Spatial analyses are important in macro-level criminological inquiry because the failure to model spatial effects, if present, can lead to inaccurate inferences about predictor variables. In addition, spatial effects can be substantively revealing because they can suggest the existence of diffusion processes. Both formally and through simulation experiments, we consider the problems associated with ecological inference and ecological inference methods in the presence of various forms of spatial effects. Thus, they do not accurately portray how realistic spatial dependence affects cross-level inference. We focus on how realistic forms of spatial autocorrelation and spatial heterogeneity affect ecological inference, in particular, OLS and King's EI estimator.
Attention is paid to the thematic distribution of articles for each journal and across journals.
The journals are also compared with respect to citation counts, areas of concentration and authorship patterns. The impact of each article is examined through an analysis of citations over the decade. Productivity of individual scholars is measured using article counts, page counts and citation indices. The field has seen a recent and rapid growth spurred both by theoretical concerns as well as by the need to be able to apply econometric models to emerging large geocoded data bases. The review presented in this chapter outlines the basic terminology and discusses in some detail the specification of spatial effects, estimation of spatial regression models, and specification tests for spatial effects.