{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T20:50:28Z","timestamp":1783198228925,"version":"3.54.6"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72174203"],"award-info":[{"award-number":["72174203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Crime issues have been attracting widespread attention from citizens and managers of cities due to their unexpected and massive consequences. As an effective technique to prevent and control urban crimes, the data-driven spatial\u2013temporal crime prediction can provide reasonable estimations associated with the crime hotspot. It thus contributes to the decision making of relevant departments under limited resources, as well as promotes civilized urban development. However, the deficient performance in the aspect of the daily spatial\u2013temporal crime prediction at the urban-district-scale needs to be further resolved, which serves as a critical role in police resource allocation. In order to establish a practical and effective daily crime prediction framework at an urban police-district-scale, an \u201conline\u201d integrated graph model is proposed. A residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) are integrated with an attention mechanism in the proposed model to extract and fuse the spatial\u2013temporal features, topological graphs, and external features. Then, the \u201conline\u201d integrated graph model is validated by daily theft and assault data within 22 police districts in the city of Chicago, US from 1 January 2015 to 7 January 2020. Additionally, several widely used baseline models, including autoregressive integrated moving average (ARIMA), ridge regression, support vector regression (SVR), random forest, extreme gradient boosting (XGBoost), LSTM, convolutional neural network (CNN), and Conv-LSTM models, are compared with the proposed model from a quantitative point of view by using the same dataset. The results show that the predicted spatial\u2013temporal patterns by the proposed model are close to the observations. Moreover, the integrated graph model performs more accurately since it has lower average values of the mean absolute error (MAE) and root mean square error (RMSE) than the other eight models. Therefore, the proposed model has great potential in supporting the decision making for the police in the fields of patrolling and investigation, as well as resource allocation.<\/jats:p>","DOI":"10.3390\/ijgi11050294","type":"journal-article","created":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T06:23:08Z","timestamp":1651386188000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["An Integrated Graph Model for Spatial\u2013Temporal Urban Crime Prediction Based on Attention Mechanism"],"prefix":"10.3390","volume":"11","author":[{"given":"Miaomiao","family":"Hou","sequence":"first","affiliation":[{"name":"School of Information Technology and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information Technology and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jitao","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Emergency Management and Safety Engineering, China University of Mining and Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinge","family":"Han","sequence":"additional","affiliation":[{"name":"School of Emergency Management and Safety Engineering, China University of Mining and Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuaiqi","family":"Yuan","sequence":"additional","affiliation":[{"name":"Safety and Security Science Section, Faculty of Technology, Policy and Management, TU Delft, 2628 BX Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.apgeog.2017.06.011","article-title":"The use of predictive analysis in spatiotemporal crime forecasting: Building and testing a model in an urban context","volume":"86","author":"Rummens","year":"2017","journal-title":"Appl. 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