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Intell. Syst. Technol."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            Given a specific region, crime prediction aims to predict the occurrence of various crime events within a certain period of time in future, which is of high significance for guaranteeing urban safety. In practice, crime events are usually affected by a variety of factors from different views, e.g., the attributes of the region, the correlations between different regions, and the correlations between different categories of crime events. Moreover, these correlations are dynamically changing over time, which makes it difficult to learn the regularity and patterns in crime data for achieving accurate prediction. To address this issue, we proposed a new\n            <jats:italic>M<\/jats:italic>\n            ulti-\n            <jats:italic>V<\/jats:italic>\n            iew\n            <jats:italic>S<\/jats:italic>\n            patial-\n            <jats:italic>T<\/jats:italic>\n            emporal (MVST) model for fine-grained crime prediction. MVST model first builds a static region graph to capture the similarity between regions in terms of region attributes such as census records and economy statistics, and creates a time-dependent graph to capture the dynamic correlations between regions based on human mobility data. Meanwhile, both static and dynamic graphs are created to capture the correlations between different categories of crime events. After that, those graphs created from different views are fused together with a multi-view graph fusion module to achieve crime prediction with fine-grained time granularities, e.g., 4 hours and 12 hours. According to the experiments on two real crime datasets, our MVST model obviously outperforms existing crime prediction methods. The code of MVST model is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/weichang811\/MVST\">https:\/\/github.com\/weichang811\/MVST<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3712607","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T11:56:15Z","timestamp":1737114975000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MVST: A Multi-View Spatial-Temporal Model for Fine-Grained Crime Prediction"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9381-3268","authenticated-orcid":false,"given":"Chang","family":"Wei","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8768-6740","authenticated-orcid":false,"given":"Wengen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9931-4733","authenticated-orcid":false,"given":"Yichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2313-7635","authenticated-orcid":false,"given":"Jihong","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1949-2768","authenticated-orcid":false,"given":"Shuigeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,16]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/FSKD.2008.222"},{"key":"e_1_3_1_3_2","unstructured":"Kyunghyun Cho Bart Van Merri\u00ebnboer Caglar Gulcehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. 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