Spatial Data Analysis With R

This is an introduction for social science students to the growing field of spatial data analysis using the R platform. The text assumes no prior knowledge of either, beyond the contents of an introductory statistics course. It uses the open-source software R, and relevant spatial data analysis packages, to provide practical guidance of how to conduct spatial data analysis with readers′ own data sets. The book first briefly introduces students to R, covers some basic concepts in statistical data analysis, and then focuses on discussing the central ideas of spatial data analysis. All the discussions are supported with R scripts so that students can work on their own and produce results that the book helps interpret. Each chapter ends with review questions to test understanding. The book is suited for upper-level undergraduate social science students and graduate students, and other social scientists who are interested in analyzing their spatial data with R.

A companion website for the book can be found on the Resources tab above. It includes R code and data for students to replicate the examples in the book. The password-protected instructor side of the site includes exercises and answers which can be set for homework.

Danlin Yu is a distinguished geographic information scientist, spatial data analyst, complex system modeler, and urban public health expert. With a specialization in geographic information and spatial data analysis, Dr. Yu has made significant contributions to the fields of urban remote sensing, cartographical design and presentation, spatial statistical analysis, geocomputation, urban and regional planning, and system dynamic modeling for complex systems. His work is particularly impactful in the realm of urban planning, sustainable development, public health and environmental health, where he applies advanced methodologies to tackle pressing urban challenges.

Over nearly two decades of dedicated work in geographic information analysis, Dr. Yu has established himself as a leader in his field. His expertise spans the entire spectrum of spatial analysis, from mapping and statistical analysis to remote sensing data extraction and the development of innovative methodologies. His ability to integrate these diverse skill sets into cohesive and actionable insights has positioned him at the forefront of his discipline.

Dr. Yu’s scholarly contributions are both extensive and influential. He has authored and co-authored over 100 peer-reviewed articles in internationally recognized journals, solidifying his reputation as a thought leader in geographic information science and urban studies. In addition, he has contributed to three collaborative books focusing on urban development and urbanization in China, providing critical insights into the complex processes shaping modern cities.

His expertise in spatial statistical analysis has been applied across multiple domains, including urban public health, environmental management, and population prediction. His research has significantly advanced the understanding of upstream factors in infectious disease prevention and the causes of urban lead poisoning. Moreover, Dr. Yu’s innovative integration of spatial data analysis, complex system dynamics modeling, advanced machine learning, and big data analytics places him at the cutting edge of research in urban planning, sustainability, and public health.

Throughout his career, Dr. Yu has collaborated with leading figures in the field, including spatial economist Dr. Roger Bivand, with whom he co-authored the R package for geographically weighted regression analysis (spgwr). Since 2010, he has been at the forefront of developing a new R package for “geographically weighted panel regression,” showcasing his pioneering contributions to the advancement of spatial analysis techniques. His work continues to influence the future direction of spatial data analysis and its applications in urban environments, making him a pivotal figure in the ongoing dialogue on sustainable urban development and public health.