ABSTRACT
Section 1.1 of this chapter introduces the concept of spatial dependence that often arises in cross-sectional spatial data samples. Spatial data samples represent observations that are associated with points or regions, for example homes, counties, states, or census tracts. Two motivational examples are provided for spatial dependence, one based on spatial spillovers stemming from congestion effects and a second that relies on omitted explanatory variables. Section 1.2 sets forth spatial autoregressive data generating processes for spatially dependent sample data along with spatial weight matrices that play an important role in describing the structure of these processes. We provide more detailed discussion of spatial data generating processes and associated spatial econometric models in Chapter 2, and spatial weight matrices in Chapter 4. Our goal here is to provide an introduction to spatial autoregressive processes and spatial regression models that rely on this type of process. Section 1.3 provides a simple example of how congestion effects lead to spatial spillovers that impact neighboring regions using travel times to the central business district (CBD) region of a metropolitan area. Section 1.4 describes various scenarios in which spatial econometric models can be used to analyze spatial spillover effects. The final section of the chapter lays out the plan of this text. A brief enumeration of the topics covered in each chapter is provided.
