{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:02:08Z","timestamp":1783036928275,"version":"3.54.6"},"reference-count":42,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"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":["BDCC"],"abstract":"<jats:p>As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities.<\/jats:p>","DOI":"10.3390\/bdcc9070179","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T09:57:39Z","timestamp":1751536659000},"page":"179","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago"],"prefix":"10.3390","volume":"9","author":[{"given":"Yuxiao","family":"Fan","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"},{"name":"Key Laboratory of Security Technology & Risk Assessment, Ministry of Public Security, Beijing 102623, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinming","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information Technology and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"},{"name":"Key Laboratory of Security Technology & Risk Assessment, Ministry of Public Security, Beijing 102623, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"ref_1","first-page":"41","article-title":"Ecological security of communities in polish cities","volume":"16","author":"Kornec","year":"2020","journal-title":"J. Plant Growth Regul."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s10940-016-9295-8","article-title":"Crime at places and spatial concentrations: Exploring the spatial stability of property crime in Vancouver BC, 2003\u20132013","volume":"33","author":"Andresen","year":"2017","journal-title":"J. Quant. Criminol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1080\/13501763.2023.2291132","article-title":"Locality as a safe haven: Place-based resentment and political trust in local and national institutions","volume":"31","author":"Hegewald","year":"2023","journal-title":"J. Eur. Public Policy"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, Y. (2021, January 23\u201325). Research on influencing factors of local epidemic prevention and control policy making: Based on the fuzzy set qualitative comparative analysis of 46 cities aiming at Chengdu\u2019s epidemic prevention and control policies. Proceedings of the 7th International Conference on Humanities and Social Science Research (ICHSSR 2021), Qingdao, China.","DOI":"10.2991\/assehr.k.210519.028"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s10098-009-0204-9","article-title":"Assessment of the performance of different compost models to manage urban household organic solid wastes","volume":"11","author":"Jayaram","year":"2009","journal-title":"Clean Technol. Environ. Policy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/JPROC.2010.2081651","article-title":"An integrated framework for smart microgrids modeling, monitoring, control, communication, and verification","volume":"99","author":"Vaccaro","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2984","DOI":"10.1109\/JSTARS.2015.2420582","article-title":"Processing of multiresolution thermal hyperspectral and digital color data: Outcome of the 2014 IEEE GRSS data fusion contest","volume":"8","author":"Liao","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","first-page":"340","article-title":"A standard cloud platform technology of traffic performance index based on multi-source data fusion","volume":"7","author":"Qiu","year":"2018","journal-title":"Open J. Trans. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"496","DOI":"10.11648\/j.ajtas.20150406.20","article-title":"Modelling crime rate using a mixed effects regression model","volume":"4","author":"Muchwanju","year":"2015","journal-title":"Am. J. Theor. Appl. Stat."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1093\/oxfordjournals.bjc.a014156","article-title":"New insights into the spatial and temporal distribution of repeat victimization","volume":"37","author":"Johnson","year":"1997","journal-title":"Br. J. Criminol."},{"key":"ref_12","unstructured":"Eysenck, H.J., and Gudjonsson, G.H. (1997). Crime and personality. The Causes and Cures of Criminality, Springer."},{"key":"ref_13","unstructured":"Clarke, R.V.G., and Felson, M. (1993). Routine Activity and Rational Choice, Routledge."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1002\/cplx.20316","article-title":"Complex adaptive systems and game theory: An unlikely union","volume":"16","author":"Hadzikadic","year":"2010","journal-title":"Complexity"},{"key":"ref_15","first-page":"168","article-title":"Selected theories on criminalisation of hacking","volume":"6","author":"Mohamad","year":"2021","journal-title":"Int. J. Law Gov. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"44","DOI":"10.26480\/ccsj.02.2022.44.48","article-title":"Students\u2019 predisposition to being a victim of internet crime in tertiary institutions of Nigeria","volume":"3","author":"Tolulope","year":"2022","journal-title":"Cult. Commun. Social. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s43762-023-00094-x","article-title":"Seasonal characteristics of crime: An empirical investigation of the temporal fluctuation of the different types of crime in London","volume":"3","author":"Shiode","year":"2023","journal-title":"Comput. Urban Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1177\/0013916510397758","article-title":"Seasonal assault and neighborhood deprivation in South Africa: Some preliminary findings","volume":"44","author":"Breetzke","year":"2012","journal-title":"Environ. Behav."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tang, Y., Zhu, X., Guo, W., Wu, L., and Fan, Y. (2019). Anisotropic diffusion for improved crime prediction in urban China. