opportunities for and development in spatial regression methods, we assert that it is an opportune time for social scientists to make better use of spatial regression methods and apply them to social science research. We believe that spatial regression models and methods can be learned with relative ease, and this book is intended to help readers do just that.
1.1 Spatial Thinking in the Social Sciences
Many phenomena of the social sciences exhibit spatial effects; this has been addressed both explicitly and implicitly in many theories and empirical studies. It would be useful to review the spatial thinking and theories as well as the empirical studies in the social sciences to build a foundation and make a case for spatial regression models in social science research. However, doing so is challenging considering that there is a large amount of spatial theories and empirical studies in the social sciences, and certain theories and studies are limited to specific social science disciplines. With both the benefit and the challenge in mind, and considering that this book is about spatial regression models rather than spatial theories, in this section, we provide a brief overview of the current status of spatial theories and empirical studies across social science disciplines.
Most social science disciplines and their subdisciplines have addressed the spatial dimension of their phenomena of study, although the extent to which they have done so varies greatly (Goodchild & Janelle, 2004). The Center for Spatially Integrated Social Science (CSISS) Classics provides a list of classic readings that contribute to spatial thinking in the social sciences.1 Overall, spatial thinking in social science disciplines and subdisciplines can be addressed from multiple perspectives.
First, spatial thinking and theories have originated largely from human geography and regional science. Space and place are in the “blood” of human geographers and regional scientists, who almost always consider space and/or place in their research. They provide the core spatial theories and use them to investigate and explain a wide range of social phenomena. Some spatial thinking and theories have been developed in other social science disciplines but not as extensively as in human geography and regional science. Most existing empirical studies of spatial social sciences, although conducted in a variety of social science disciplines, cite the work of human geography and regional science.
Second, spatial methodologies have been developed by human and physical geographers, regional scientists, economists, statisticians, and others. These methodologies, which are discussed in Section 1.2, include spatial analysis techniques such as GIS and remote sensing image processes as well as statistical methods for spatial data analysis such as spatial point pattern analysis, lattice (or areal) data analysis (where the spatial regression models and methods described in this book fall), geostatistics, and spatial interactive data analysis. The development of spatial methodologies enables and facilitates spatial thinking and theories to be applied to empirical studies of social science research.
Third, the application of spatial thinking and methodologies has experienced a rapid increase in the past two decades in many social science disciplines and subdisciplines (other than geography and regional science), including anthropology; criminology; demography; economics; political science (international studies, political economy, public administration); urban studies and urban planning; sociology; and interdisciplinary areas (such as area studies, development studies, environmental studies, and public health). Their data, when geographically referenced, can be analyzed using spatial methods. The rise in the application of spatial thinking and methodologies in these disciplines is largely due to the increased availability of geographically referenced data (i.e., spatial data), more user-friendly software packages for analyzing spatial data, and the rapid advances in robust and affordable computing power, as previously discussed.
Finally, spatial thinking and methodologies are seen as potentially beneficial to the humanities and social sciences such as communication, education, history, law, linguistics, and psychology from at least two perspectives. One, at the individual level, the socioeconomic and physical environments where the individual is located have effects on the individual; these environments can be seen as the “spatial” elements. Two, if individuals or observations are geocoded, which becomes increasingly easy to do with the development of geocoding techniques, the spatial dimension could be incorporated into empirical analysis using spatial methodologies. As a matter of fact, spatial thinking has already been developed in or for the disciplines of communication, history, and linguistics. Refer to the CSISS Classics for the relevant work.
It should be noted that the discussion here on spatial social science research is far from complete; rather, it is limited to our incomplete understanding of fields outside our own areas of expertise. Many books, journal issues, book chapters, journal articles, and websites provide overviews of spatial social science research. We suggest that readers look into these resources as well as spatial social science research in their own disciplines, if available, for more comprehensive understanding of spatial thinking and theories, methodologies, and applications.
1.2 Introduction to Spatial Effects
What are spatial effects, spatial analysis, spatial data analysis, spatial statistics, spatial autocorrelation, spatial dependence, and spatial heterogeneity? A newcomer to spatial regression models could easily be confused by the numerous concepts and terminologies associated with the models. This section introduces concepts related to spatial effects and the relevant terminologies used in the existing literature. We organize these concepts and terminologies into two categories:
Spatial analysis versus spatial data analysis versus geographic analysis
Four types of spatial data analyses
1.2.1 Spatial Analysis, Spatial Data Analysis, and Geographic Analysis
Spatial data refer to data that are geographically referenced and represent phenomena that are located in space. More specifically, spatial data refer to data that not only have the values or attributes related to the phenomena of interest but also the geographical or locational information of the observations. While aspatial data analysis uses only the former, spatial data analysis uses both. In a broad sense, spatial data analysis is the quantitative study of spatial data (Bailey & Gatrell, 1995). Spatial analysis is sometimes used interchangeably with spatial data analysis, geographic analysis, spatial information analysis, and geographic information analysis in the existing literature. While these terms refer to different things and have different foci, the boundary among them is somehow not clear and not completely agreed upon among researchers from different disciplines. For the purposes of this book, we understand spatial analysis as being composed of spatial data analysis and geographic analysis. The spatial regression models and methods are a specific set of tools for spatial data analysis.
Spatial data refer to data that are referenced geographically and represent phenomena located in space.
Spatial data analysis describes, models, and explains spatial data, from which we can make inferences about the phenomena under study and make predictions for areas where observations have not been sampled (Bailey & Gatrell, 1995). A spatial data analysis is conducted instead of aspatial data analysis if the data have spatial information and the spatial arrangements in the data or in the interpretation of the results are given explicit consideration. In particular, spatial data analysis is about using statistical methods to analyze spatial data; in the existing literature, this is often referred to as spatial statistics.
Spatial
