{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T14:57:27Z","timestamp":1763564247634,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:00:00Z","timestamp":1666051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Zhejiang Provincial Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LQ18G010001","41901160"],"award-info":[{"award-number":["LQ18G010001","41901160"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LQ18G010001","41901160"],"award-info":[{"award-number":["LQ18G010001","41901160"]}],"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 prediction is crucial for sustainable urban development and protecting citizens\u2019 quality of life. However, there exist some challenges in this regard. First, the spatio-temporal correlations in crime data are relatively complex and are heterogenous in time and space, hence it is difficult to model the spatio-temporal correlation in crime data adequately. Second, crime prediction at fine spatial temporal scales can be applied to micro patrol command; however, crime data are sparse in both time and space, making crime prediction very challenging. To overcome these challenges, based on the deep spatio-temporal 3D convolutional neural networks (ST-3DNet), we devise an improved ST-3DNet framework for crime prediction at fine spatial temporal scales (ST3DNetCrime). The framework utilizes diurnal periodic integral mapping to solve the problem of sparse and irregular crime data at fine spatial temporal scales. ST3DNetCrime can, respectively, capture the spatio-temporal correlations of recent crime data, near historical crime data and distant historical crime data as well as describe the difference in the correlations\u2019 contributions in space. Extensive experiments on real-world datasets from Los Angeles demonstrated that the proposed ST3DNetCrime framework has better prediction performance and enhanced robustness compared with baseline methods. In additon, we verify that each component of ST3DNetCrime is helpful in improving prediction performance.<\/jats:p>","DOI":"10.3390\/ijgi11100529","type":"journal-article","created":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T21:18:02Z","timestamp":1666127882000},"page":"529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["ST3DNetCrime: Improved ST-3DNet Model for Crime Prediction at Fine Spatial Temporal Scales"],"prefix":"10.3390","volume":"11","author":[{"given":"Qifen","family":"Dong","sequence":"first","affiliation":[{"name":"Department of Computer and Information Security, Zhejiang Police College, Hangzhou 310053, China"},{"name":"Ministry of Public Security\u2019s Key Laboratory of Public Security Information Application Based on Big-Data Architecture, Hangzhou 310053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Li","sequence":"additional","affiliation":[{"name":"Hahang Network Technology Co., Ltd., Hangzhou 310012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziwan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Ministry of Public Security\u2019s Key Laboratory of Public Security Information Application Based on Big-Data Architecture, Hangzhou 310053, China"},{"name":"School of Big-Data and Network Security, Zhejiang Police College, Hangzhou 310053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Security, Zhejiang Police College, Hangzhou 310053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guojun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Basic Courses, Zhejiang Police College, Hangzhou 310053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S0264-2751(00)00008-1","article-title":"Urban regeneration and sustainable development in britain","volume":"17","author":"Couch","year":"2000","journal-title":"Cities"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"588","DOI":"10.2307\/2094589","article-title":"Social change and crime rate trends: A routine activity approach","volume":"44","author":"Cohen","year":"1979","journal-title":"Am. Sociol. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1177\/0093854811417551","article-title":"Assessing the Generalizability of the Near Repeat Phenomenon","volume":"38","author":"Youstin","year":"2011","journal-title":"Crim. Justice Behav."},{"unstructured":"Cornish, D.B., and Clarke, R.V. (2014). Introduction. The Reasoning Criminal: Rational Choice Perspectives on Offending, Springer.","key":"ref_4"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1016\/S0169-2070(03)00092-X","article-title":"Short-term forecasting of crime","volume":"19","author":"Gorr","year":"2003","journal-title":"Int. J. Forecast."},{"doi-asserted-by":"crossref","unstructured":"Chen, P., Yuan, H., and Shu, X. (2008, January 5). Forecasting crime using the arima model. Proceedings of the International Conference on Fuzzy Systems and Knowledge Discovery, Jinan, China.","key":"ref_6","DOI":"10.1109\/FSKD.2008.222"},{"doi-asserted-by":"crossref","unstructured":"Cesario, E., Catlett, C., and Talia, D. (2016, January 8\u201312). Forecasting crimes using autoregressive models. Proceedings of the International Conference on Dependable, Autonomic and Secure Computing, Pervasive Intelligence and Computing, Big Data Intelligence and Computing and Cyber Science and Technology Congress, Auckland, New Zealand.","key":"ref_7","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.138"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1057\/palgrave.sj.8350066","article-title":"The utility of hotspot mapping for predicting spatial patterns of crime","volume":"21","author":"Chainey","year":"2008","journal-title":"Secur. J."},{"key":"ref_9","first-page":"7","article-title":"Examining the influence of cell size and bandwidth size on kernel density estimation crime hotspot maps for predicting spatial patterns of crime","volume":"60","author":"Chainey","year":"2013","journal-title":"Bull. Geogr. Soc. Liege"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.dss.2014.02.003","article-title":"Predicting crime using twitter and kernel density estimation","volume":"61","author":"Gerber","year":"2014","journal-title":"Decis. Support Syst."},{"key":"ref_11","first-page":"1","article-title":"A review of spatio-temporal pattern analysis approaches on crime analysis","volume":"9","author":"Leong","year":"2015","journal-title":"Int. E-J. Crim. Sci."