Crime Hot-Spot Modeling via Topic Modeling and Relative Density Estimation
Jonathan Zhou, Sarah Huestis-Mitchell, Xiuyuan Cheng, Yao Xie
We present a method to capture groupings of similar calls and determine their relative spatial distribution from a collection of crime record narratives. We first obtain a topic distribution for each narrative, and then propose a nearest neighbors relative density estimation (kNN-RDE) approach to obtain spatial relative densities per topic. Experiments over a large corpus ($n=475,019$) of narrative documents from the Atlanta Police Department demonstrate the viability of our method in capturing geographic hot-spot trends which call dispatchers do not initially pick up on and which go unnoticed due to conflation with elevated event density in general.
cs.LG, cs.CL, cs.IR