Spatial Regression Models 2nd Revised edition

For readers already familiar with the classical regression model that social science researchers use widely, Ward and Gleditsch present a self-contained overview of how to integrate spatial dependence into a regression framework. In this edition, they add several aspects of spatial analysis that were outside the domain of the first edition. They cover why space in the social sciences, maps as displays of information, interdependency among observations, spatially lagged dependent variables, the spatial error model, and extensions. Annotation ©2018 Ringgold, Inc., Portland, OR (protoview.com)
Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including: mapping data on spatial units, exploratory spatial data analysis, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures. Using social sciences examples based on real data, Michael D. Ward and Kristian Skrede Gleditsch illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.