{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T09:20:25Z","timestamp":1782379225843,"version":"3.54.5"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Indicator 11.3.1 of the UN Sustainable Development Goals (SDG 11.3.1) was designed to test land-use efficiency, which was defined as the ratio of the land consumption rate (LCR) to the population growth rate (PGR), namely, LCRPGR. This study calculates the PGRs, LCRs, and LCRPGRs for 333 cities from 1990\u20132000 and 391 cities from 2000\u20132015 in four geographical divisions in Eurasia according to the method given by UN metadata. The results indicate that Europe and Japan have the lowest PGR and LCR, indicating that this region\u2019s level of urbanization is the highest. South and Central Asia have the lowest values of LCRPGR, indicating relatively lower urban land supply during the measurement periods. Compared with the mean LCRPGR in a region, the average values from SDG 11.3.1 by different types of cities in a region can have more guiding significance for urban sustainable development. While paying attention to the urban land-use efficiency of mega and extra-large cities, more attention should be paid to the coordination relationship between urban land supply and population growth in large, medium, and small cities. Additionally, the method from UN metadata works well for most urban expansion cities but is not suitable for cities with small changes in urban populations.<\/jats:p>","DOI":"10.3390\/ijgi10020064","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T13:01:12Z","timestamp":1612270872000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Big Data Supported the Identification of Urban Land Efficiency in Eurasia by Indicator SDG 11.3.1"],"prefix":"10.3390","volume":"10","author":[{"given":"Chaopeng","family":"Li","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoyin","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyi","family":"Du","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"ref_1","unstructured":"(2014). 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