{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T00:00:19Z","timestamp":1762041619757,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T00:00:00Z","timestamp":1634256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801147"],"award-info":[{"award-number":["41801147"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Despite the trending studies on smart city development, how to evaluate the smartness of a city remains unclear. This research aimed to design a smart city evaluation system, named the City Intelligence Quotient (CityIQ) evaluation system, which considers both the hard (e.g., physical infrastructure) and soft sides (e.g., citizens\u2019 perspectives) of smart city development. Based on the two-level structure of the CityIQ evaluation system (i.e., five dimensions and twenty indicators), a list of keywords was defined for automated information scraping in leading social media platforms to obtain volunteered geographic information. Semantic analysis was then used to update the CityIQ evaluations in a timely manner. Fifteen major cities in the Yangtze River Delta region, China, were selected for the empirical study, in which their smartness indices were calculated, traced and compared. Finally, suggestions for collaborative smart agglomerations were put forward. With the CityIQ evaluation system, policy makers can be informed of up-to-date changes in urban smartness levels and, thus, design context-specific collaborative policies to promote smart agglomerations.<\/jats:p>","DOI":"10.3390\/ijgi10100702","type":"journal-article","created":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T13:35:47Z","timestamp":1634304947000},"page":"702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["City Intelligence Quotient Evaluation System Using Crowdsourced Social Media Data: A Case Study of the Yangtze River Delta Region, China"],"prefix":"10.3390","volume":"10","author":[{"given":"Zhiqiang","family":"Wu","sequence":"first","affiliation":[{"name":"College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China"},{"name":"Key Laboratory of Ecology and Energy\u2014Saving Study of Dense Habitat, Ministry of Education, Tongji University, Shanghai 200092, China"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Design and Architecture, Zhejiang University of Technology, Hangzhou 310014, China"}]},{"given":"Xingang","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China"},{"name":"Key Laboratory of Ecology and Energy\u2014Saving Study of Dense Habitat, Ministry of Education, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3547-5853","authenticated-orcid":false,"given":"Tianren","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China"}]},{"given":"Ruiyao","family":"Lu","sequence":"additional","affiliation":[{"name":"The Bartlett School of Architecture, University College London, London WC1H 0QB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1140\/epjst\/e2012-01703-3","article-title":"Smart cities of the future","volume":"214","author":"Batty","year":"2012","journal-title":"Eur. 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