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arXiv · 2202.04176

Crime Hot-Spot Modeling via Topic Modeling and Relative Density Estimation

Jonathan Zhou·Sarah Huestis-Mitchell·Xiuyuan Cheng·Yao Xie
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Abstract

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.

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Jonathan Zhou, Sarah Huestis-Mitchell, Xiuyuan Cheng, Yao Xie. 2022-02-08. Crime Hot-Spot Modeling via Topic Modeling and Relative Density Estimation. https://arxiv.org/abs/2202.04176

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