{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T10:50:09Z","timestamp":1775127009589,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52408042"],"award-info":[{"award-number":["52408042"]}]},{"name":"National Natural Science Foundation of China","award":["BK20240931"],"award-info":[{"award-number":["BK20240931"]}]},{"name":"National Natural Science Foundation of China","award":["2024M750430"],"award-info":[{"award-number":["2024M750430"]}]},{"name":"National Natural Science Foundation of China","award":["2023SJYB2054"],"award-info":[{"award-number":["2023SJYB2054"]}]},{"name":"Jiangsu Natural Science Foundation","award":["52408042"],"award-info":[{"award-number":["52408042"]}]},{"name":"Jiangsu Natural Science Foundation","award":["BK20240931"],"award-info":[{"award-number":["BK20240931"]}]},{"name":"Jiangsu Natural Science Foundation","award":["2024M750430"],"award-info":[{"award-number":["2024M750430"]}]},{"name":"Jiangsu Natural Science Foundation","award":["2023SJYB2054"],"award-info":[{"award-number":["2023SJYB2054"]}]},{"name":"China Postdoctoral Science Funding","award":["52408042"],"award-info":[{"award-number":["52408042"]}]},{"name":"China Postdoctoral Science Funding","award":["BK20240931"],"award-info":[{"award-number":["BK20240931"]}]},{"name":"China Postdoctoral Science Funding","award":["2024M750430"],"award-info":[{"award-number":["2024M750430"]}]},{"name":"China Postdoctoral Science Funding","award":["2023SJYB2054"],"award-info":[{"award-number":["2023SJYB2054"]}]},{"name":"Jiangsu Provincial Department of Education Fund of Philosophy and Social Science","award":["52408042"],"award-info":[{"award-number":["52408042"]}]},{"name":"Jiangsu Provincial Department of Education Fund of Philosophy and Social Science","award":["BK20240931"],"award-info":[{"award-number":["BK20240931"]}]},{"name":"Jiangsu Provincial Department of Education Fund of Philosophy and Social Science","award":["2024M750430"],"award-info":[{"award-number":["2024M750430"]}]},{"name":"Jiangsu Provincial Department of Education Fund of Philosophy and Social Science","award":["2023SJYB2054"],"award-info":[{"award-number":["2023SJYB2054"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas.<\/jats:p>","DOI":"10.3390\/ijgi13100371","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T08:53:11Z","timestamp":1729500791000},"page":"371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Prediction of Commercial Street Location Based on Point of Interest (POI) Big Data and Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Linghan","family":"Yao","sequence":"first","affiliation":[{"name":"School of Architecture, South China University of Technology, Guangzhou 510641, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3158-397X","authenticated-orcid":false,"given":"Chao","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Humanities, Chang\u2019an University, Xi\u2019an 710061, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0908-8710","authenticated-orcid":false,"given":"Yanqing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Architectural Science and Engineering, Yangzhou University, Yangzhou 225009, China"},{"name":"School of Architecture, Southeast University, Nanjing 210096, China"}]},{"given":"Xinyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Bartlett School of Architecture, University College London, London WC1E 6BT, UK"}]},{"given":"Xiaoyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Suzhou 215000, China"}]},{"given":"Yequan","family":"Hu","sequence":"additional","affiliation":[{"name":"Bartlett School of Architecture, University College London, London WC1E 6BT, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, L., Yan, J., Zhu, Y., Deng, J., Chen, L., and Lu, S. 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