Spatial Data Analysis With R

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.

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.

• PrefaceAcknowledgmentsAbout the AuthorChapter 1. The Journey Starts With R1.1 What Is R, and Why Should We Use R?1.2 Getting and Familiarizing Yourselves With R1.3 The Two Companions of R1.4 Basic Operations in R1.5 The R Packages1.6 The R Task Views and Spatial Task ViewConclusionReview QuestionsChapter 2. Very Basic Concepts of Statistical Data Analysis2.1 The Concepts of Variable, Random Variable and Variable Distribution, and Degrees of Freedom2.2 The Concept of Hypothesis Testing2.3 Exploratory Data Analysis2.4 Have a Taste of Regression Analysis2.5 Practices in RReview QuestionsChapter 3. Spatial Data is Special: Working With the Complexity of Spatial Data3.1 Spatial/Geographical/Map Data—Recognize Them3.2 Spatial Data is Special—Spatial Effects3.3 Spatial Data Analysis3.4 Spatial Effects’ Impact on Data Analysis3.5 Exploratory Spatial Data Analysis3.6 Quantifying Spatial Autocorrelation—Essence of ESDA3.7 Practice in RReview QuestionsChapter 4. The Concept of Neighbor: Spatial Linkage Matrix and Spatial Weight4.1 Second Contact: Spatial Autocorrelation4.2 Spatial Neighbors—Are You My Neighbor?4.3 Spatial Weight and Spatial Lag Revisit4.4 Practice in RReview QuestionsChapter 5. Global Spatial Autocorrelation5.1 Third Contact: Spatial Autocorrelation: The Global and Local Versions5.2 Introducing the Moran’s Index (Coefficient)5.3 Practice in RReview QuestionsChapter 6. Local Spatial Autocorrelation6.1 Global and Local: What Is Their Relationship6.2 The Local Moran’s Index6.3 Global and Local Again: The Moran’s Scatterplot6.4 Practice in RReview QuestionsChapter 7. Spatial Autoregressive Models7.1 Regression With Spatial Data7.2 Taxonomy of Spatial Autoregressive Models as Alternative7.3 Practice in RReview QuestionsChapter 8. Eigenfunction-Based Spatial Filtering Regression8.1 Fourth Contact: Spatial Autocorrelation8.2 Spatial Autocorrelation as Map Pattern8.3 Augmented Regression With Spatial Filters as Synthetic Covariates8.4 Practice in RReview QuestionsChapter 9. Introduction to Local Models: Geographically Weighted Regression and Eigenfunction-Based Spatial Filtering Approach9.1 Global and Local Regression9.2 Geographically Weighted Regression (GWR)9.3 Eigenfunction-Based Spatial Filtering Approach to Addressing Spatial Nonstationarity9.4 Comparison Between GWR and ESF SVC Models9.5 Practice in RReview QuestionsChapter 10. Brief Introduction to Spatial Panel Regression and SVC Panel Regression10.1 Panel Dataset and Panel Regression10.2 Spatial Panel Models10.3 Spatially Varying Coefficient Process With Panel Model10.4 Practice in RReview QuestionsChapter 11. Conclusion11.1 Journey So Far11.2 Future Learning DirectionsAppendix: Answers to Review QuestionsReferencesIndex