SCIENTIA GEOGRAPHICA SINICA ›› 2010, Vol. 30 ›› Issue (1): 22-29.doi: 10.13249/j.cnki.sgs.2010.01.22

• 论文 • Previous Articles     Next Articles

An Analysis of Urban Social Space Based on ESDA ——A Case Study of the Central Urban District in Shanghai

XUAN Guo-fu1, XU Jian-gang2, ZHAO Jing3   

  1. 1. Department of Tourism, Southeast University, Nanjing, Jiangsu 210096;
    2. Department of Urban and Regional Planning, Nanjing University, Nanjing, Jiangsu 210093;
    3. Geography School of Nanjing Xiaozhuang College, Nanjing, Jiangsu 211171
  • Received:2009-07-26 Revised:2009-12-10 Online:2010-01-20 Published:2010-01-20

Abstract: Spatial association is the essential characteristics of spatial related things and phenomena. Exploratory Spatial Data Analysis (ESDA) provides an effective method to reveal the spatial association. The formation of urban social space and its characteristics make the phenomenon of urban social spatial pattern also show significant spatial association. Based on factor analysis, the ESDA methods were applied to urban social space research with the Central Urban District in Shanghai as a case study. From the global and local dimensions, spatial association characteristic of the main factors of urban social space were revealed, by using the indicators and methods of Global Moran’s I index, Moran scatter plot and LISA(Local Indicators of Spatial Association). Global spatial autocorrelation analysis showed that the main factors of urban social space were all with significant spatial association, but there were differences in the degree of spatial association. The factors of socio-economic status and living conditions had stronger spatial association than other factors. Spatial agglomerations of similar socio-economic status and living conditions groups had more prominent contributions to the formation of urban social space. Local spatial autocorrelation analysis demonstrated that the main factors had different local spatial association from the overall pattern, there are obvious "hot spots" and "cold spots", and also some spatial "outliers". The socio-economic status and the residential condition factors show more obvious "hot spots" and "cold spots ", which manifested the characteristics of significant "homogeneous agglomeration, heterogeneous segregation ".

CLC Number: 

  • K928.5