Dissertation/Thesis Abstract

A Critical Study of Geospatial Algorithm Use in Crime Analysis and Predictive Policing
by Weathington, Katy, M.S., Marquette University, 2020, 75; 27837264
Abstract (Summary)

We examine in detail two geospatial analysis algorithms commonly used in predictive policing. The k-means clustering algorithm is used to partition input data into k clusters, while Kernel Density Estimation algorithms convert geospatial data into a 2-dimensional probability distribution function. Both algorithms serve unique roles in predictive policing, helping to inform the allocation of limited police resources.

Through critical analysis of the k-means algorithm, we found that parameter choice can greatly impact how crime in a city is clustered, which therefor impacts how mental models of crime in the city are developed. Interviews with crime analysts who regularly used k-means revealed that parameters are overwhelmingly chosen arbitrarily. Similarly, KDE parameters greatly influence the resulting PDF, which are visualized in difficult to interpret heatmaps. A mixed method user study with participants of varying backgrounds revealed that those with backgrounds in law enforcement and/or criminal justice rarely actively chose the parameters used, in part due to not fully comprehending the meaning of less obvious parameters. It was also found that individuals with different backgrounds tended to interpret heatmaps and make resource distribution decisions differently.

There are several implications from these findings. Primarily, this implies that most would-be users lack the training and expertise to reliably implement and interpret geospatial crime analysis algorithms. Both within and without crime labs, critical thought is rarely given to parameter choice, especially for parameters without a clear, easily understandable explanation. These factors illuminate predictive policing being an inexact science, despite being taken as reliable and objective. These shortcomings and misconceptions, due to their pivotal role at the earliest part of the policing and criminal justice system, have long term consequences for denizens of any place being policed at behest of an algorithm.

Indexing (document details)
Advisor: Guha, Shion
Commitee: Zimmer, Michael, Snowden, Aleksandra
School: Marquette University
Department: Computing
School Location: United States -- Wisconsin
Source: MAI 81/10(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Information science, Computer science, Criminology
Keywords: Geospatial analysis, Human computer interaction, Predictive policing
Publication Number: 27837264
ISBN: 9798617024731
Copyright © 2020 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest