  The classification of crime into discrete categories entails a massive loss
of information. Crimes emerge out of a complex mix of behaviors and situations,
yet most of these details cannot be captured by singular crime type labels.
This information loss impacts our ability to not only understand the causes of
crime, but also how to develop optimal crime prevention strategies. We apply
machine learning methods to short narrative text descriptions accompanying
crime records with the goal of discovering ecologically more meaningful latent
crime classes. We term these latent classes "crime topics" in reference to
text-based topic modeling methods that produce them. We use topic distributions
to measure clustering among formally recognized crime types. Crime topics
replicate broad distinctions between violent and property crime, but also
reveal nuances linked to target characteristics, situational conditions and the
tools and methods of attack. Formal crime types are not discrete in topic
space. Rather, crime types are distributed across a range of crime topics.
Similarly, individual crime topics are distributed across a range of formal
crime types. Key ecological groups include identity theft, shoplifting,
burglary and theft, car crimes and vandalism, criminal threats and confidence
crimes, and violent crimes. Though not a replacement for formal legal crime
classifications, crime topics provide a unique window into the heterogeneous
causal processes underlying crime.
