Monitoring patterns of segregation and inequality at small area geographic levels is extremely costly. However, the increased availability of data through nontraditional sources such as satellite imagery facilitates this task. This paper assess the relevance of data from nightlight and day-time satellite imagery as well as building footprints and localization of points of interest for mapping variability in socioeconomic outcomes, i.e., household income, labor formality, life quality perception and household informality. The outcomes are computed at a granular level by combining census data, survey data, and small area estimation. The results reveal that non traditional sources are important to predict spatial differences socio-economic outcomes. Furthermore, the combination of all sources creates complementarities that enable a more accurate spatial distribution of the studied variables.
