Spatial Data Analysis With R

1. Auflage 2025
ISBN: 978-1-0718-6235-3
Verlag: SAGE Publications Inc

Buch, Englisch, Band 16, 416 Seiten, Format (B × H): 177 mm x 254 mm, Gewicht: 880 g

Reihe: Advanced Quantitative Techniques in the Social Sciences

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.

Autoren/Hrsg.

Yu, Danlin

Inhaltsverzeichnis

Preface
Acknowledgments
About the Author
Chapter 1. The Journey Starts With R
1.1 What Is R, and Why Should We Use R?
1.2 Getting and Familiarizing Yourselves With R
1.3 The Two Companions of R
1.4 Basic Operations in R
1.5 The R Packages
1.6 The R Task Views and Spatial Task View
Conclusion
Review Questions
Chapter 2. Very Basic Concepts of Statistical Data Analysis
2.1 The Concepts of Variable, Random Variable and Variable Distribution, and Degrees of Freedom
2.2 The Concept of Hypothesis Testing
2.3 Exploratory Data Analysis
2.4 Have a Taste of Regression Analysis
2.5 Practices in R
Review Questions
Chapter 3. Spatial Data is Special: Working With the Complexity of Spatial Data
3.1 Spatial/Geographical/Map Data—Recognize Them
3.2 Spatial Data is Special—Spatial Effects
3.3 Spatial Data Analysis
3.4 Spatial Effects’ Impact on Data Analysis
3.5 Exploratory Spatial Data Analysis
3.6 Quantifying Spatial Autocorrelation—Essence of ESDA
3.7 Practice in R
Review Questions
Chapter 4. The Concept of Neighbor: Spatial Linkage Matrix and Spatial Weight
4.1 Second Contact: Spatial Autocorrelation
4.2 Spatial Neighbors—Are You My Neighbor?
4.3 Spatial Weight and Spatial Lag Revisit
4.4 Practice in R
Review Questions
Chapter 5. Global Spatial Autocorrelation
5.1 Third Contact: Spatial Autocorrelation: The Global and Local Versions
5.2 Introducing the Moran’s Index (Coefficient)
5.3 Practice in R
Review Questions
Chapter 6. Local Spatial Autocorrelation
6.1 Global and Local: What Is Their Relationship
6.2 The Local Moran’s Index
6.3 Global and Local Again: The Moran’s Scatterplot
6.4 Practice in R
Review Questions
Chapter 7. Spatial Autoregressive Models
7.1 Regression With Spatial Data
7.2 Taxonomy of Spatial Autoregressive Models as Alternative
7.3 Practice in R
Review Questions
Chapter 8. Eigenfunction-Based Spatial Filtering Regression
8.1 Fourth Contact: Spatial Autocorrelation
8.2 Spatial Autocorrelation as Map Pattern
8.3 Augmented Regression With Spatial Filters as Synthetic Covariates
8.4 Practice in R
Review Questions
Chapter 9. Introduction to Local Models: Geographically Weighted Regression and Eigenfunction-Based Spatial Filtering Approach
9.1 Global and Local Regression
9.2 Geographically Weighted Regression (GWR)
9.3 Eigenfunction-Based Spatial Filtering Approach to Addressing Spatial Nonstationarity
9.4 Comparison Between GWR and ESF SVC Models
9.5 Practice in R
Review Questions
Chapter 10. Brief Introduction to Spatial Panel Regression and SVC Panel Regression
10.1 Panel Dataset and Panel Regression
10.2 Spatial Panel Models
10.3 Spatially Varying Coefficient Process With Panel Model
10.4 Practice in R
Review Questions
Chapter 11. Conclusion
11.1 Journey So Far
11.2 Future Learning Directions
Appendix: Answers to Review Questions
References
Index

Über Autor(innen)

Yu, Danlin
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.