Event Networks and the Identification of Crime Pattern Motifs.
Event Networks and the Identification of Crime Pattern Motifs.
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DOI：
10.1371/journal.pone.0143638
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Marchione E
中科院分区：
文献类型：
--
作者：
Davies T;Marchione E
在本文中，我们证明了使用网络分析来表征时空事件中聚类的模式。这种聚类在犯罪研究中既具有理论和实际重要性，又是许多预防策略的基础。但是，现有的分析方法仅表明数据中存在聚类，同时几乎没有洞察到存在的模式的性质。在这里，我们展示了如何将成对事件的分类作为时空和时间上的近距离定义网络，从而概括了先前的方法。然后，将图理论技术应用于这些网络可以比以前可能更深入地了解数据的结构。特别是，我们专注于网络图案的识别，这些识别在时空行为方面具有明确的解释。统计分析对基础数据的性质变得复杂，我们提供了一种可以生成适当随机图的方法。两个数据集用作案例研究：全球范围内的海上盗版，以及在市区的住宅入室盗窃。在这两种情况下，都发现了相同的3个vertex基序。该结果表明，事件往往不仅成对发生，而且实际上是在受限的时空结构域内的较大组中发生的。在四个vertex情况下，在每种情况下都发现不同的基序都很重要，这表明该技术能够区分比以前更细的颗粒状的聚类模式。
In this paper we demonstrate the use of network analysis to characterise patterns of clustering in spatio-temporal events. Such clustering is of both theoretical and practical importance in the study of crime, and forms the basis for a number of preventative strategies. However, existing analytical methods show only that clustering is present in data, while offering little insight into the nature of the patterns present. Here, we show how the classification of pairs of events as close in space and time can be used to define a network, thereby generalising previous approaches. The application of graph-theoretic techniques to these networks can then offer significantly deeper insight into the structure of the data than previously possible. In particular, we focus on the identification of network motifs, which have clear interpretation in terms of spatio-temporal behaviour. Statistical analysis is complicated by the nature of the underlying data, and we provide a method by which appropriate randomised graphs can be generated. Two datasets are used as case studies: maritime piracy at the global scale, and residential burglary in an urban area. In both cases, the same significant 3-vertex motif is found; this result suggests that incidents tend to occur not just in pairs, but in fact in larger groups within a restricted spatio-temporal domain. In the 4-vertex case, different motifs are found to be significant in each case, suggesting that this technique is capable of discriminating between clustering patterns at a finer granularity than previously possible.