"Poverty maps" are designed to simultaneously display the spatial distribution of welfare and different dimensions of poverty determinants. The plotting of such information on maps heavily relies on data that is collected through infrequent national household surveys and censuses. However, due to the high cost associated with this type of data collection process, poverty maps are often inaccurate in capturing the current deprivation status. In this paper, we address this challenge by means of a methodology that relies on alternative data sources from which to derive up-to-date poverty indicators, at a very fine level of spatial granularity. We validate our methodology for the city of Milano and demonstrate how it could be used to implement a poverty mapping tool for policy makers.
This project has been conducted as one of major projects at the 2016 Data Science for Social Good (DSSG) summer program at the University of Washington's eScience Institute.
This abstract has been invited for an oral presentation at the conference.