• 经济与区域发展 •

### 基于Moran统计量的空间自相关理论发展和方法改进

1. 北京大学城市与环境学院,北京 100871
• 收稿日期:2009-02-18 修回日期:2009-06-15 出版日期:2009-11-25 发布日期:2009-11-25
• 作者简介:陈彦光(1965-),男,河南罗山人,副教授,理学博士。从事地理分形和空间复杂性研究,重点研究自组织城市网络。 E-mail:chenyg@pku.edu.cn
• 基金资助:

国家科技部科技基础工作专项重点资助项目"地理学方法研究"的综合集成部分(2007FY140800);国家自然科学基金资助项目(40771061)。

### Reconstructing the mathematical process of spatial autocorrelation based on Moran’s statistics

CHEN Yan-guang

1. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
• Received:2009-02-18 Revised:2009-06-15 Online:2009-11-25 Published:2009-11-25
• Supported by:

国家科技部科技基础工作专项重点资助项目"地理学方法研究"的综合集成部分(2007FY140800);国家自然科学基金资助项目(40771061)。

Abstract:

This paper is devoted to developing the theory and methods of spatial autocorrelation analysis based on the Moran statistics. Firstly, the mathematical process of the Moran’s index is reconstructed with the theory of linear algebra. Two kinds of generalized spatial weighting matrix (GSWM) are defined as follows: one is the ideal spatial weighting matrix (ISWM), and the other is the real spatial weighting matrix (RSWM). The Moran’s I can be redefined by both ISWM and RSWM. Secondly, the theoretical essence of Moran’s I is brought to light by using the ideas from symmetry and invariance of mathematical transform. The Moran’s I is in fact the eigenvalue of ISWM and RSWM, and the corresponding eigenvector is just the vector consisting of the standardized data for spatial autocorrelation analysis. Thirdly, the Moran scatterplot is revised. Based on ISWM and RSWM, the Moran scatterplot for local analysis of spatial association is improved and the result is more satisfying than the original form. In the improved scatterplot, ISWM presents a straight line, and RSWM shows itself as a random distribution of data points. Three approaches to estimating the Moran’s I are advanced as follows: (1) The method of formula. Three-step computation process is summed up by means of matrix theory. (2) The method of matrix. The scaling relation is employed to estimate the Moran’s I by calculating the eigenvalue of ISWM or RSWM. (3) The method of regression analysis. This approach is based on the correlation between the standardized vector and ISWM or RSWM. The key step of making analysis of spatial autocorrelation is to construct the contiguity matrix. The spatial weighting matrix (SWM) is divided into four types: (1) locality correlation, (2) quasi-locality correlation, (3) quasi-long-distance correlation, and (4) long distance correlation associated with action at a distance. Different types of SWM are suitable for different cases of geographical analysis. The improved theory and method of spatial autocorrelation based on the Moran’s I is applied to the systems of towns in Hebi Prefecture of Henan Province, China. Based on the measure of total population of towns, a symmetrical pattern of spatial autocorrelation, which looks like a butterfly, is revealed and illustrated. This example shows how to make use of spatial autocorrelation theory in human geographical analysis easily and simply.