{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T03:13:37Z","timestamp":1774926817614,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":65,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62322601;"],"award-info":[{"award-number":["62322601;"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671639","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"6037-6048","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Weather Knows What Will Occur: Urban Public Nuisance Events Prediction and Control with Meteorological Assistance"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1773-7968","authenticated-orcid":false,"given":"Yi","family":"Xie","sequence":"first","affiliation":[{"name":"Shanghai Key Lab of Data Science, School of Computer Science, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5106-711X","authenticated-orcid":false,"given":"Tianyu","family":"Qiu","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Data Science, School of Computer Science, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8575-5415","authenticated-orcid":false,"given":"Yun","family":"Xiong","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Data Science, School of Computer Science, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4443-1555","authenticated-orcid":false,"given":"Xiuqi","family":"Huang","sequence":"additional","affiliation":[{"name":"MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1776-8799","authenticated-orcid":false,"given":"Xiaofeng","family":"Gao","sequence":"additional","affiliation":[{"name":"MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2094-9734","authenticated-orcid":false,"given":"Chao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3462-677X","authenticated-orcid":false,"given":"Qiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Meteorological Disaster Prevention Centre, Shanghai Meteorological Bureau, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9862-6877","authenticated-orcid":false,"given":"Haihong","family":"Li","sequence":"additional","affiliation":[{"name":"Meteorological Disaster Prevention Centre, Shanghai Meteorological Bureau, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"A. Ahlbom and M. Feychting. 2003. Electromagnetic radiation: environmental pollution and health. British medical bulletin Vol. 68 1 (2003) 157--165.","DOI":"10.1093\/bmb\/ldg030"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"F. Ahouz and A. Golabpour. 2021. Predicting the incidence of COVID-19 using data mining. BMC public health Vol. 21 (2021) 1--12.","DOI":"10.1186\/s12889-021-11058-3"},{"key":"e_1_3_2_2_3_1","article-title":"Assessing long-term fire risk at local scale by means of decision tree technique","volume":"111","author":"Amatulli Giuseppe","year":"2006","unstructured":"Giuseppe Amatulli, Maria Jo ao Rodrigues, Marco Trombetti, and Raffaella Lovreglio. 2006. Assessing long-term fire risk at local scale by means of decision tree technique. Journal of Geophysical Research: Biogeosciences, Vol. 111, G4 (2006).","journal-title":"Journal of Geophysical Research: Biogeosciences"},{"key":"e_1_3_2_2_4_1","volume-title":"Social stress: Theory and research. Annual review of sociology","author":"Aneshensel Carol S","year":"1992","unstructured":"Carol S Aneshensel. 1992. Social stress: Theory and research. Annual review of sociology, Vol. 18, 1 (1992), 15--38."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"T. Baltru?aitis C. Ahuja and L. P. Morency. 2018. Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence Vol. 41 2 (2018) 423--443.","DOI":"10.1109\/TPAMI.2018.2798607"},{"key":"e_1_3_2_2_6_1","volume-title":"Nature","volume":"525","author":"Bauer P.","year":"2015","unstructured":"P. Bauer, A. Thorpe, and G. Brunet. 2015. The quiet revolution of numerical weather prediction. Nature, Vol. 525, 7567 (2015), 47--55."},{"key":"e_1_3_2_2_7_1","first-page":"1","article-title":"Mathematical modeling and epidemic prediction of COVID-19 and its significance to epidemic prevention and control measures","volume":"1","author":"Cao Jinming","year":"2020","unstructured":"Jinming Cao, Xia Jiang, Bin Zhao, et al. 2020. Mathematical modeling and epidemic prediction of COVID-19 and its significance to epidemic prevention and control measures. Journal of Biomedical Research & Innovation, Vol. 1, 1 (2020), 1--19.","journal-title":"Journal of Biomedical Research & Innovation"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.janxdis.2020.102263"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2542-5196(20)30144-3"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"A. Cunsolo Willox S. L. Harper J. D. Ford K. Landman K. Houle and V. L. Edge. 2012. ?From this place and of this place:\" climate change sense of place and health in Nunatsiavut Canada. Social science & medicine Vol. 75 3 (2012) 538--547.","DOI":"10.1016\/j.socscimed.2012.03.043"},{"key":"e_1_3_2_2_11_1","volume-title":"Regulation and Social Control of Incivilities","author":"Ronco A. Di","unstructured":"A. Di Ronco. 