},{"doi-asserted-by":"crossref","unstructured":"Hou, M., Hu, X., Cai, J., Han, X., and Yuan, S. (2022). An Integrated Graph Model for Spatial\u2013Temporal Urban Crime Prediction Based on Attention Mechanism. ISPRS Int. J. Geo-Inf., 11.","key":"ref_12","DOI":"10.3390\/ijgi11050294"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1007\/s11401-019-0168-y","article-title":"Deep learning for real-time crime forecasting and its ternarization","volume":"40","author":"Wang","year":"2019","journal-title":"Chin. Ann. Math. Ser. B"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3913","DOI":"10.1109\/TITS.2019.2906365","article-title":"Deep Spatial-Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting","volume":"21","author":"Guo","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1080\/13658816.2020.1737701","article-title":"A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery","volume":"34","author":"Yang","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"doi-asserted-by":"crossref","unstructured":"Yu, H., Liu, L., Yang, B., and Lan, M. (2020). Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method. ISPRS Int. J. Geo-Inf., 9.","key":"ref_16","DOI":"10.3390\/ijgi9120732"},{"doi-asserted-by":"crossref","unstructured":"Zhao, X.Y., and Tang, J. (2017, January 6\u201310). Modeling Temporal-Spatial Correlations for Crime Prediction. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore.","key":"ref_17","DOI":"10.1145\/3132847.3133024"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.apgeog.2018.08.001","article-title":"A spatio-temporal kernel density estimation framework for predictive crime hotspot mapping and evaluation","volume":"99","author":"Hu","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1111\/rssc.12277","article-title":"Self-exciting point processes with spatial covariates: Modelling the dynamics of crime","volume":"67","author":"Reinhart","year":"2018","journal-title":"J. R. Stat. Soc. Ser. C Appl. Stat."},{"doi-asserted-by":"crossref","unstructured":"Farjami, Y., and Abdi, K. (2021). A genetic-fuzzy algorithm for spatio-temporal crime prediction. J. Ambient. Intell. Humaniz. Comput.","key":"ref_20","DOI":"10.1007\/s12652-020-02858-3"},{"unstructured":"Yong, Z., Almeida, M., Morabito, M., and Wei, D. (2017, January 9\u201310). Crime Hot Spot Forecasting: A Recurrent Model with Spatial and Temporal Information. Proceedings of the IEEE International Conference on Big Knowledge, Hefei, China.","key":"ref_21"},{"doi-asserted-by":"crossref","unstructured":"Huang, C., Zhang, J., Zheng, Y., and Chawla, N.V. (2018, January 22\u201326). DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction. Proceedings of the 27th ACM International Conference on Information and Knowledge, Torino, Italy.","key":"ref_22","DOI":"10.1145\/3269206.3271793"},{"doi-asserted-by":"crossref","unstructured":"Huang, C., Zhang, C., Zhao, J., Wu, X., and Yin, D. (2019, January 13\u201317). MiST: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event Forecasting. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.","key":"ref_23","DOI":"10.1145\/3308558.3313730"},{"doi-asserted-by":"crossref","unstructured":"Wang, Y., Ge, L., Li, S., and Chang, F. (2020, January 3\u20136). Deep Temporal Multi-graph Convolutional Network for Crime Prediction. Proceedings of the International Conference on Conceptual Modeling, Vienna, Austria.","key":"ref_24","DOI":"10.1007\/978-3-030-62522-1_39"},{"doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., and Qi, D. (2017, January 4\u20139). Deep spatio-temporal residual networks for citywide crowd flows prediction. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","key":"ref_25","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.artint.2018.03.002","article-title":"Predicting citywide crowd flows using deep spatio-temporal residual networks","volume":"259","author":"Zhang","year":"2018","journal-title":"Artif. Intell."},{"doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","key":"ref_27","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, J., Wang, Z., and Yin, H. (2021). An Adaptive Spatial Resolution Method Based on the ST-ResNet Model for Hourly Property Crime Prediction. ISPRS Int. J. Geo-Inf., 10.","key":"ref_28","DOI":"10.3390\/ijgi10050314"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s40163-020-00116-7","article-title":"A systematic review on spatial crime forecasting","volume":"9","author":"Kounadi","year":"2020","journal-title":"Crime Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"533","DOI":"10.3923\/itj.2006.353.357","article-title":"Time series prediction based on support vector regression","volume":"5","author":"Lin","year":"2006","journal-title":"Inf. Technol. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.eswa.2017.12.037","article-title":"An architecture for emergency event prediction using LSTM recurrent neural networks","volume":"97","author":"Cortez","year":"2018","journal-title":"Expert Syst. Appl."},{"unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015, January 7\u201312). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada.","key":"ref_32"},{"doi-asserted-by":"crossref","unstructured":"Urcuqui, C., Moreno, J., Montenegro, C., Riascos, A., and Dulce, M. (2020, January 5\u20137). Accuracy and Fairness in a Conditional Generative Adversarial Model of Crime Prediction. Proceedings of the 7th International Conference on Behavioural and Social Computing, Bournemouth, UK.","key":"ref_33","DOI":"10.1109\/BESC51023.2020.9348315"},{"doi-asserted-by":"crossref","unstructured":"Hosseini, S., Yin, H., Zhang, M., Elovici, Y., and Zhou, X. (2018, January 25\u201328). Mining Subgraphs from Propagation Networks through Temporal Dynamic Analysis. Proceedings of the 19th IEEE International Conference on Mobile Data Management, Aalborg, Denmark.","key":"ref_34","DOI":"10.1109\/MDM.2018.00023"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TKDE.2020.2982148","article-title":"SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual Embedding","volume":"34","author":"Najafipour","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/10\/529\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:56:51Z","timestamp":1760144211000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/10\/529"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,18]]},"references-count":35,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["ijgi11100529"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11100529","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2022,10,18]]}}}