Spatial Regression Models for the Social Sciences provides comprehensive coverage of spatial regression methods for social scientists and introduces the methods in an easy-to-follow manner.
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 phenomena of various behaviors and events. There has been a rapid development of interest in regression methods for analyzing spatial data over recent decades, 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, regional science, or economics, and require a good understanding of advanced statistics and probability theory).
Spatial Regression Models for the Social Sciences fills the gap and focuses on methods that are commonly used by social scientists. Each spatial regression method is introduced in the same way. Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it, by connecting it to social science research topics. They try to avoid mathematical formulas and symbols as much as possible. Secondly, throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.
Software packages:
Kamenetsky, Maria, Guangqing Chi, and Jun Zhu. 2020. "strm: Spatio-Temporal Regression Modeling." [R package]. Available at https://cran.r-project.org/web/packages/strm/index.html. Released on November 2, 2020.
Zhou, Shuai, Yanling Li, Guangqing Chi, Sy-Miin Chow, and Yosef Bodovski. 2020. " GPS2space: An Open-source Python Library for Spatial Data Building and Spatial Measure Extraction." [python package]. Available at https://gps2space.readthedocs.io/en/latest. Released on September 23, 2020.