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8050234"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Liu, X. (2017). Analysis of burglary hot spots and near-repeat victimization in a large Chinese city. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050148"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1177\/0022427813483753","article-title":"Uncovering the spatial patterning of crimes: A criminal movement model (CriMM)","volume":"51","author":"Reid","year":"2014","journal-title":"J. Res. Crime Delinq."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, M., Chen, Z., Zhou, M., Liang, X., and Bai, Z. (2021). The impact of COVID-19 on crime: A spatial temporal analysis in Chicago. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030152"},{"key":"ref_23","first-page":"937","article-title":"Chicago crime analysis using r programming","volume":"5","author":"Monish","year":"2019","journal-title":"Int. J. Sci. Res. Comput. Sci. Eng. Inform. Technol."},{"key":"ref_24","first-page":"107","article-title":"Prediction of burglary crime based on LSTM","volume":"34","author":"Shen","year":"2019","journal-title":"J. Stat. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhuang, Y., Almeida, M., Morabito, M., and Ding, W. (2017, January 9\u201310). Crime hot spot forecasting: A recurrent model with spatial and temporal information. Proceedings of the 2017 IEEE International Conference on Big Knowledge (ICBK), Hefei, China.","DOI":"10.1109\/ICBK.2017.3"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., and Lin, D. (2018, January 2\u20137). Spatial temporal graph convolutional networks for skeleton-based action recognition. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gao, Z., Yang, R., Zhao, K., Yu, W., Liu, Z., and Liu, L. (2024). Hybrid convolutional neural network approaches for recognizing collaborative actions in human-robot assembly tasks. Sustainability, 16.","DOI":"10.3390\/su16010139"},{"key":"ref_28","first-page":"1448","article-title":"Spatio-temporal distribution prediction model of urban theft by fusing graph autoencoder and GRU","volume":"25","author":"Zhao","year":"2023","journal-title":"J. Geo-Inform. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"217222","DOI":"10.1109\/ACCESS.2020.3041924","article-title":"Risk prediction of theft crimes in urban communities: An integrated model of LSTM and ST-GCN","volume":"8","author":"Han","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","first-page":"13","article-title":"Exploring the correlation between temperature and crime: A case-crossover study of eight cities in America","volume":"5","author":"Hu","year":"2024","journal-title":"J. Saf. Sci. Resil."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s11416-024-00529-x","article-title":"Next gen cybersecurity paradigm towards artificial general intelligence: Russian market challenges and future global technological trends","volume":"20","author":"Pleshakova","year":"2024","journal-title":"J. Comput. Virol. Hack. Tech."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1504\/IJKBD.2024.137600","article-title":"Crime detection and crime hot spot prediction using the BI-LSTM deep learning model","volume":"14","author":"Sivakumaran","year":"2024","journal-title":"Int. J. Knowl.-Based Dev."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Fan, Q., and Xu, G. (2025). Real-time prediction model of public safety events driven by multi-source heterogeneous data. Front. Phys., 13.","DOI":"10.3389\/fphy.2025.1553640"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2020). Informer: Beyond efficient transformer for long sequence time-series forecasting. arXiv.","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Atero, F.J., Vinagre, J.J., Morgado, E., and Wilby, M.R. (2011, January 20\u201324). A low energy and adaptive architecture for efficient routing and robust mobility management in wireless sensor networks. Proceedings of the 2011 31st International Conference on Distributed Computing Systems Workshops, Minneapolis, MN, USA.","DOI":"10.1109\/ICDCSW.2011.39"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ya, M.A., Yaw, C., Koh, S., Tiong, S., Chen, C., Yusaf, T., Abdalla, A., Ali, K., and Raj, A. (2023). Detection of corona faults in switchgear by using 1D-CNN, LSTM, and 1D-CNN-LSTM methods. Sensors, 23.","DOI":"10.3390\/s23063108"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xu, H., Peng, Q., Wang, Y., and Zhan, Z. (2023). Power-load forecasting model based on informer and its application. Energies, 16.","DOI":"10.3390\/en16073086"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, Z., Liu, L., Li, N., and Tian, J. (2022). Time series forecasting of motor bearing vibration based on informer. Sensors, 22.","DOI":"10.3390\/s22155858"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jun, J., and Kim, H.K. (2023). Informer-based temperature prediction using observed and numerical weather prediction data. Sensors, 23.","DOI":"10.3390\/s23167047"},{"key":"ref_40","unstructured":"Davies, N.B., Krebs, J.R., and West, S.A. (2012). An Introduction to Behavioural Ecology, Wiley."},{"key":"ref_41","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.","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 11\u201314). Identity mappings in deep residual networks. Proceedings of the Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_38"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/7\/179\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:04:04Z","timestamp":1760033044000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/7\/179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,3]]},"references-count":42,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["bdcc9070179"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9070179","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,3]]}}}