US Fatal Police Shooting Analysis and Prediction
Yuan Wang, Yangxin Fan

TL;DR
This paper applies multidimensional statistical analysis to understand and predict US fatal police shootings, revealing key factors and testing for racial discrimination with high predictive accuracy.
Contribution
It introduces a new method to quantify media reporting deviations, analyzes comprehensive datasets with pattern mining and clustering, and develops models to predict shooting rates and victim race.
Findings
Police shooting rate depends on multiple variables like land area and gun ownership.
Best regression model predicts shooting rates with 88.53% correlation.
No significant evidence of racial discrimination in fatal shootings.
Abstract
We believe that "all men are created equal". With the rise of the police shootings reported by media, more people in the U.S. think that police use excessive force during law enforcement, especially to a specific group of people. We want to apply multidimensional statistical analysis to reveal more facts than the monotone mainstream media. Our paper has three parts. First, we proposed a new method to quantify fatal police shooting news reporting deviation of mainstream media, which includes CNN, FOX, ABC, and NBC. Second, we analyzed the most comprehensive US fatal police shooting dataset from Washington Post. We used FP-growth to reveal the frequent patterns and DBSCAN clustering to find fatal shooting hotspots. We brought multi-attributes (social economics, demographics, political tendency, education, gun ownership rate, police training hours, etc.) to reveal connections under the…
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Taxonomy
TopicsCrime Patterns and Interventions · Gun Ownership and Violence Research · Data-Driven Disease Surveillance
MethodsLogistic Regression · Approximate Bayesian Computation
