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
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
Recommended Citation
Stewart-Wallace, Chanté L., "Data Mining and Predictive Policing" (2019). Theses, Dissertations and Culminating Projects. 310.
https://digitalcommons.montclair.edu/etd/310