{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T03:35:30Z","timestamp":1775187330493,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Director Fund of the International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2022DF016"],"award-info":[{"award-number":["CBAS2022DF016"]}]},{"name":"Director Fund of the International Research Center of Big Data for Sustainable Development Goals","award":["42071321"],"award-info":[{"award-number":["42071321"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["CBAS2022DF016"],"award-info":[{"award-number":["CBAS2022DF016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071321"],"award-info":[{"award-number":["42071321"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the challenges in data acquisition, especially for developing countries and at local levels, spatiotemporal evaluation for SDG11 indicators was still lacking. The availability of big data and earth observation technology can play an important role to facilitate the monitoring of urban sustainable development. Taking Guilin, a sustainable development agenda innovation demonstration area in China as a case study, we developed an assessment framework for SDG indicators 11.2.1, 11.3.1, and 11.7.1 at the neighborhood level using high-resolution (HR) satellite images, gridded population data, and other geospatial big data (e.g., road network and point of interest data). The findings showed that the proportion of the population with convenient access to public transport in the functional urban area gradually improved from 42% in 2013 to 52% in 2020. The increase in built-up land was much faster than the increase in population. The areal proportion of public open space decreased from 56% in 2013 to 24% in 2020, and the proportion of the population within the 400 m service areas of open public space decreased from 73% to 59%. The township-level results indicated that low-density land sprawling should be strictly managed, and open space and transportation facilities should be improved in the three fast-growing towns, Lingui, Lingchuan, and Dingjiang. The evaluation results of this study confirmed the applicability of SDG11 indicators to neighborhood-level assessment and local urban governance and planning practices. The evaluation framework of the SDG11 indicators based on HR satellite images and geospatial big data showed great promise to apply to other cities for targeted planning and assessment.<\/jats:p>","DOI":"10.3390\/rs14194985","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"4985","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Assessing Spatiotemporal Changes of SDG Indicators at the Neighborhood Level in Guilin, China: A Geospatial Big Data Approach"],"prefix":"10.3390","volume":"14","author":[{"given":"Liying","family":"Han","sequence":"first","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-1647-1950","authenticated-orcid":false,"given":"Linlin","family":"Lu","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Junyu","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Community Resources and Development, Arizona State University, Phoenix, AZ 85004, USA"}]},{"given":"Xintong","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-0357-5312","authenticated-orcid":false,"given":"Shuangcheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Ke","family":"Luo","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Dan","family":"He","sequence":"additional","affiliation":[{"name":"Urban Science Department, College of Applied Arts and Science, Beijing Union University, Beijing 100191, China"}]},{"given":"Penglong","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Huadong","family":"Guo","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-6322-8307","authenticated-orcid":false,"given":"Qingting","family":"Li","sequence":"additional","affiliation":[{"name":"Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"key":"ref_1","unstructured":"UN (2022, June 30). 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