{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T23:49:54Z","timestamp":1774568994143,"version":"3.50.1"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["72072091, 72002067 and 71801123"],"award-info":[{"award-number":["72072091, 72002067 and 71801123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Project of Natural Science Research in Jiangsu Provincial Colleges and Universities","award":["21KJA520002"],"award-info":[{"award-number":["21KJA520002"]}]},{"name":"Humanities and Social Science Research Project of Ministry of Education of China","award":["20YJCZH081"],"award-info":[{"award-number":["20YJCZH081"]}]},{"name":"Jiangsu Provincial Colleges and Universities Outstanding S&T Innovation Team Fund"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>\n            Crime poses a major threat to human life and property, which has been recognized as one of the most crucial problems in our society. Predicting the number of crime incidents in each region of a city before they happen is of great importance to fight against crime. There has been a great deal of research focused on crime prediction, ranging from introducing diversified data sources to exploring various prediction models. However, most of the existing approaches fail to offer fine-scale prediction results and take little notice of the intricate spatial-temporal-categorical correlations contained in crime incidents. In this article, we propose a tailor-made framework called\n            <jats:sc>CrimeTensor<\/jats:sc>\n            to predict the number of crime incidents belonging to different categories within each target region via tensor learning with spatiotemporal consistency. In particular, we model the crime data as a tensor and present an objective function which tries to take full advantage of the spatial, temporal, and categorical correlations contained in crime incidents. Moreover, a well-designed optimization algorithm which transforms the objective into a compact form and then applies CP decomposition to find the optimal solution is elaborated to solve the objective function. Furthermore, we develop an enhanced framework which takes a set of pre-selected regions to conduct prediction so as to further improve the computational efficiency of the optimization algorithm. Finally, extensive experiments are performed on both proprietary and public datasets and our framework significantly outperforms all the baselines in terms of each evaluation metric.\n          <\/jats:p>","DOI":"10.1145\/3501807","type":"journal-article","created":{"date-parts":[[2022,3,26]],"date-time":"2022-03-26T03:06:38Z","timestamp":1648263998000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["<scp>CrimeTensor<\/scp>\n            : Fine-Scale Crime Prediction via Tensor Learning with Spatiotemporal Consistency"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8035-5255","authenticated-orcid":false,"given":"Weichao","family":"Liang","sequence":"first","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0591-1861","authenticated-orcid":false,"given":"Zhiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Nanjing Audit University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1680-1083","authenticated-orcid":false,"given":"Zhe","family":"Li","sequence":"additional","affiliation":[{"name":"Hubei Engineering University, Xiaogan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9630-795X","authenticated-orcid":false,"given":"Yong","family":"Ge","sequence":"additional","affiliation":[{"name":"University of Arizona, Tucson, Arizona, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2018.03.084"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3190345"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2663204.2663254"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"e_1_3_2_6_2","first-page":"43","volume-title":"Business and the Risk of Crime in China","author":"Broadhurst Roderic","year":"2011","unstructured":"Roderic Broadhurst, John Bacon-Shone, Brigitte Bouhours, and Thierry Bouhours. 2011. Crime and its control in China. In Business and the Risk of Crime in China. Australian National University Press, Canberra, 43\u201378."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2019.01.003"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CCAA.2017.8229823"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2019.102563"},{"key":"e_1_3_2_10_2","first-page":"1114","volume-title":"Proceedings of the 19th International Conference on Artificial Intelligence and Statistics","author":"Cheng Hao","year":"2016","unstructured":"Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, and Dale Schuurmans. 2016. Scalable and sound low-rank tensor learning. 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