{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T06:04:20Z","timestamp":1781849060129,"version":"3.54.5"},"reference-count":61,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,3,28]],"date-time":"2020-03-28T00:00:00Z","timestamp":1585353600000},"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":["31971639"],"award-info":[{"award-number":["31971639"]}],"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":["41901221"],"award-info":[{"award-number":["41901221"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>An in-depth analysis of urban road network distribution plays a critical role in understanding the urbanization process. However, effective ways to quantitatively analyze the spatial paradigms of road networks are still lacking, and few studies have utilized road networks to rapidly identify urban areas of a region. Thus, using a fast-developing region in the south-eastern costal region of China, Fuzhou City, as a case, we introduced kernel density estimation (KDE) to characterize road networks and quantified the area\u2019s spatial heterogeneity using exploratory spatial data analysis (ESDA) and semivariance analysis (SA). The results show that there is an uneven spatial distribution of the networks both at the regional and downtown levels. At the regional level, there is a conspicuous polarization in the road distribution, with the KDE being much higher in the urban areas than in the rural areas; at the downtown level, the KDE gradually decreases from the center to the periphery. Quantitatively, the ranges of the spatial dependence of the networks are approximately 25 km for the entire study region and 12 km for the downtown area. Additionally, the spatial variations vary among different directions, with greater variations in the northeast\u2013southwest and the southeast\u2013northwest directions compared with the other directions, which is in line with the urban sprawl policy of the study area. Both the qualitative and quantitative results show that the distribution of road networks has a clear urban\u2013rural dual structure, which indicates that road networks can be an active tool in identifying the urban areas of a region. To this end, we propose a quick and easy method to delimit urban areas using KDE. The extraction results of KDE are better than those of the index-based built-up index (IBI), indicating the effectivity and feasibility of our proposed method to identify the urban areas in the region. This research sheds new light on urbanization development research.<\/jats:p>","DOI":"10.3390\/ijgi9040204","type":"journal-article","created":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T13:27:19Z","timestamp":1585661239000},"page":"204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Spatial Paradigms in Road Networks and Their Delimitation of Urban Boundaries Based on KDE"],"prefix":"10.3390","volume":"9","author":[{"given":"Yuying","family":"Lin","sequence":"first","affiliation":[{"name":"College of Tourism, Fujian Normal University, Fuzhou 350117, China"},{"name":"College of Geographical Science, Fujian Normal University, Fuzhou 350117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xisheng","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingshui","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Tourism, Fujian Normal University, Fuzhou 350117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongzu","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinguo","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Material Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoyin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geographical Science, Fujian Normal University, Fuzhou 350117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6312","DOI":"10.1073\/pnas.1011615108","article-title":"Urban growth, climate change, and freshwater availability","volume":"108","author":"Mcdonald","year":"2011","journal-title":"Proc. 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