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Article

Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China

by 1, 1,* and 1,2,*
1
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(11), 425; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110425
Received: 18 September 2018 / Revised: 20 October 2018 / Accepted: 27 October 2018 / Published: 31 October 2018
Urban land use and transportation are closely associated. Previous studies have investigated the spatial interrelationship between street centralities and land use intensities using land cover data, thus neglecting the social functions of urban land. Taking the city of Shenzhen, China, as a case study, we used reclassified points of interest (POI) data to represent commercial, public service, and residential land, and then investigated the varying interrelationships between the street centralities and different types of urban land use intensities. We calculated three global centralities (“closeness”, “betweenness”, and “straightness”) as well as local centralities (1-km, 2-km, 3-km, and 5-km searching radiuses), which were transformed into raster frameworks using kernel density estimation (KDE) for correlation analysis. Global closeness and straightness are high in the urban core area, and roads with high global betweenness outline the skeleton of the street network. The spatial patterns of the local centralities are distinguished from the global centralities, reflecting local location advantages. High intensities of commercial and public service land are concentrated in the urban core, while residential land is relatively scattered. The bivariate correlation analysis implies that commercial and public service land are more dependent on centralities than residential land. Closeness and straightness have stronger abilities in measuring the location advantages than betweenness. The centralities and intensities are more positively correlated on a larger scale (census block). These findings of the spatial patterns and interrelationships of the centralities and intensities have major implications for urban land use and transportation planning. View Full-Text
Keywords: street network; land use intensity; street centralities; POI; complex network street network; land use intensity; street centralities; POI; complex network
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MDPI and ACS Style

Wang, S.; Xu, G.; Guo, Q. Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China. ISPRS Int. J. Geo-Inf. 2018, 7, 425. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110425

AMA Style

Wang S, Xu G, Guo Q. Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China. ISPRS International Journal of Geo-Information. 2018; 7(11):425. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110425

Chicago/Turabian Style

Wang, Shuai, Gang Xu, and Qingsheng Guo. 2018. "Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China" ISPRS International Journal of Geo-Information 7, no. 11: 425. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110425

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