Application of the Integration of Spatial Statistical Analysis with GIS to the Analysis of Regional Economy

Spatial autocorrelation means the self-correlation or spatial dependence among observations of a geo-referenced 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 spatial autocorrelation and spatial association firstly,then discuss the criteria for the identification of spatial association by the use of global Moran Coefficient,Local Moran and Local Geary.[FK(W20?40ZQ]Secondly,the authors make a brief review about the integration of the spatial statistical analysis with GIS which is believed to occur in two different ways:embedding spatial statistical analysis into a GIS environment and embedding selected GIS functions into a spatial statistical analysis environment.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.Constructing an adjacency spatial weight matrix is the first step to deal with further analysis.A two-dimensional matrix can be expressed as a one-dimensional array by using the"List"class.In this paper,we use a spatial neighbor list table to represent spatially adjacent relations among different regional units.With the use of Avenue,users can view,choose,input and report important information and results,and report error messages.All of these window-based operations make it possible for users to execute abstract statistical analysis simply by pointing and clicking a friendly GUI(graphical user interface).We take Xinjiang Uyger Autonomous Region as a research area,and utilize mean annual GDP increasing velocity from 1978 to 1999 in different counties,then calculate global Moran and local Moran 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.