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 Both Spatial Dependence and Spatial Heterogeneity

Learning Objectives

  • Understand how to incorporate spatial dependence and spatial heterogeneity simultaneously within the spatial regime context.
  • Understand how to fit a spatial regime lag model, a spatial regime error model, and a spatial regime error and lag model.
  • Understand how to interpret the spatial effects and different sets of coefficients across regimes in spatial regime models.
  • Interpret the spatial Chow test.

So far, we have introduced models dealing with spatial dependence (Chapters 3 and 4) and spatial heterogeneity (Chapter 5) separately. Can we relatively easily model both spatial dependence and spatial heterogeneity? The answer is yes. Our approach is to incorporate spatial dependence into spatial regime models.1 This approach incorporates spatial dependence and spatial heterogeneity simultaneously within the spatial ...

  • Loading...
locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles