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Open AccessArticle

Construction, Detection, and Interpretation of Crime Patterns over Space and Time

by Zengli Wang 1,2,* and Hong Zhang 3,4
1
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
2
National Engineering Research Center of Biomaterials, Nanjing Forestry University, Nanjing 210037, China
3
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China
4
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 339; https://doi.org/10.3390/ijgi9060339
Received: 24 March 2020 / Revised: 30 April 2020 / Accepted: 25 May 2020 / Published: 26 May 2020
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
Empirical studies have focused on investigating the interactive relationships between crime pairs. However, many other types of crime patterns have not been extensively investigated. In this paper, we introduce three basic crime patterns in four combinations. Based on graph theory, the subgraphs for each pattern were constructed and analyzed using criminology theories. A Monte Carlo simulation was conducted to examine the significance of these patterns. Crime patterns were statistically significant and generated different levels of crime risk. Compared to the classical patterns, combined patterns create much higher risk levels. Among these patterns, “co-occurrence, repeat, and shift” generated the highest level of crime risk, while “repeat” generated much lower levels of crime risk. “Co-occurrence and shift” and “repeat and shift” showed undulated risk levels, while others showed a continuous decrease. These results outline the importance of proposed crime patterns and call for differentiated crime prevention strategies. This method can be extended to other research areas that use point events as research objects. View Full-Text
Keywords: crime pattern; repeat and near-repeat; Monte Carlo Simulation; relations; burglary crime pattern; repeat and near-repeat; Monte Carlo Simulation; relations; burglary
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Wang, Z.; Zhang, H. Construction, Detection, and Interpretation of Crime Patterns over Space and Time. ISPRS Int. J. Geo-Inf. 2020, 9, 339.

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