Detecting latent groups in fatal police shootings data using established and emerging methods of unsupervised classfication

Unsupervised classification methods are still being discovered and refined, so it is necessary to look at the methods available and discern which may be most useful in different situations. Taking data collected by the Washington Post on fatal police shootings, various statistical methods of unsupervised classification were explored to determine if there were any meaningful groups present within the data. The variables explored were “race”- the race of the individual, “threat level”- what the perceived threat level of the situation was, “signs of mental illness”- whether or not the individual displayed signs of mental illness at the time of the shooting, and “body camera”- whether or not there was a body camera present for at least part of the incident. The variables were examined for latent groups using a variety of methods: k-means clustering, k-medoids clustering, hierarchical clustering, fuzzy clustering, densitybased spatial clustering of applications with noise (DBSCAN), gaussian mixture model (GMM), and principal components analysis (PCA). The number of clusters to retain was explored for k-means, kmedoids, hierarchical clustering, fuzzy clustering, DBSCAN, and GMM. The means were then examined to understand the how the clusters were structured. PCA required the determination of how many principal components to retain and then the rotation matrix was examined to determine what factors were important for each cluster. Though no conclusive consensus was achieved by all the models, there was some overlap in the structure of some of the latent groups that emerged. Many of the final solutions found differences by race, age, and mental illness that may call for more analyses to done to understand the groups of people that are more likely to be involved in a fatal police shooting.