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.

Models Dealing With Spatial Heterogeneity

Learning Objectives

  • Become familiar with the traditional methods used in social science research to deal with spatial heterogeneity.
  • Understand when and how to fit a spatial regime standard linear regression model, interpret the coefficients across different regimes, and use appropriate diagnostics to assess the model.
  • Understand the strengths and weaknesses that spatial regime models have over the aspatial regression models dealing with spatial heterogeneity.
  • Understand when and how to apply a geographically weighted regression method, describe its assumptions, interpret the estimates of the global and local coefficients, and use appropriate model diagnostics.
  • Describe the differences in and cautions about using the three types of methods dealing with spatial heterogeneity.

As addressed in Section 2.3, spatial heterogeneity, or spatial variation, refers to differences in the mean, ...

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