A Geostatistical Analysis of Crime In Seattle Considering Infrastructure and Data-Mined Colocation
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Delts, Ryan G
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Abstract
One of the most persistent problems in our society is criminal behavior. Crime persists regardless of perceived punishment and the increased focus of law enforcement. Objective: This thesis examines the hypotheses that specific infrastructure types can have impacts on crime densities in Seattle, Washington, and examines crime type occurrence by census blocks to observe if predictive crime pattern identification is possible. Method: The hypothesis for the significance of infrastructure on crime density is assessed by the distance-based application of the T-test for significance. The predictive crime pattern analysis hypothesis is evaluated with data mining using the Apriori algorithm to develop association rules that are predictive based on existing crime in the census blocks. Results: Both hypotheses demonstrated varying amounts of success, indicating that infrastructure does have a significant effect on crime density and that predictive data mining algorithms can create crime association rules. Conclusion: The results suggest that specific types of infrastructure do have a direct relationship with crime density in the immediate surroundings and Bus Stops, Religious Centers demonstrate higher significant effects than others when paired with specific types of crime. The pattern analysis results demonstrated that crime association rules are possible and can be used to predict crimes occurrences based on the type of crimes are reported in the surrounding area.
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Crime studies, Geospatial crime trends and patterns, Apriori Algorithms, T-Test for Significance