Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China
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 stu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/7/11/425 |
| _version_ | 1819243222649536512 |
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| author | Shuai Wang Gang Xu Qingsheng Guo |
| author_facet | Shuai Wang Gang Xu Qingsheng Guo |
| author_sort | Shuai Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T14:52:16Z |
| format | Article |
| id | doaj.art-7ee0e07da5674e1f830b7c02670c245f |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-23T14:52:16Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-7ee0e07da5674e1f830b7c02670c245f2022-12-21T17:42:54ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-10-0171142510.3390/ijgi7110425ijgi7110425Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, ChinaShuai Wang0Gang Xu1Qingsheng Guo2School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaUrban 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.https://www.mdpi.com/2220-9964/7/11/425street networkland use intensitystreet centralitiesPOIcomplex network |
| spellingShingle | Shuai Wang Gang Xu Qingsheng Guo Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China ISPRS International Journal of Geo-Information street network land use intensity street centralities POI complex network |
| title | Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China |
| title_full | Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China |
| title_fullStr | Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China |
| title_full_unstemmed | Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China |
| title_short | Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China |
| title_sort | street centralities and land use intensities based on points of interest poi in shenzhen china |
| topic | street network land use intensity street centralities POI complex network |
| url | https://www.mdpi.com/2220-9964/7/11/425 |
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