Date of Award
Master of Science (MS)
College of Science and Mathematics
Thesis Sponsor/Dissertation Chair/Project Chair
Bharath Kumar Samanthula
Data mining in law enforcement, Data mining -- Statistical methods, Criminal statistics -- California -- San Francisco -- Data processing, Criminal statistics -- Illinois -- Chicago -- Data processing
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.
Stewart-Wallace, Chanté L., "Data Mining and Predictive Policing" (2019). Theses, Dissertations and Culminating Projects. 310.
Available for download on Wednesday, July 22, 2020