Date of Award

5-2019

Document Type

Thesis

Degree Name

Master of Science (MS)

College/School

College of Science and Mathematics

Department/Program

Computer Science

Thesis Sponsor/Dissertation Chair/Project Chair

Katherine Herbert

Committee Member

Bharath Kumar Samanthula

Committee Member

John Jenq

Subject(s)

Data mining in law enforcement, Data mining -- Statistical methods, Criminal statistics -- California -- San Francisco -- Data processing, Criminal statistics -- Illinois -- Chicago -- Data processing

Abstract

This paper focuses on the operation and utilization of predictive policing software that generates spatial and temporal hotspots. There is a literature review that evaluates previous work surrounding the topics branched from predictive policing. It dissects two different crime datasets for San Francisco, California and Chicago, Illinois. Provided, is an in depth comparison between the datasets using both statistical analysis and graphing tools. Then, it shows the application of the Apriori algorithm to re-enforce the formation of possible hotspots pointed out in a actual predictive policing software. To further the analysis, targeted demographics of the study were evaluated to create a snapshot of the factors that have attributed to the safety of the neighborhoods. The results of this study can be used to create solutions for long term crime reduction by adding green spaces and community planning in areas with high crime rates and heavy environmental neglect.

File Format

PDF

Available for download on Wednesday, July 22, 2020

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