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Location-based data is increasingly prevalent with the rapid increase and adoption of mobile devices. In this paper we address the problem of learning spatial density models, focusing specifically on individual-level data. Modeling and predicting a spatial distribution for an individual is a challenging problem given both (a) the typical sparsity of data at the individual level and (b) the heterogeneity of spatial mobility patterns across individuals. We investigate the application of kerneldoi:10.1145/2623330.2623681 dblp:conf/kdd/LichmanS14 fatcat:adhy4z7h6fap3dmavmsit5uari