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However, the complexity and diversity of urban patch distribution make it difficult to achieve a global rapid assessment. Based on the self-developed high-resolution global impervious surface area 2021 (Hi-GISA 2021) product, this study combined the complex network with remote sensing technology to propose a new method to delineate and evaluate the pattern and inner spatial links of the urban\u2013rural continuum for five typical urban agglomerations in China, including the Beijing\u2013Tianjin\u2013Hebei urban agglomeration (BTHUA), the Yangtze River Delta urban agglomeration (YRDUA), the Greater Bay Area (GBAUA), the Chengdu\u2013Chongqing urban agglomeration (CYUA), and the Middle Reaches of Yangtze River urban agglomeration (MRYRUA). The research results are in good agreement with Chinese government documents. First, the five urban agglomerations are all small-world networks with a low degree of overall polycentricity, and the urbanization degrees of GBAUA and YRDUA are higher than BTHUA, CYUA, and MRYRUA. Second, the imbalanced development of YRDUA is higher than the other regions, and the siphon effects of BTHUA and MRYRUA are more significant than YRDUA, CYUA, and GBAUA. Third, some multi-centers show significant siphon effects. The urbanization degree is highly correlated with the urbanization potential but not positively correlated with the degree of balanced development. The results can provide data, methods, and technical support for monitoring and evaluating SDG11.A.<\/jats:p>","DOI":"10.3390\/rs15040920","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T05:37:31Z","timestamp":1675834651000},"page":"920","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Patterns of Typical Chinese Urban Agglomerations Based on Complex Spatial Network Analysis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4543-0103","authenticated-orcid":false,"given":"Sijia","family":"Li","sequence":"first","affiliation":[{"name":"College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals (CBAS), Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0337-1862","authenticated-orcid":false,"given":"Huadong","family":"Guo","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals (CBAS), Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3219-0542","authenticated-orcid":false,"given":"Zhongchang","family":"Sun","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals (CBAS), Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"Key Laboratory for Earth Observation of Hainan Province, Hainan Research Institute, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Space-Ground Integrated Information Technology, Beijing Institute of Satellite Information Engineering, Beijing 100194, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9539-1662","authenticated-orcid":false,"given":"Huiping","family":"Jiang","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals (CBAS), Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6135-9442","authenticated-orcid":false,"given":"Hongsheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Hong Kong, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1016\/j.scib.2021.01.012","article-title":"Big Earth Data: A practice of sustainability science to achieve the Sustainable Development Goals","volume":"66","author":"Guo","year":"2021","journal-title":"Sci. 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