Summary
Contents
Subject index
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 Dependence
Models Dealing With Spatial Dependence
Learning Objectives
- Understand why and when to account for spatial dependence and how to interpret the diagnostics for spatial dependence in standard linear regression.
- Understand when and how to fit a spatial lag model, interpret the spatial lag effect, and use appropriate diagnostics to assess the model.
- Understand when and how to fit a spatial error model, interpret the spatial error effect, and use appropriate diagnostics to assess the model.
- Identify appropriate spatial weight matrices to use in a spatial lag model or a spatial error model.
- Describe the cautions about using a spatial lag model and a spatial error model.
- Choose between a spatial lag model and a spatial error model in fitting to data.
In Chapters 1 and 2, we introduced basic concepts and ...
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