2016. Understanding uncivil behaviour through urban space and culture. In Regulation and Social Control of Incivilities. Routledge, 108--124."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939875"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"J. R. Edwards and C. L. Cooper. 1988. The impacts of positive psychological states on physical health: A review and theoretical framework. Social science & medicine Vol. 27 12 (1988) 1447--1459.","DOI":"10.1016\/0277-9536(88)90212-2"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2804277"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063212.2063226"},{"key":"e_1_3_2_2_16_1","volume-title":"Proceedings of the ACM Web Conference","author":"Gao J.","year":"2022","unstructured":"J. Gao, C. Xiao, L. M. Glass, and J. Sun. 2022. PopNet: Real-Time Population-Level Disease Prediction with Data Latency. In Proceedings of the ACM Web Conference 2022. 2552--2562."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"L. A. Gottschalk and G. C. Gleser. 1969. The measurement of psychological states through the content analysis of verbal behavior. Univ of California Press.","DOI":"10.1525\/9780520376762"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2017.12.018"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0001-4575(02)00005-2"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2916887"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/58.1.83"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00536900"},{"key":"e_1_3_2_2_23_1","volume-title":"Proceedings of the 27th ACM international conference on information and knowledge management. 1423--1432","author":"Huang C.","unstructured":"C. Huang, J. Zhang, Y. Zheng, and N. V. Chawla. 2018. DeepCrime: Attentive hierarchical recurrent networks for crime prediction. In Proceedings of the 27th ACM international conference on information and knowledge management. 1423--1432."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3478099","article-title":"Multi-Head Spatio-Temporal Attention Mechanism for Urban Anomaly Event Prediction","volume":"5","author":"Huang H.","year":"2021","unstructured":"H. Huang, X. Yang, and S. He. 2021. Multi-Head Spatio-Temporal Attention Mechanism for Urban Anomaly Event Prediction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 5, 3 (2021), 1--21.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_2_25_1","volume-title":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2333--2338","author":"Huang P. S.","unstructured":"P. S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2333--2338."},{"key":"e_1_3_2_2_26_1","volume-title":"Proceedings of the 13th ACM Conference on Recommender Systems. 169--177","author":"Huang T.","unstructured":"T. Huang, Z. Zhang, and J. Zhang. 2019. FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction. In Proceedings of the 13th ACM Conference on Recommender Systems. 169--177."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.17485\/ijst\/2013\/v6i3.6"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"V. Isham and M. Westcott. 1979. A self-correcting point process. Stochastic processes and their applications Vol. 8 3 (1979) 335--347.","DOI":"10.1016\/0304-4149(79)90008-5"},{"key":"e_1_3_2_2_29_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems","author":"Ke G.","year":"2017","unstructured":"G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, et al. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_30_1","unstructured":"T. N. Kipf and M. Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm9030789"},{"key":"e_1_3_2_2_32_1","volume-title":"Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2984--2996","author":"Li Z.","unstructured":"Z. Li, C. Huang, L. Xia, Y. Xu, and J. Pei. 2022. Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2984--2996."},{"key":"e_1_3_2_2_33_1","unstructured":"Thomas Josef Liniger. 2009. Multivariate hawkes processes. Ph. D. Dissertation. ETH Zurich."},{"key":"e_1_3_2_2_34_1","volume-title":"Forecasting hotspots-A predictive analytics approach","author":"Maciejewski Ross","year":"2010","unstructured":"Ross Maciejewski, Ryan Hafen, Stephen Rudolph, Stephen G Larew, Michael A Mitchell, William S Cleveland, and David S Ebert. 2010. Forecasting hotspots-A predictive analytics approach. IEEE transactions on visualization and computer graphics, Vol. 17, 4 (2010), 440--453."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1081\/CBI-120019310"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcan.2017.10.002"},{"key":"e_1_3_2_2_37_1","unstructured":"H. Mei and J. M. Eisner. 2017. The neural hawkes process: A neurally self-modulating multivariate point process. In Advances in neural information processing systems Vol. 30."},{"key":"e_1_3_2_2_38_1","volume-title":"The neural hawkes process: A neurally self-modulating multivariate point process. Advances in neural information processing systems","author":"Mei Hongyuan","year":"2017","unstructured":"Hongyuan Mei and Jason M Eisner. 2017. The neural hawkes process: A neurally self-modulating multivariate point process. