{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T12:07:06Z","timestamp":1750162026876,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031402852"},{"type":"electronic","value":"9783031402869"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-40286-9_22","type":"book-chapter","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T23:02:27Z","timestamp":1691535747000},"page":"263-275","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spatial-Temporal Diffusion Probabilistic Learning for\u00a0Crime Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9621-5414","authenticated-orcid":false,"given":"Qiang","family":"Gao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9953-2076","authenticated-orcid":false,"given":"Hongzhu","family":"Fu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5035-6078","authenticated-orcid":false,"given":"Yutao","family":"Wei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0086-5461","authenticated-orcid":false,"given":"Li","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1357-3945","authenticated-orcid":false,"given":"Xingmin","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-0466","authenticated-orcid":false,"given":"Guisong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Shamsuddin, N.H.M., Ali, N.A., Alwee, R.: An overview on crime prediction methods. In: 2017 6th ICT International Student Project Conference (ICT-ISPC), pp. 1\u20135. IEEE (2017)","DOI":"10.1109\/ICT-ISPC.2017.8075335"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Huang, C., Zhang, J., Zheng, Y., Chawla, N.V.: DeepCrime: attentive hierarchical recurrent networks for crime prediction. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, October 2018","DOI":"10.1145\/3269206.3271793"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Zhao, X., Tang, J.: Modeling temporal-spatial correlations for crime prediction. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, November 2017","DOI":"10.1145\/3132847.3133024"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Chen, P., Yuan, H., Shu, X.: Forecasting crime using the ARIMA model. In: 2008 5th International Conference on Fuzzy Systems and Knowledge Discovery, vol. 5, pp, 627\u2013630 (2008)","DOI":"10.1109\/FSKD.2008.222"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Wang, H., Kifer, D., Graif, C., Li, Z.: Crime rate inference with big data. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016","DOI":"10.1145\/2939672.2939736"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Li, Z., Huang, C., Xia, L., Xu, Y., Pei, J.: Spatial-temporal hypergraph self-supervised learning for crime prediction. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 2984\u20132996. IEEE (2022)","DOI":"10.1109\/ICDE53745.2022.00269"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Xia, L., et al.: Spatial-temporal sequential hypergraph network for crime prediction with dynamic multiplex relation learning. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization (2021)","DOI":"10.24963\/ijcai.2021\/225"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Wei, H., et al.: PressLight: learning max pressure control to coordinate traffic signals in arterial network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, July 2019","DOI":"10.1145\/3292500.3330949"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Wu, X., Huang, C., Zhang, C., Chawla, N.V.: Hierarchically structured transformer networks for fine-grained spatial event forecasting. In: Proceedings of the Web Conference 2020, May 2020","DOI":"10.1145\/3366423.3380296"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Huang, C., Zhang, C., Dai, P., Bo, L.: Deep dynamic fusion network for traffic accident forecasting. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, November 2019","DOI":"10.1145\/3357384.3357829"},{"key":"22_CR11","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.inffus.2019.06.016","volume":"53","author":"J Liu","year":"2019","unstructured":"Liu, J., Li, T., Xie, P., Shengdong, D., Teng, F., Yang, X.: Urban big data fusion based on deep learning: an overview. Inf. Fus. 53, 123\u2013133 (2019)","journal-title":"Inf. Fus."},{"key":"22_CR12","unstructured":"Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the AAAI Conference on Artificial Intelligence, June 2022"},{"key":"22_CR13","unstructured":"Rose, Y., Li, Y., Shahabi, C., Demiryurek, U., Liu, Y.: A generic approach for extreme condition traffic forecasting. In: Proceedings of the 2017 SIAM International Conference on Data Mining (SDM) (2017)"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Huang, C., Zhang, C., Zhao, J., Wu, X., Yin, D., Chawla, N.: MiST: a multiview and multimodal spatial-temporal learning framework for citywide abnormal event forecasting. In: The World Wide Web Conference, May 2019","DOI":"10.1145\/3308558.3313730"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Yao, H., Tang, X., Wei, H., Zheng, G., Li, Z.: Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5668\u20135675, August 2019","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, July 2018","DOI":"10.24963\/ijcai.2018\/505"},{"key":"22_CR17","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000\u20136010 (2017)"},{"key":"22_CR18","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"key":"22_CR19","doi-asserted-by":"publisher","unstructured":"Croitoru, F.-A., Hondru, V., Ionescu, R.T., Shah, M.: Diffusion models in vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201320 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3261988","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Pan, B., Demiryurek, U., Shahabi, C.: Utilizing real-world transportation data for accurate traffic prediction. In: 2012 IEEE 12th International Conference on Data Mining, January 2013","DOI":"10.1109\/ICDM.2012.52"},{"key":"22_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"C-C Chang","year":"2012","unstructured":"Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1\u201327 (2012)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"22_CR22","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: 6th International Conference on Learning Representations, ICLR 2018, Conference Track Proceedings, Vancouver, BC, Canada, 30 April\u20133 May 2018. OpenReview.net (2018)"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-40286-9_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T23:12:08Z","timestamp":1691536328000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-40286-9_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031402852","9783031402869"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-40286-9_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ksem2023.conferences.academy\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"395","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"114","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}