eprintid: 1473071 rev_number: 24 eprint_status: archive userid: 608 dir: disk0/01/47/30/71 datestamp: 2015-12-10 15:59:08 lastmod: 2021-10-05 00:26:04 status_changed: 2015-12-10 15:59:08 type: article metadata_visibility: show creators_name: Davies, T creators_name: Marchione, E title: Event networks and the identification of crime pattern motifs ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F52 note: © 2015 Davies, Marchione. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. abstract: 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. date: 2015-11-25 date_type: published official_url: http://dx.doi.org/10.1371/journal.pone.0143638 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green article_type_text: Journal Article verified: verified_manual elements_id: 1091298 doi: 10.1371/journal.pone.0143638 pii: PONE-D-15-35665 language_elements: eng lyricists_name: Davies, Toby lyricists_id: TPDAV87 actors_name: Davies, Toby actors_id: TPDAV87 actors_role: owner full_text_status: public publication: PLoS One volume: 10 number: 11 article_number: e0143638 event_location: United States issn: 1932-6203 citation: Davies, T; Marchione, E; (2015) Event networks and the identification of crime pattern motifs. PLoS One , 10 (11) , Article e0143638. 10.1371/journal.pone.0143638 <https://doi.org/10.1371/journal.pone.0143638>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1473071/1/journal.pone.0143638.pdf