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Throughout this book, each spatial regression method is introduced in two components. First, we explain what the method is and when we can or should use it by connecting it to a few social science research topics. Mathematical formulas and symbols are kept to a minimum. Second, we use three social science examples to demonstrate how to use the method and what the results can tell us. The primary example, which is the same research and data for most methods discussed in the book, examines the association between population growth from 1970 to 2010 in 1,837 Wisconsin MCDs and its relevant factors based on a geographically referenced longitudinal data set. The second example relates migration from 1995 to 2000 to individual, household, and community characteristics in Wisconsin. The third example examines poverty in association with demographic characteristics and socioeconomic conditions from 2000 to 2010 at the county level in the contiguous United States. Readers can apply our research procedures to their research at various levels of units of analysis, such as countries, regions, counties, census tracts, metro/nonmetro areas, neighborhoods, communities, block groups, and others.

This book is composed of eight chapters divided into thirty sections. Chapter 1 has provided a brief summary of spatial social science theories and thinking as well as introductions to spatial effects and the primary data example used throughout this book. Chapter 2 addresses some important concepts and issues of spatial regression models and methods, including exploratory data analysis, neighborhood structure and spatial weight matrix, spatial dependence and heterogeneity, and exploratory spatial data analysis.

Chapter 3 introduces spatial regression models dealing with spatial dependence, including spatial lag models and spatial error models. Advanced spatial regression models dealing with spatial dependence, including spatial error models with spatially lagged responses, spatial cross-regressive models, and multilevel linear regression models, are introduced in Chapter 4. Chapter 5 introduces spatial regression models and methods dealing with spatial heterogeneity, including aspatial regression models, spatial regime models, and geographically weighted regression. Although both this chapter and Chapter 2 are recommended to be read in full, Chapters 3 to 5, which each introduce one method, are recommended to be read consecutively but do not have to be—readers can go to the method of interest directly.

Chapter 6 discusses extended spatial regime models and approaches for dealing with both spatial dependence and spatial heterogeneity in spatial regression analysis. Chapter 7 introduces some more advanced spatial regression models, including spatio-temporal regression models, spatial regression forecasting models, and geographically weighted regression for forecasting. A general procedure for studying social science phenomena with the spatial dimension in mind is suggested in Chapter 8 using the poverty data example and R code.

Study Questions

1 Which social science disciplines have a spatial aspect? How? To what extent?

2 What are spatial effect, spatial data analysis, geographic analysis, and spatial analysis?

3 What is the difference between spatial data analysis and geographic analysis?

4 What is the difference between spatial analysis and spatial data analysis?

5 What are the four types of spatial data analysis? What are they mainly used for?

6 How is the spatial dimension of population change addressed in related disciplines?

7 For your area of research, are spatial concepts and/or theories used? If so, what are they?

8 For your area of research, how do empirical studies typically address the spatial dimension?