Abstract
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.
Highlights
Quantitative criminology has conducted an investigation into crime patterns
Consistent with previous literature, the results show that the repeat phenomenon is evident for burglary in a Chinese context [6,7,17,35,36,50,54]
Using the burglary record from a large Chinese city, crime patterns were statistically significant within a certain spatiotemporal bandwidth
Summary
Quantitative criminology has conducted an investigation into crime patterns. Many studies have been conducted to detect and interpret these phenomena [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22] They are typically designed to investigate one particular pattern type and often overlook the complexity of crimes [17]. Time, and culture [23,24] They are not generalized enough to detect, display, and interpret combinations of those patterns simultaneously
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