The compactness of spatial structure in Chinese cities: measurement, clustering patterns and influencing factors

Rapid urbanization in China has led to an excessive urban expansion of built-up areas, which makes quantitative research on compact city important. We adopted density and the degree of mixed land use to measure the compactness of 160 Chinese cities. Spatial autocorrelation analysis was performed to...

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Autores principales:	Fangqi Zhao, Lina Tang, Quanyi Qiu, Gang Wu
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2020
Materias:	compact city urban spatial structure point of interest geographically weighted regression Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/8cd733883d6b45f9936a2036f6441108
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spelling	oai:doaj.org-article:8cd733883d6b45f9936a2036f64411082021-12-02T16:25:31ZThe compactness of spatial structure in Chinese cities: measurement, clustering patterns and influencing factors2332-887810.1080/20964129.2020.1743763https://doaj.org/article/8cd733883d6b45f9936a2036f64411082020-12-01T00:00:00Zhttp://dx.doi.org/10.1080/20964129.2020.1743763https://doaj.org/toc/2332-8878Rapid urbanization in China has led to an excessive urban expansion of built-up areas, which makes quantitative research on compact city important. We adopted density and the degree of mixed land use to measure the compactness of 160 Chinese cities. Spatial autocorrelation analysis was performed to identify spatial clustering patterns, and the relationships between compactness and five variables were explored through regression models. The result shows that in nearly half of the cases, the calculated values of two indices are less than the average. The high or low values of density and the degree of mixed land use tend to be spatially clustered. The hot spot regions of density and the degree of mixed land use lie mainly in the south of China, while the north present as cold spots or the insignificant regions. Urban compactness can be affected by multifaceted factors and the relationships between compactness and five variables are not consistent throughout the areas of analysis. The GWR model can identify this phenomenon and provides a better fit than the OLS model. This study proposed a new approach to measure the compactness, and the results of GWR analysis can conducive to appropriate policy-making based on different local conditions.Fangqi ZhaoLina TangQuanyi QiuGang WuTaylor & Francis Grouparticlecompact cityurban spatial structurepoint of interestgeographically weighted regressionEcologyQH540-549.5ENEcosystem Health and Sustainability, Vol 6, Iss 1 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	compact city urban spatial structure point of interest geographically weighted regression Ecology QH540-549.5
spellingShingle	compact city urban spatial structure point of interest geographically weighted regression Ecology QH540-549.5 Fangqi Zhao Lina Tang Quanyi Qiu Gang Wu The compactness of spatial structure in Chinese cities: measurement, clustering patterns and influencing factors
description	Rapid urbanization in China has led to an excessive urban expansion of built-up areas, which makes quantitative research on compact city important. We adopted density and the degree of mixed land use to measure the compactness of 160 Chinese cities. Spatial autocorrelation analysis was performed to identify spatial clustering patterns, and the relationships between compactness and five variables were explored through regression models. The result shows that in nearly half of the cases, the calculated values of two indices are less than the average. The high or low values of density and the degree of mixed land use tend to be spatially clustered. The hot spot regions of density and the degree of mixed land use lie mainly in the south of China, while the north present as cold spots or the insignificant regions. Urban compactness can be affected by multifaceted factors and the relationships between compactness and five variables are not consistent throughout the areas of analysis. The GWR model can identify this phenomenon and provides a better fit than the OLS model. This study proposed a new approach to measure the compactness, and the results of GWR analysis can conducive to appropriate policy-making based on different local conditions.
format	article
author	Fangqi Zhao Lina Tang Quanyi Qiu Gang Wu
author_facet	Fangqi Zhao Lina Tang Quanyi Qiu Gang Wu
author_sort	Fangqi Zhao
title	The compactness of spatial structure in Chinese cities: measurement, clustering patterns and influencing factors
title_short	The compactness of spatial structure in Chinese cities: measurement, clustering patterns and influencing factors
title_full	The compactness of spatial structure in Chinese cities: measurement, clustering patterns and influencing factors
title_fullStr	The compactness of spatial structure in Chinese cities: measurement, clustering patterns and influencing factors
title_full_unstemmed	The compactness of spatial structure in Chinese cities: measurement, clustering patterns and influencing factors
title_sort	compactness of spatial structure in chinese cities: measurement, clustering patterns and influencing factors
publisher	Taylor & Francis Group
publishDate	2020
url	https://doaj.org/article/8cd733883d6b45f9936a2036f6441108
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The compactness of spatial structure in Chinese cities: measurement, clustering patterns and influencing factors

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