Research has shown that police officer involved shootings, misconduct and excessive use of force complaints exhibit network effects, where officers are at greater risk of being involved in these incidents when they socialize with officers who have a history of use of force and misconduct. In this work, we first construct a network survival model for the time-to-event of use of force incidents involving new police trainees. The model includes network effects of the diffusion of risk from field training officer (FTO) to trainee. We then introduce a network rewiring algorithm to maximize the expected time to use of force events upon completion of field training. We study several versions of the algorithm, including constraints that encourage demographic diversity of FTOs. Using data from Indianapolis, we show that rewiring the network can increase the expected time (in days) of a recruit's first use of force incident by 8%. We then discuss the potential benefits and challenges associated with implementing such an algorithm in practice.

Increasing rates of opioid drug abuse and heightened prevalence of online support communities underscore the necessity of employing data mining techniques to better understand drug addiction using these rapidly developing online resources. In this work, we obtained data from Reddit, an online collection of forums, to gather insight into drug use/misuse using text snippets from users narratives. Specifically, using users' posts, we trained a binary classifier which predicts a user's transitions from casual drug discussion forums to drug recovery forums. We also proposed a Cox regression model that outputs likelihoods of such transitions. In doing so, we found that utterances of select drugs and certain linguistic features contained in one's posts can help predict these transitions. Using unfiltered drug-related posts, our research delineates drugs that are associated with higher rates of transitions from recreational drug discussion to support/recovery discussion, offers insight into modern drug culture, and provides tools with potential applications in combating the opioid crisis.

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.