新疆各县市空间经济关联分析初步研究

doi:10.13826/j.cnki.cn65-1103/x.2003.03.015

Abstract

Abstract: Spatial autocorrelation means the self-correlation or spatial dependence among observations of a georeferenced attribute. There are two different scales for spatial dependence：global indicators and local indicators. In this paper, the authors summarize a few spatial statistical analysis methods concerning about how to measure and identify spatial autocorrelation and spatial association firstly, then make a brief review about the integration of Spatial Statistical Analysis with GIS. Based on what has been done in this area, the authors point out that it is necessary and worthwhile to develop a user-friendly statistical module combining spatial statistical analysis methods with GIS visual techniques in GIS directly, and provide an example to illustrate how this can be implemented in Arcview using Avenue. To construct spatial proximity weight matrix is the first step. A two-dimensional matrix can be expressed as a one-dimensional array by using the "List" class. In this paper, we use a spatial proximity list table to represent spatially adjacent relations among different regional units. We take Xinjiang Uyger Autonomous Region as research area, and utilize mean Growth Rate of GDP ([1978～1990, 1991～1999]) in different counties, then calculate global MC and local MC based on those data, and illustrate the usefulness of that module in identifying the characteristic and significance of spatial association among observed locations over space. According to analytical results, there is a significant positive spatial autocorrelation between mean growth rates of GDP over 87 counties in Xinjiang, either in 1978～1990, or 1991～1999. We also investigate the spatial association between core counties and adjacent counties by computing the Local Moran and Geary Statistics at the county level. With the use of a conditional randomization or permutation approach, we can identify some different types of significant local spatial association based on the analysis of different counties. As a results, insight into the types of spatial association present in an economic region allow for more effective implementation of economic development policies.

Key words: Xinjiang, Spatial Economic Association, Regional Economic Analysis

CLC Number:

F127

CHEN Fei, GAO Zhi-gang, GOU Zhong-lin. A preliminary study on Spatial Economic Association among Counties in Xinjiang[J].Arid Land Geography, 2003, 26(3): 274-280.

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A preliminary study on Spatial Economic Association among Counties in Xinjiang

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