DMAC will develop and implement data science methods, ranging from natural language processing to predictive geospatial analysis to integrate and interrogate multiple diverse data streams alongside conventional health data. Risk is a function of exposure to a hazard, inherent vulnerability and the anticipated consequence of exposure. Thus, apart from individual demographic, economic & behavioral factors that contribute to vulnerability to adverse health outcomes, we will focus on capturing vulnerability characterized by the urban form of the two African cities. Image processing, such as convolutional neural networks, will be applied to available satellite imagery, to analyze changes in urban form including changes in building types, building, street & green area densities. Census and other geospatial survey socioeconomic data will also be critical in this assessment of vulnerability to heat in both African cities.