Many theories in the social sciences predict spatial dependence or
the similarity of behaviors at neighboring locations. Spatial
Analysis for the Social Sciences demonstrates how researchers can
diagnose and model this spatial dependence and draw more valid
inferences as a result. The book is structured around the
well-known Galton's problem and presents a step-by-step guide to
the application of spatial analysis. The book examines a variety of
spatial diagnostics and models through a series of applied examples
drawn from the social sciences. These include spatial lag models
that capture behavioral diffusion between actors, spatial error
models that account for spatial dependence in errors, and models
that incorporate spatial heterogeneity in the effects of
covariates. Spatial Analysis for the Social Sciences also examines
advanced spatial models for time-series cross-sectional data,
categorical and limited dependent variables, count data, and
survival data.
General
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