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.