<p><b>Spatial Regression Models</b> illustrates the use of spatial analysis in the social sciences. The text includes sections that cover different modeling-related topics: mapping and making projections; doing exploratory spatial data analysis; working with models which have lagged endogenous right-handed side variables; using spatial error correction models; employing conditionally autoregressive models; and dealing with over-time panels exhibiting spatial structures. Each of the modeling-based discussions includes separate delineations of how to proceed when dealing with main variables that are quantitative as well as qualitative. In each section, the authors employ prominent and diverse examples, introducing readers to key literature in the field. The examples are presented along with relevant data and programs written in the R, which illustrate exactly how to undertake the analyses described. The book ends with a chapter that covers techniques for presenting spatial information.</p><p></p><p><b>Key Features</b></p><ul type="disc"><li>Geared toward social science readers, unlike other volumes on this topic. </li><li>Illustrates concepts using well-known international, comparative, and national examples of spatial regression analysis. </li><li>Presents each example alongside relevant data and code, which is also available on a Web site maintained by the authors.</li></ul><p><b>Intended Audience </b></p><p>This book is appropriate for graduate students taking any applied social research methods class. </p>