Abstract
Nan-negative matrix factorization (NAfP) topic modeling has recently been introduced for the categorization and analysis of crime report text. Topic modeling in Otis context allows for more nuanced categories of crime compared to official UCR categorizations. In this paper we suggest two metrics for the evaluation of crime topic models: coherence and spatial concentration. The importance of space comes into play through Weisburd's law of crime concentration, that states a large percentage of crime occurs in a small area of a city. We investigate the extent to which topic models that improve coherence lead to higher levels of crime concentration. Through analyzing a dataset of crime reports from Los Angeles, CA, we find that Latent Dirichlst Allocation (IDA) generates crime topics with both higher coherence and crime concentration. While NMF improves the coherence compared to UCR categorization, die spatial concentration is not as high. These foldings have important implications for hotspot policing.
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