initial population size (Chi, 2009). The population data for this case study are provided by decennial U.S. Census Bureau censuses from 1970 to 2010.
Figure 1.4 shows population change in Wisconsin in four time periods: 1970–1980, 1980–1990, 1990–2000, and 2000–2010. From 1970 to 1980, relatively high population growth occurred in rural and suburban areas. The 1970s marked a rural renaissance for the first time in Wisconsin, also called “turnaround migration” by rural demographers, as natural amenities and employment opportunities attracted migrants to the amenity-rich rural areas (Johnson, 1999). During this time, rural and suburban MCDs experienced higher population growth rates than urban MCDs: the average population growth rate was 13.4 percent for rural MCDs, 16.5 percent for suburban MCDs, and 11.7 percent for urban MCDs (Table 1.1).
Figure 1.4 ⬢ Population change at the MCD level in 1970–1980, 1980–1990, 1990–2000, and 2000–2010 in Wisconsin
Table 1.1
Note: The numbers in each cell represent the average population change rate at the MCD level. Standard deviations are in parentheses.
From 1980 to 1990, a majority of MCDs experienced population decline; it was the slowest growth decade in the history of Wisconsin. The population redistribution pattern was renewed metropolitan growth, mainly due to economic disruptions such as the farm debt crisis, deindustrialization (which downsized the manufacturing), and urban revival (which stopped people migrating to rural areas). In this decade, the average population growth rate in urban MCDs (6.3 percent) was much higher than that in suburban MCDs (3.8 percent), which was much higher than that in rural MCDs (0.1 percent).
From 1990 to 2000, rural areas rebounded as an improved economy and the areas’ natural amenities attracted retirees. Relatively high population growth occurred in northern Wisconsin, central Wisconsin, and some suburban areas. The population growth rate of rural MCDs (9.1 percent) was similar to that of urban MCDs (10.6 percent), and the average population growth rate in suburban MCDs (13.1 percent) was relatively higher than those in rural and urban MCDs.
From 2000 to 2010, the population redistribution pattern was selective deconcentration; technological innovations in communications and transportation, companioned with the economic crisis that occurred in 2008, made migration more selective. It appears that population growth occurred mostly in suburban areas. The average population growth rate of MCDs from 2000 to 2010 in Wisconsin was 3.6 percent. The growth rate varies spatially along the urban-rural continuum: the average population growth rate was 1.8 percent in rural MCDs, 9 percent in suburban MCDs, and 6.1 percent in urban MCDs.
1.4 Structure of the Book
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?
Descriptions of Images and Figures
Back to Figure
The figure shows a detailed map of the state of Wisconsin in the United States.
The state is surrounded by Illinois in the south, the Mississippi River in the southwest with Iowa beyond that, Minnesota in the northwest, Lake Superior in the north, Michigan in the northeast, and Lake Michigan in the east. The state
