The book *Applied Spatial Data Analysis with R* by Roger S. Bivand, Edzer J. Pebesma, and Virgilio Gómez-Rubio is reviewed by Guangqing Chi, an Associate Professor of Sociology and Research Scientist at Mississippi State University. The review highlights the book's strengths in addressing the challenges of teaching spatial analysis to social science students, particularly in terms of content, accessibility, and practical application. The book is divided into two parts: Part I covers the basics of using R for handling spatial data, including R classes, data input/output, visualization, and programming. Part II delves into spatial statistical analysis, covering methods such as spatial point data analysis, geostatistics, and areal data analysis, with a focus on disease mapping. Key strengths of the book include its comprehensive yet concise coverage of essential topics, clear writing style, and seamless integration of methodological discussions with R code examples. The authors provide concise introductions, summaries, and R outputs for each method, allowing readers to practice and verify their understanding. However, the reviewer suggests two areas for improvement: more detailed coverage of spatial econometric models and a comparison of R with other software packages. Overall, the book is recommended for both students and instructors seeking a single resource for learning spatial analysis and using R.The book *Applied Spatial Data Analysis with R* by Roger S. Bivand, Edzer J. Pebesma, and Virgilio Gómez-Rubio is reviewed by Guangqing Chi, an Associate Professor of Sociology and Research Scientist at Mississippi State University. The review highlights the book's strengths in addressing the challenges of teaching spatial analysis to social science students, particularly in terms of content, accessibility, and practical application. The book is divided into two parts: Part I covers the basics of using R for handling spatial data, including R classes, data input/output, visualization, and programming. Part II delves into spatial statistical analysis, covering methods such as spatial point data analysis, geostatistics, and areal data analysis, with a focus on disease mapping. Key strengths of the book include its comprehensive yet concise coverage of essential topics, clear writing style, and seamless integration of methodological discussions with R code examples. The authors provide concise introductions, summaries, and R outputs for each method, allowing readers to practice and verify their understanding. However, the reviewer suggests two areas for improvement: more detailed coverage of spatial econometric models and a comparison of R with other software packages. Overall, the book is recommended for both students and instructors seeking a single resource for learning spatial analysis and using R.