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.

Practical Considerations for Spatial Data Analysis

Learning Objectives

  • Gain a basic understanding of spatial regression analysis of areal data in the R environment.
  • Become familiar with a practical procedure of spatial data analysis.
  • Choose between a theory-based approach and a data-driven approach for spatial regression modeling.
  • Summarize the methods involved in spatial data analysis and understand their advantages and disadvantages.
  • Develop a practical procedure of spatial data analysis for your own research.

In this chapter, we illustrate the procedure of spatial regression analysis by

  • using a new data example of U.S. poverty and
  • demonstrating the data analyses in R.

Throughout this book, we have primarily used the population change example for illustration purposes; a different data example provides a fresh view of spatial data analysis. We chose R for running the data analysis ...

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