{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:16:44Z","timestamp":1760145404444,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T00:00:00Z","timestamp":1721606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Super Postdoctoral Funding Project","award":["2023045","K202301"],"award-info":[{"award-number":["2023045","K202301"]}]},{"name":"Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University","award":["2023045","K202301"],"award-info":[{"award-number":["2023045","K202301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Human mobility data are crucial for transportation planning and congestion management. However, challenges persist in accessing and using raw mobility data due to privacy concerns and data quality issues such as redundancy, missing values, and noise. This research introduces an innovative GIS-based framework for creating individual-level long-term spatio-temporal mobility data at a city scale. The methodology decomposes and represents individual mobility by identifying key locations where activities take place and life patterns that describe transitions between these locations. Then, we present methods for extracting, representing, and generating key locations and life patterns from large-scale human mobility data. Using long-term mobility data from Shanghai, we extract life patterns and key locations and successfully generate the mobility of 30,000 virtual users over seven days in Shanghai. The high correlation (R\u00b2 = 0.905) indicates a strong similarity between the generated data and ground-truth data. By testing the combination of key locations and life patterns from different areas, the model demonstrates strong transferability within and across cities, with relatively low RMSE values across all scenarios, the highest being around 0.04. By testing the representativeness of the generated mobility data, we find that using only about 0.25% of the generated individuals\u2019 mobility is sufficient to represent the dynamic changes of the entire urban population on a daily and hourly resolution. The proposed methodology offers a novel tool for generating long-term spatiotemporal mobility patterns at the individual level, thereby avoiding the privacy concerns associated with releasing real data. This approach supports the broad application of individual mobility data in urban planning, traffic management, and other related fields.<\/jats:p>","DOI":"10.3390\/ijgi13070261","type":"journal-article","created":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T12:20:38Z","timestamp":1721650838000},"page":"261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A GIS-Based Framework for Synthesizing City-Scale Long-Term Individual-Level Spatial\u2013Temporal Mobility"],"prefix":"10.3390","volume":"13","author":[{"given":"Yao","family":"Yao","sequence":"first","affiliation":[{"name":"Shanghai Urban Construction Design and Research Institute (Group) Co., Ltd., Shanghai 200125, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shanghai Urban Construction Design and Research Institute (Group) Co., Ltd., Shanghai 200125, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2513-2969","authenticated-orcid":false,"given":"Qing","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4641-0641","authenticated-orcid":false,"given":"Haoran","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102666","DOI":"10.1016\/j.trc.2020.102666","article-title":"From cellular positioning data to trajectories: Steps towards a more accurate mobility exploration","volume":"117","author":"Forghani","year":"2020","journal-title":"Transp. 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