2017-2018 in Burkina Faso. We first present a deep semisupervised domain adaptation strategy based on the inter-tropical climatic characteristics of the country for precisely mapping Local Climate Zones (LCZs). This strategy models seasonal variations through contrastive learning to extract useful information for the mapping process. We then use this high-resolution LCZ map to characterize, in four groups, the immediate environment of the sampled households. We find a significant association between these local environments and malaria among households’ children. Going beyond the traditional dichotomous urban/rural characterization, our results provide interesting insights for public health. This innovative method offers new avenues for exploring population and environment interactions, especially in the growing climate change concern.

Recent research in demography focuses on linking population data to environmental indicators. Satellite imagery can support such projects by providing data at a large scale and a high frequency. Moreover, population surveys often provide geolocations of households, yet sometimes with an offset, to guarantee data confidentiality. In such cases, the proper management of this incertitude is required, to accurately link environmental indicators such as land cover/land use maps or spectral indices to population data. In this paper, we introduce a method based on the random sampling of possible households geolocations around the coordinates provided. Then, we link a land cover map generated using semi-supervised deep learning and a Malaria Indicator Survey in Burkina Faso. After linking households to their close environment, we distinguish several types of environment conducive to high malaria rates, beyond the urban/rural dichotomy.

Environment and demographic dynamics are strongly linked. However, relevant data to study this interaction may be scarce especially in sub-Saharan Africa where it is not always possible to perform such studies with a high temporal frequency. Satellite imagery, when linked to demographic data, can be a significant asset to estimate missing data as it covers every country with both high spatial and temporal resolution. We aim to take advantage of satellite data to characterize the environment in inter-tropical areas. This environment is regulated by the changing of two seasons that are essential to consider. We introduce a semi-supervised domain adaptation strategy for neural networks based on seasonal changes. This strategy can be used to produce land cover maps in regions of the world where limited labeled datasets are available. We apply this method to produce environmental indicators and link them to malaria rates from the Malaria Indicator Survey of Burkina Faso. We show that malaria rates are correlated not only to urbanisation but also to the environmental characterisation of studied areas.