Authors:
Solomon Y. Sonya
;
Luke Brantley
and
Meagan Whitaker
Affiliation:
U.S.A. Air Force Academy, United States
Keyword(s):
Opportunistic Crime, Statistical Analysis, Frequency Distribution, Pattern Recognition.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Data Mining and Business Analytics
;
Decision Analysis
;
Forecasting
;
Methodologies and Technologies
;
Operational Research
;
OR in Education
;
Pattern Recognition
;
Software Engineering
Abstract:
Urban opportunistic crime is a problem throughout the world causing financial, physical, and emotional damages to innocent citizens and organizations. Opportunistic crimes require minimal reconnaissance and preparation in order to conduct an attack (e.g., burglary, robbery, vandalism, and assault). Opportunistic criminals are more spontaneous in nature making their actions difficult to anticipate and create an approach to reduce these crimes. Statistical analysis of crimes may reveal distinct patterns from which a strategy can be created to better mitigate future crimes. This paper describes analysis performed on real-world campus crime data in which distinct correlations were discovered to determine the significant factors that motivate opportunistic crime. This research concludes by developing a dynamic defender placement strategy that adapts over time to reduce the utility of opportunistic crimes. The research contribution allows for the determination of significant factors motiva
ting opportunistic crime and releases a program that maps crime occurrences over time, determines the minimum defender allocation for a given area, and dynamically specifies defender placement strategy to mitigate future crime. The novelty of this approach allows for application to other campuses, shopping complexes, and living districts to form conclusions about opportunistic criminal activity and formulate an approach to abate such crimes.
(More)