Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China

Urban spatial structure reflects the organization of urban land use and is closely related to the travel patterns of residents. The characteristics of urban spatial structure include both static and dynamic aspects. The static characteristics of urban spatial structure reflect the morphological feat...

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Autores principales:	Ruixi Dong, Fengying Yan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	megacity spatial structure polycentricity commuting communities Tianjin Agriculture S
Acceso en línea:	https://doaj.org/article/7f6a26ac5c2e40678585f0a73e587ae6
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id	oai:doaj.org-article:7f6a26ac5c2e40678585f0a73e587ae6
record_format	dspace
spelling	oai:doaj.org-article:7f6a26ac5c2e40678585f0a73e587ae62021-11-25T18:09:14ZRevealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China10.3390/land101111442073-445Xhttps://doaj.org/article/7f6a26ac5c2e40678585f0a73e587ae62021-10-01T00:00:00Zhttps://www.mdpi.com/2073-445X/10/11/1144https://doaj.org/toc/2073-445XUrban spatial structure reflects the organization of urban land use and is closely related to the travel patterns of residents. The characteristics of urban spatial structure include both static and dynamic aspects. The static characteristics of urban spatial structure reflect the morphological features of space, and the dynamic characteristics of urban spatial structure reflect intra-city functional linkages. With the continuous agglomeration of population and industries; megacities have become the core spatial carriers leading China’s social and economic development; and their urban spatial structure has also been reconstructed. However; there is still a certain lack of understanding of the characteristics of the spatial structure of China’s megacities. This study aimed to reveal characteristics of the spatial structure of Chinese megacities at different scales using jobs-housing big data. To achieve this goal, spatial autocorrelation and a geographically weighted regression (GWR) model were applied to reveal static polycentricity, and community detection was used to reveal dynamic commuting communities. The distribution of jobs in urban space and jobs–housing balance levels in commuting communities were further analyzed. Experiments were conducted in Tianjin, China. We found that: (1) the static characteristics of the spatial structure of megacities presented the coexistence of polycentricity and a high degree of dispersion at macro- and meso-scales; (2) the dynamic characteristics of the spatial structure of megacities revealed two types of commuting communities at macro- and meso-scales and most commuting communities had a good jobs-housing balance. These findings can be referenced by urban managers and planners to formulate relevant policies for spatial distribution optimization of urban functions and transportation development at different spatial scales.Ruixi DongFengying YanMDPI AGarticlemegacityspatial structurepolycentricitycommuting communitiesTianjinAgricultureSENLand, Vol 10, Iss 1144, p 1144 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	megacity spatial structure polycentricity commuting communities Tianjin Agriculture S
spellingShingle	megacity spatial structure polycentricity commuting communities Tianjin Agriculture S Ruixi Dong Fengying Yan Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China
description	Urban spatial structure reflects the organization of urban land use and is closely related to the travel patterns of residents. The characteristics of urban spatial structure include both static and dynamic aspects. The static characteristics of urban spatial structure reflect the morphological features of space, and the dynamic characteristics of urban spatial structure reflect intra-city functional linkages. With the continuous agglomeration of population and industries; megacities have become the core spatial carriers leading China’s social and economic development; and their urban spatial structure has also been reconstructed. However; there is still a certain lack of understanding of the characteristics of the spatial structure of China’s megacities. This study aimed to reveal characteristics of the spatial structure of Chinese megacities at different scales using jobs-housing big data. To achieve this goal, spatial autocorrelation and a geographically weighted regression (GWR) model were applied to reveal static polycentricity, and community detection was used to reveal dynamic commuting communities. The distribution of jobs in urban space and jobs–housing balance levels in commuting communities were further analyzed. Experiments were conducted in Tianjin, China. We found that: (1) the static characteristics of the spatial structure of megacities presented the coexistence of polycentricity and a high degree of dispersion at macro- and meso-scales; (2) the dynamic characteristics of the spatial structure of megacities revealed two types of commuting communities at macro- and meso-scales and most commuting communities had a good jobs-housing balance. These findings can be referenced by urban managers and planners to formulate relevant policies for spatial distribution optimization of urban functions and transportation development at different spatial scales.
format	article
author	Ruixi Dong Fengying Yan
author_facet	Ruixi Dong Fengying Yan
author_sort	Ruixi Dong
title	Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China
title_short	Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China
title_full	Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China
title_fullStr	Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China
title_full_unstemmed	Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China
title_sort	revealing characteristics of the spatial structure of megacities at multiple scales with jobs-housing big data: a case study of tianjin, china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7f6a26ac5c2e40678585f0a73e587ae6
work_keys_str_mv	AT ruixidong revealingcharacteristicsofthespatialstructureofmegacitiesatmultiplescaleswithjobshousingbigdataacasestudyoftianjinchina AT fengyingyan revealingcharacteristicsofthespatialstructureofmegacitiesatmultiplescaleswithjobshousingbigdataacasestudyoftianjinchina
_version_	1718411568825237504

Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China

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