Spatial Analysis Using Big Data

Chapter Three

Global and local indicators of spatial associations

Hajime Seya      Departments of Civil Engineering, Kobe University, Kobe, Hyogo, Japan

Abstract

Section 3.1 of this chapter introduces a spatial weight matrix, which is a central tool for addressing spatial autocorrelation and spatial heterogeneity among data. Section 3.2 describes the methods of testing for spatial autocorrelation; more specifically, subsection 3.2.1 focuses on global indicators of spatial association, where the central question is whether there is any spatial autocorrelation in the data, and subsection 3.2.2 focuses on local indicators of spatial association, which is related to where the spatial autocorrelation occurs. Section 3.3 introduces methods ...