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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>\n            The proliferation of multimodal big data in cities provides unprecedented opportunities for modeling and forecasting urban problems, such as crime prediction and house price prediction, through data-driven approaches. A fundamental and critical issue in modeling and forecasting urban problems lies in identifying suitable spatial analysis units, also known as city region partition. Existing works rely on subjective domain knowledge for static partitions, which is general and universal for all tasks. In fact, different tasks may need different city region partitions. To address this issue, we propose\n            <jats:italic>JLPR<\/jats:italic>\n            , a task-oriented framework for\n            <jats:italic>J<\/jats:italic>\n            oint\n            <jats:italic>L<\/jats:italic>\n            earning of region\n            <jats:italic>P<\/jats:italic>\n            artition and\n            <jats:italic>R<\/jats:italic>\n            epresentation. To make partitions fit tasks, JLPR integrates the region partition into the representation model training and learns region partitions using the supervision signal from the downstream task. We evaluate the framework on two prediction tasks (i.e., crime prediction and housing price prediction) in Chicago. Experiments show that JLPR consistently outperforms state-of-the-art partitioning methods in both tasks, which achieves above 25% and 70% performance improvements in terms of mean absolute error for crime prediction and house price prediction tasks, respectively. Additionally, we meticulously undertake three visualization case studies, which yield profound and illuminating findings from diverse perspectives, demonstrating the remarkable effectiveness and superiority of our approach.\n          <\/jats:p>","DOI":"10.1145\/3652857","type":"journal-article","created":{"date-parts":[[2024,3,17]],"date-time":"2024-03-17T07:44:05Z","timestamp":1710661445000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Framework for Joint Learning of City Region Partition and Representation"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7687-2162","authenticated-orcid":false,"given":"Mingyu","family":"Deng","sequence":"first","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3952-0826","authenticated-orcid":false,"given":"Wanyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0039-6220","authenticated-orcid":false,"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2368-8947","authenticated-orcid":false,"given":"Zhu","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1874-3641","authenticated-orcid":false,"given":"Mingliang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5122-6806","authenticated-orcid":false,"given":"Jun","family":"Luo","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, 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":"Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"issue":"1","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1038\/s41746-021-00511-7","article-title":"A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan","volume":"4","author":"Ar\u0131k Sercan \u00d6","year":"2021","unstructured":"Sercan \u00d6 Ar\u0131k, Joel Shor, Rajarishi Sinha, Jinsung Yoon, Joseph R. 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