Spatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics. Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it by connecting it to social science research topics. Throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.

Exploratory Spatial Data Analysis

Learning Objectives

  • Review exploratory data analysis methods.
  • Understand the concept and different types of neighborhood structure.
  • Understand why and when to use spatial weight matrices and how to choose which spatial weight matrices to use.
  • Distinguish spatial autocorrelation, spatial dependence, and spatial heterogeneity.
  • Understand the statistics for measuring global and local spatial association.
  • Familiarize yourself with methods for visualizing spatial data.

In this chapter, we introduce some basic but important concepts and issues related to spatial regression models as well as methods for preliminary analysis before conducting spatial regression analysis. Section 2.1 briefly summarizes exploratory data analysis, which is generally familiar to social scientists. Section 2.2 addresses neighborhood structures and spatial weight matrices, two key components of spatial data analysis. Section 2.3 discusses the concepts of spatial ...

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