Abstract
Discovering latent structures in spatial data is of critical importance to understanding the user behavior of location-based services. In this paper, we study the problem of geographic segmentation of spatial data, which involves dividing a collection of observations into distinct geo-spatial regions and uncovering abstract correlation structures in the data. We introduce a novel, Latent Poisson Factor (LPF) model to describe spatial count data. The model describes the spatial counts as a Poisson distribution with a mean that factors over a joint item-location latent space. The latent factors are constrained with weak labels to help uncover interesting spatial dependencies. We study the LPF model on a mobile app usage data set and a news article readership data set. We empirically demonstrate its effectiveness on a variety of prediction tasks on these two data sets.
Rose Yu
UC San Diego