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1093\/bjc\/azm076"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2005.10.004"},{"key":"e_1_3_2_2_41_1","unstructured":"Takahiro Omi Kazuyuki Aihara et al. 2019. Fully neural network based model for general temporal point processes. Advances in neural information processing systems Vol. 32 (2019)."},{"key":"e_1_3_2_2_42_1","volume-title":"Intensitatsschwankungen im fernsprechverker. Ericsson technics","author":"Palm Conny","year":"1943","unstructured":"Conny Palm. 1943. Intensitatsschwankungen im fernsprechverker. Ericsson technics (1943)."},{"key":"e_1_3_2_2_43_1","volume-title":"The Lancet","volume":"352","author":"Partonen Timo","year":"1998","unstructured":"Timo Partonen and Jouko L\u00f6nnqvist. 1998. Seasonal affective disorder. The Lancet, Vol. 352, 9137 (1998), 1369--1374."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0034487"},{"key":"e_1_3_2_2_45_1","unstructured":"R. Pollitzer. 1954. Plague. WHO Geneva. 409--482 pages."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"crossref","unstructured":"C. P. Robert G. Casella and G. Casella. 1999. Monte Carlo statistical methods. Vol. 2. Springer.","DOI":"10.1007\/978-1-4757-3071-5"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12553-021-00553-7"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0008197300108347"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1080\/00224545.1994.9923010"},{"key":"e_1_3_2_2_50_1","unstructured":"John C Turner. 2010. Social categorization and the self-concept: a social cognitive theory of group behavior. (2010)."},{"key":"e_1_3_2_2_51_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence","volume":"35","author":"Wang B.","unstructured":"B. Wang, Y. Lin, S. Guo, and H. Wan. 2021. GSNet: learning spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 4402--4409."},{"key":"e_1_3_2_2_52_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"36","author":"Wang C.","unstructured":"C. Wang, Z. Lin, X. Yang, J. Sun, M. Yue, and C. Shahabi. 2022. Hagen: Homophily-aware graph convolutional recurrent network for crime forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 4193--4200."},{"key":"e_1_3_2_2_53_1","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu H.","year":"2021","unstructured":"H. Wu, J. Xu, J. Wang, and M. Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems, Vol. 34 (2021), 22419--22430.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10724"},{"key":"e_1_3_2_2_55_1","unstructured":"C. Yang H. Mei and J. Eisner. 2021. Transformer embeddings of irregularly spaced events and their participants. arXiv preprint arXiv:2201.00044 (2021)."},{"key":"e_1_3_2_2_56_1","volume-title":"Transformer embeddings of irregularly spaced events and their participants. arXiv preprint arXiv:2201.00044","author":"Yang Chenghao","year":"2021","unstructured":"Chenghao Yang, Hongyuan Mei, and Jason Eisner. 2021. Transformer embeddings of irregularly spaced events and their participants. arXiv preprint arXiv:2201.00044 (2021)."},{"key":"e_1_3_2_2_57_1","first-page":"424","article-title":"A study into on-street parking: Effects on traffic congestion","volume":"40","author":"Yousif S.","year":"1999","unstructured":"S. Yousif. 1999. A study into on-street parking: Effects on traffic congestion. Traffic Engineering and Control, Vol. 40 (1999), 424--427.","journal-title":"Traffic Engineering and Control"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.2991008"},{"key":"e_1_3_2_2_60_1","volume-title":"International conference on machine learning. PMLR, 11183--11193","author":"Zhang Q.","unstructured":"Q. Zhang, A. Lipani, O. Kirnap, and E. Yilmaz. 2020. Self-attentive Hawkes process. In International conference on machine learning. PMLR, 11183--11193."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3133024"},{"key":"e_1_3_2_2_62_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence","volume":"35","author":"Zhou H.","unstructured":"H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106--11115."},{"key":"e_1_3_2_2_63_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence","volume":"34","author":"Zhou Z.","unstructured":"Z. Zhou, Y. Wang, X. Xie, L. Chen, and H. Liu. 2020. RiskOracle: a minute-level citywide traffic accident forecasting framework. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 1258--1265."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3034312"},{"key":"e_1_3_2_2_65_1","volume-title":"International conference on machine learning. PMLR, 11692--11702","author":"Zuo S.","unstructured":"S. Zuo, H. Jiang, Z. Li, T. Zhao, and H. Zha. 2020. Transformer hawkes process. In International conference on machine learning. PMLR, 11692--11702."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671639","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671639","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:05:59Z","timestamp":1750291559000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":65,"alternative-id":["10.1145\/3637528.3671639","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671639","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}