Overview

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.

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9781071862353

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Reviews

This text provides an excellent introduction along with a thorough overview of spatial analysis techniques with R. The book provides a solid framework to move students through a wide variety of models and spatial frameworks for analysis while maintaining a level of accessibility superior to other texts on the subject. With the increasing importance and application of spatial analysis in research, this text is appropriate for a variety of disciplines including the natural sciences and social sciences.   -- Mike Hollingsworth The book′s approach to teaching spatial data analysis has the potential to significantly enhance the learning experience in the classroom. -- Kesong Hu The book effectively combines theoretical concepts with practical applications, providing students with the essential skills to translate learning into practice. -- Man Kit Lei This is a textbook I would use with my graduate students and doctoral students. This text will help students feel more comfortable with statistics and numbers. -- Bret D. Cormier

Author Information

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.

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