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Space and geography are important aspects of social science research in fields such as criminology, sociology, political science, and public health. Many social scientists are interested in the spatial clustering of various behaviors and events. There has been a rapid development of interest in regression methods for analyzing spatial data over recent years, but little available on the topic that is aimed at graduate students and advanced undergraduate classes in the social sciences (most texts are for the natural sciences, or regional science, or economics, and require a good understanding of advanced statistics and probability theory). <

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Book ID Asin: 154430207X
Book Title: Spatial Regression Models for the Social Sciences (Advanced Quantitative Techniques in the Social Sciences)
Book Author: Guangqing Chi,Jun Zhu
Book Format: unknownBook Price: unknown
Book Category: Books, Science & Math, Earth Sciences and unknown
Book Rating: 7 ratings

Spatial Regression Models for the Social Sciences (Advanced Quantitative Techniques in the Social Sciences) by Guangqing Chi,Jun Zhu Book Review

Name: bunny
Rating: 5.0 out of 5 stars
Title: Great book for spatial regression modeling
Date: Reviewed in the United States on January 3, 2020
Review: Those who use spatial regression modeling should find this work to be thought-provoking and persuasive. The book begins with an introduction to spatial thinking that is peppered heavily with classic works from Cressie, O’Sullivan & Unwin, Anselin, and Bailey & Gatrell, introducing readers to LISA, Geary’s C, Moran’s I, and other common spatial analysis measures. This lays the groundwork for social scientists who have had little exposure to spatial analysis.

Chapters begin with learning objectives and they are enriched with margin notes that define terms such as “spatial cross-regressive model” and help the reader with “tips”; these definitions are rapid-fire and helpfully so. Authors also provide study questions at the end of each chapter (e.g. “How could spatial dependence and spatial heterogeneity be modeled simultaneously?”) that help instructors who choose this book as a text for a class.

On the whole, the book gives a clinical, unbiased interpretation of multiple permutations of models that combine different definitions of spatial influence: inverse-distance weights, buffers, k-nearest neighbors, and rook/queen contiguity. The authors also cite literature associated with each variable and what they might imply for society, when introducing the input variables.

A significant gem in this volume is the final chapter entitled Practical Considerations for Spatial Data Analysis, which sets out to implement some of the book’s aforementioned models in the R statistical environment. The chapter instructs on making plots, calling correlation functions, conducting a step-wise regression, and builds up to a simultaneous autoregressive model for interpreting clustering in regression error terms. The output of each model is also printed alongside the code and the authors interpret the evidence in subsequent paragraphs. This chapter could serve as a standalone instruction guide for those who have been exposed to spatial regression theoretically, but are looking to experiment with their own data.

Overall, I highly recommend it.