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.

中文翻译：

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时空上犯罪模式的构建，侦破和解释

实证研究集中于调查犯罪对之间的互动关系。但是，许多其他类型的犯罪模式尚未得到广泛研究。在本文中，我们介绍了四种组合的三种基本犯罪模式。基于图论，使用犯罪学理论构建和分析每种模式的子图。进行了蒙特卡洛模拟，以检验这些模式的重要性。犯罪模式具有统计学意义，并产生不同程度的犯罪风险。与经典模式相比，组合模式会产生更高的风险级别。在这些模式中，“同现，重复和转移”产生最高的犯罪风险，而“重复”产生的犯罪风险低得多。“同时发生和转移”和“重复和转移”表明风险水平是波动的，而其他风险则表明持续降低。这些结果概述了拟议的犯罪模式的重要性，并呼吁采取有区别的犯罪预防策略。该方法可以扩展到使用点事件作为研究对象的其他研究领域。