Abstract

Spatial autocorrelation and spatial interaction are two important analytical processes for geographical analyses. However, the internal relations between the two types of models have not been brought to light. This paper is devoted to integrating spatial autocorrelation analysis and spatial interaction analysis into a logic framework by means of Getis-Ord’s indexes. Based on mathematical derivation and transform, the spatial autocorrelation measurements of Getis-Ord’s indexes are reconstructed in a new and simple form. A finding is that the local Getis-Ord’s indexes of spatial autocorrelation are equivalent to the rescaled potential energy indexes of spatial interaction theory based on power-law distance decay. The normalized scatterplot is introduced into the spatial analysis based on Getis-Ord’s indexes, and the potential energy indexes are proposed as a complementary measurement. The global Getis-Ord’s index proved to be the weighted sum of the potential energy indexes and the direct sum of total potential energy. The empirical analysis of the system of Chinese cities are taken as an example to illustrate the effect of the improved methods and measurements. The mathematical framework newly derived from Getis-Ord’s work is helpful for further developing the methodology of geographical spatial modeling and quantitative analysis.

Highlights

  • Spatial autocorrelation and spatial interaction models represent two theoretical cornerstones and classic contents of geographical analyses

  • The mathematical links between spatial autocorrelation and spatial interaction have not been revealed at present

  • A scatter plot can be introduced into the analytical process

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Summary

Introduction

Spatial autocorrelation and spatial interaction models represent two theoretical cornerstones and classic contents of geographical analyses. Spatial autocorrelation is based on the concept of correlation coefficient, and the main measurements include Moran’s index [1], Geary’s coefficient [2], and Getis-Ord’s indexes [3, 4]. Spatial interaction is based on the gravity concept, and the chief models and methods including gravity model [5,6,7], potential energy formulae [8, 9], and entropy-maximizing model family [10,11,12]. The similarities between spatial autocorrelation and interaction are as follows Both of them are based on size measurements and distance decay effect. Both of them can be used to describe strength patterns of spatial association between different geographical elements.

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