{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:27:36Z","timestamp":1740148056103,"version":"3.37.3"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"5","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62102110"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Foshan HKUST Projects","award":["FSUST21-FYTRI01A, FSUST21-FYTRI02A"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"\n Urban vibrancy describes the prosperity, diversity, and accessibility of urban areas, which is vital to a city\u2019s socio-economic development and sustainability. While many efforts have been made for statically measuring and evaluating urban vibrancy, there are few studies on the evolutionary process of urban vibrancy, yet we know little about the relationship between urban vibrancy evolution and sophisticated spatiotemporal dynamics. In this article, we make use of multi-sourced urban data to develop a data-driven framework,\n U-Evolve<\/jats:italic>\n , to investigate urban vibrancy evolution. Specifically, we first exploit the spatiotemporal characteristics of urban areas to create multi-view time-dependent graphs. Then, we analyze the contextual features and graph patterns of multi-view time-dependent graphs in terms of informing future urban vibrancy variations. Our analysis validates the informativeness of multi-view time-dependent graphs for characterizing and informing future urban vibrancy evolution. After that, we construct a feature based model to forecast future urban vibrancy evolution and quantify each feature\u2019s importance. Moreover, to further enhance the forecasting effectiveness, we propose a graph learning based model to capture spatiotemporal autocorrelation of urban areas based on multi-view time-dependent graphs in an end-to-end manner. Finally, extensive experiments on two metropolises, Beijing and Shanghai, demonstrate the effectiveness of our forecasting models. The\n U-Evolve<\/jats:italic>\n framework has also been deployed in the production environment to deliver real-world urban development and planning insights for various cities in China.\n <\/jats:p>","DOI":"10.1145\/3568683","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T13:09:15Z","timestamp":1669813755000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Characterizing and Forecasting Urban Vibrancy Evolution: A Multi-View Graph Mining Perspective"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4271-1567","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"first","affiliation":[{"name":"The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), and Guangzhou HKUST Fok Ying Tung Research Institute, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8509-3933","authenticated-orcid":false,"given":"Qingyu","family":"Guo","sequence":"additional","affiliation":[{"name":"The Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-643X","authenticated-orcid":false,"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Baidu Talent Intelligence Center, Baidu Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-8024","authenticated-orcid":false,"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, FL"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9170-7009","authenticated-orcid":false,"given":"Fuzhen","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Beihang University, and Xiamen Institute of Data Intelligence, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9847-7784","authenticated-orcid":false,"given":"Xiaojuan","family":"Ma","sequence":"additional","affiliation":[{"name":"The Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), and Guangzhou HKUST Fok Ying Tung Research Institute, Guangdong, China"}]}],"member":"320","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0252015"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/b97391"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/1132952.1132954"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2910591"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2771231"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2238531"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-0178-1"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_3_10_2","unstructured":"Junyoung Chung Caglar Gulcehre KyungHyun Cho and Yoshua Bengio. 2014. 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