Applied Spatial Statistics and Econometrics: Data Analysis in R

This textbook provides a comprehensive introduction to applied spatial data analysis using R, with chapters on unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity, and bootstrapping. It is accompanied by data and R code on Github for practice.

\n Format: Paperback / softback
\n Length: 594 pages
\n Publication date: 26 November 2020
\n Publisher: Taylor & Francis Ltd
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This comprehensive textbook provides a thorough introduction to applied spatial data analysis using the powerful R programming language. Spanning across 10 chapters, each chapter delves into a distinct method, guiding the reader step-by-step in interpreting the obtained results and drawing meaningful conclusions. The author team, consisting of renowned experts in the field, highlights key topics such as unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity, and bootstrapping. To enhance the learning experience, the textbook is accompanied by a comprehensive suite of data and R code hosted on GitHub. This resource allows readers to practice and apply the techniques learned through replication and hands-on exercises, further solidifying their understanding and proficiency in spatial data analysis.

This textbook is a valuable resource for advanced students pursuing degrees in econometrics, spatial planning, and regional science. It equips them with the necessary tools and techniques to effectively analyze and interpret spatial data, enabling them to make informed decisions in various fields. Moreover, researchers and data scientists working with spatial data will find this textbook immensely useful as it offers a comprehensive and up-to-date coverage of the latest methodologies and applications.

The textbook is organized into ten chapters, each dedicated to a specific method or topic in spatial data analysis. The first chapter provides an overview of R and its spatial data analysis capabilities, while subsequent chapters delve into more advanced topics such as unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity, and bootstrapping. Each chapter is accompanied by clear explanations, examples, and R code snippets that allow readers to replicate the analyses and apply the techniques to their own data sets.

One of the key strengths of this textbook is its emphasis on practical applications. The author team showcases real-world case studies and examples from various fields, such as urban planning, environmental science, and health economics, to illustrate the practical significance of spatial data analysis. This helps readers connect the theoretical concepts with real-world scenarios and encourages them to think critically about the challenges and opportunities presented by spatial data.

Furthermore, the textbook is well-written and accessible to students with a basic understanding of R programming. The authors use a clear and concise writing style, and they provide ample explanations and examples to assist readers in navigating the complexities of spatial data analysis. Additionally, the inclusion of a comprehensive suite of data and R code on GitHub allows readers to practice the techniques learned through replication and exercises, further enhancing their understanding and proficiency in the field.

In conclusion, this textbook is a must-have for anyone interested in applied spatial data analysis using R. It provides a comprehensive and practical introduction to the field, covering a wide range of topics and applications. With its emphasis on practical applications, clear writing style, and comprehensive resources, this textbook is suitable for advanced students, researchers, and data scientists alike. Whether you are looking to expand your knowledge in econometrics, spatial planning, or regional science, this textbook will be a valuable asset in your academic and professional journey.

\n Weight: 1624g\n
Dimension: 210 x 278 x 32 (mm)\n
ISBN-13: 9780367470760\n \n