{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T01:18:57Z","timestamp":1755998337479,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":11,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,2]]},"DOI":"10.1145\/3474717.3483945","type":"proceedings-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T22:59:49Z","timestamp":1636066789000},"page":"207-210","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["CityOutlook"],"prefix":"10.1145","author":[{"given":"Soto","family":"Anno","sequence":"first","affiliation":[{"name":"Tokyo Institute of Technology, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kota","family":"Tsubouchi","sequence":"additional","affiliation":[{"name":"Yahoo Japan Corporation Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masamichi","family":"Shimosaka","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Fitting autoregressive models for prediction. Annals of the institute of Statistical Mathematics","author":"Akaike Hirotugu","year":"1969","unstructured":"Hirotugu Akaike . 1969. Fitting autoregressive models for prediction. Annals of the institute of Statistical Mathematics ( 1969 ). Hirotugu Akaike. 1969. Fitting autoregressive models for prediction. Annals of the institute of Statistical Mathematics (1969)."},{"volume-title":"Proc. of SIGSPATIAL.","author":"Soto","key":"e_1_3_2_1_2_1","unstructured":"Soto Anno et al. 2020. Supervised-CityProphet: Towards Accurate Anomalous Crowd Prediction . In Proc. of SIGSPATIAL. Soto Anno et al. 2020. Supervised-CityProphet: Towards Accurate Anomalous Crowd Prediction. In Proc. of SIGSPATIAL."},{"key":"e_1_3_2_1_3_1","volume-title":"A tutorial on kernel density estimation and recent advances. Biostatistics & Epidemiology","author":"Chen Yen-Chi","year":"2017","unstructured":"Yen-Chi Chen . 2017. A tutorial on kernel density estimation and recent advances. Biostatistics & Epidemiology ( 2017 ). Yen-Chi Chen. 2017. A tutorial on kernel density estimation and recent advances. Biostatistics & Epidemiology (2017)."},{"key":"e_1_3_2_1_4_1","volume-title":"Proc. IJCAI.","author":"Elkan Charles","year":"2001","unstructured":"Charles Elkan . 2001 . The Foundations of Cost-Sensitive Learning . In Proc. IJCAI. Charles Elkan. 2001. The Foundations of Cost-Sensitive Learning. In Proc. IJCAI."},{"volume-title":"Proc. of SIGKDD.","author":"Renhe","key":"e_1_3_2_1_5_1","unstructured":"Renhe Jiang et al. 2019. DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events . In Proc. of SIGKDD. Renhe Jiang et al. 2019. DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events. In Proc. of SIGKDD."},{"volume-title":"Proc. of UbiComp.","author":"Tatsuya","key":"e_1_3_2_1_6_1","unstructured":"Tatsuya Konishi et al. 2016. CityProphet: City-Scale Irregularity Prediction Using Transit App Logs . In Proc. of UbiComp. Tatsuya Konishi et al. 2016. CityProphet: City-Scale Irregularity Prediction Using Transit App Logs. In Proc. of UbiComp."},{"key":"e_1_3_2_1_7_1","volume-title":"Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of statistical planning and inference","author":"Shimodaira Hidetoshi","year":"2000","unstructured":"Hidetoshi Shimodaira . 2000. Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of statistical planning and inference ( 2000 ). Hidetoshi Shimodaira. 2000. Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of statistical planning and inference (2000)."},{"volume-title":"Proc. of UbiComp.","author":"Masamichi","key":"e_1_3_2_1_8_1","unstructured":"Masamichi Shimosaka et al. 2015. Forecasting Urban Dynamics with Mobility Logs by Bilinear Poisson Regression . In Proc. of UbiComp. Masamichi Shimosaka et al. 2015. Forecasting Urban Dynamics with Mobility Logs by Bilinear Poisson Regression. In Proc. of UbiComp."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3314417"},{"key":"e_1_3_2_1_10_1","unstructured":"Makoto Yamada et al. 2011. Relative Density-Ratio Estimation for Robust Distribution Comparison. In Advances in NIPS.  Makoto Yamada et al. 2011. Relative Density-Ratio Estimation for Robust Distribution Comparison. In Advances in NIPS."},{"key":"e_1_3_2_1_11_1","volume-title":"Urban Computing: Concepts, Methodologies, and Applications. ACM Trans. on Intelligent Systems and Technology","author":"Yu Zheng","year":"2014","unstructured":"Yu Zheng et al. 2014 . Urban Computing: Concepts, Methodologies, and Applications. ACM Trans. on Intelligent Systems and Technology (2014). Yu Zheng et al. 2014. Urban Computing: Concepts, Methodologies, and Applications. ACM Trans. on Intelligent Systems and Technology (2014)."}],"event":{"name":"SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"],"location":"Beijing China","acronym":"SIGSPATIAL '21"},"container-title":["Proceedings of the 29th International Conference on Advances in Geographic Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474717.3483945","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474717.3483945","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:43Z","timestamp":1750191523000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474717.3483945"}},"subtitle":["Early Crowd Dynamics Forecast towards Irregular Events Detection with Synthetically Unbiased Regression"],"short-title":[],"issued":{"date-parts":[[2021,11,2]]},"references-count":11,"alternative-id":["10.1145\/3474717.3483945","10.1145\/3474717"],"URL":"https:\/\/doi.org\/10.1145\/3474717.3483945","relation":{},"subject":[],"published":{"date-parts":[[2021,11,2]]},"assertion":[{"value":"2021-11-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}