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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)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Sonya, S.; Brantley, L. and Whitaker, M. (2016). Urban Crime Mitigation - A Model to Derive Criminal Patterns and Determine Defender Placement to Reduce Opportunistic Crime. In Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - ICORES; ISBN 978-989-758-171-7; ISSN 2184-4372, SciTePress, pages 218-224. DOI: 10.5220/0005827302180224

@conference{icores16,
author={Solomon Y. Sonya. and Luke Brantley. and Meagan Whitaker.},
title={Urban Crime Mitigation - A Model to Derive Criminal Patterns and Determine Defender Placement to Reduce Opportunistic Crime},
booktitle={Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - ICORES},
year={2016},
pages={218-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005827302180224},
isbn={978-989-758-171-7},
issn={2184-4372},
}

TY - CONF

JO - Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - ICORES
TI - Urban Crime Mitigation - A Model to Derive Criminal Patterns and Determine Defender Placement to Reduce Opportunistic Crime
SN - 978-989-758-171-7
IS - 2184-4372
AU - Sonya, S.
AU - Brantley, L.
AU - Whitaker, M.
PY - 2016
SP - 218
EP - 224
DO - 10.5220/0005827302180224
PB - SciTePress