PROGRESS IN GEOGRAPHY ›› 2017, Vol. 36 ›› Issue (11): 1349-1358.doi: 10.18306/dlkxjz.2017.11.004

• Orginal Article • Previous Articles     Next Articles

Correlation analysis of road structure and commercial agglomeration in Wuhan City

Yuyao HAN1(), Limin JIAO1,2,*(), Gang XU1   

  1. 1. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
    2. Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China
  • Online:2017-12-07 Published:2017-12-07
  • Contact: Limin JIAO E-mail:hanyy@whu.edu.cn;lmjiao027@163.com
  • Supported by:
    National Natural Science Foundation of China, No.41571385

Abstract:

Urban road structure is one of the most important factors influencing commercial agglomeration. Research on the relationship between urban road structure and commercial agglomeration plays a supporting role for the layout of services and traffic planning. Based on theories of space syntax combined with GIS and bivariate correlation analysis, we explored the correlation between road structure and commercial agglomeration in the Wuhan metropolitan area. The space syntax model was used to compute road structure indicators. The kernel density estimation method was used to calculate the density of commercial points of interests (C-POI) in 2014 to analyze the spatial structure of commercial agglomeration. We used the Pearson correlation coefficient to analyze the correlation between road structure and commercial agglomeration. The results show that: (1) Global integration showed the highest correlation with the commercial agglomeration among the four spatial syntactic variables. Finance and insurance services had the highest correlation with road structure. (2) Connectivity value and global integration value were significantly and positively correlated with C-POI density, with a spatial correlation pattern of "high-high" agglomeration. Total depth value showed a significant and negative correlation with C-POI density, with a spatial correlation pattern of "high-low" agglomeration. C-POI density increased first and then decreased with increasing control values. (3) The spatial distribution of commercial agglomeration presented a "multicore-transitional area-periphery" multiple level structure. "High-high" agglomeration and "high-low" agglomeration were concentrated in the "core area" and the "transitional area" in the vicinity of the cores.

Key words: space syntax, kernel density estimation, road structure, commercial agglomeration, spatial correlation, Wuhan City