{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T10:10:47Z","timestamp":1792059047955,"version":"4.4.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030676698","type":"print"},{"value":"9783030676704","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-67670-4_4","type":"book-chapter","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T17:04:13Z","timestamp":1614186253000},"page":"52-67","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["CrimeForecaster: Crime Prediction by Exploiting the Geographical Neighborhoods\u2019 Spatiotemporal Dependencies"],"prefix":"10.1007","author":[{"given":"Jiao","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxuan","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongyu","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luciano","family":"Nocera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriel","family":"Kahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cyrus","family":"Shahabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"4_CR1","unstructured":"Crime data of 2015 in The City of Chicago. https:\/\/data.cityofchicago.org\/Public-Safety\/Crimes-2015\/vwwp-7yr9"},{"key":"4_CR2","unstructured":"Boundary information of Chicago (2020). https:\/\/data.cityofchicago.org\/widgets\/bbvz-uum9"},{"key":"4_CR3","unstructured":"Census Bureau (2020). https:\/\/www.census.gov\/"},{"key":"4_CR4","unstructured":"Crosstown Los Angeles (2020). https:\/\/xtown.la\/"},{"key":"4_CR5","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th $$\\{$$USENIX$$\\}$$ Symposium on Operating Systems Design and Implementation ($$\\{$$OSDI$$\\}$$ 16), pp. 265\u2013283 (2016)"},{"issue":"3","key":"4_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang, C.C., Lin, C.J.: LibSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1\u201327 (2011)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"4_CR7","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"issue":"3","key":"4_CR8","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1109\/TPWRS.2002.804943","volume":"18","author":"J Contreras","year":"2003","unstructured":"Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J.: Arima models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 1014\u20131020 (2003)","journal-title":"IEEE Trans. Power Syst."},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Covington, P., Adams, J., Sargin, E.: Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191\u2013198 (2016)","DOI":"10.1145\/2959100.2959190"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Featherstone, C.: Identifying vehicle descriptions in microblogging text with the aim of reducing or predicting crime. In: 2013 International Conference on Adaptive Science and Technology, pp. 1\u20138. IEEE (2013)","DOI":"10.1109\/ICASTech.2013.6707494"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: Continual prediction with LSTM (1999)","DOI":"10.1049\/cp:19991218"},{"issue":"5","key":"4_CR12","doi-asserted-by":"publisher","first-page":"628","DOI":"10.15288\/jsa.2001.62.628","volume":"62","author":"DM Gorman","year":"2001","unstructured":"Gorman, D.M., Speer, P.W., Gruenewald, P.J., Labouvie, E.W.: Spatial dynamics of alcohol availability, neighborhood structure and violent crime. J. Stud. Alcohol 62(5), 628\u2013636 (2001)","journal-title":"J. Stud. Alcohol"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"4_CR14","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024\u20131034 (2017)"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"He, J., Qi, J., Ramamohanarao, K.: A joint context-aware embedding for trip recommendations. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 292\u2013303. IEEE (2019)","DOI":"10.1109\/ICDE.2019.00034"},{"key":"4_CR16","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387","volume-title":"Applied Logistic Regression","author":"DW Hosmer Jr","year":"2013","unstructured":"Hosmer Jr., D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, vol. 398. Wiley, Hoboken (2013)"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Huang, C., Zhang, C., Zhao, J., Wu, X., Chawla, N.V., Yin, D.: Mist: a multiview and multimodal spatial-temporal learning framework for citywide abnormal event forecasting. In: WWW 2019 (2019)","DOI":"10.1145\/3308558.3313730"},{"key":"4_CR18","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, pp. 717\u2013728 (2019)","DOI":"10.1145\/3308558.3313730"},{"key":"4_CR19","doi-asserted-by":"publisher","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, pp. 1423\u20131432, CIKM 2018, Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3269206.3271793","DOI":"10.1145\/3269206.3271793"},{"key":"4_CR20","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"4_CR21","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"4_CR22","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)"},{"key":"4_CR23","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: International Conference on Learning Representations (ICLR 2018) (2018)"},{"issue":"1","key":"4_CR24","doi-asserted-by":"publisher","first-page":"31","DOI":"10.2307\/2580317","volume":"76","author":"JD Morenoff","year":"1997","unstructured":"Morenoff, J.D., Sampson, R.J.: Violent crime and the spatial dynamics of neighborhood transition: Chicago, 1970\u20131990. Social Forces 76(1), 31\u201364 (1997)","journal-title":"Social Forces"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067\u20131077 (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"4_CR26","unstructured":"Vaswani, A., et al: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"4_CR27","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, pp. 635\u2013644 (2016)","DOI":"10.1145\/2939672.2939736"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Wang, J., Cong, G., Zhao, W.X., Li, X.: Mining user intents in Twitter: a semi-supervised approach to inferring intent categories for tweets. In: AAAI (2015)","DOI":"10.1609\/aaai.v29i1.9196"},{"key":"4_CR29","unstructured":"Zhao, L., et al: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. (2019)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67670-4_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T23:09:03Z","timestamp":1740352143000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67670-4_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030676698","9783030676704"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67670-4_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd2020.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"945","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":"195","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":"0","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":"21% - 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":"4,5","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":"4,4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}