Applied Spatial Statistics and Econometrics

This textbook provides a detailed introduction to applied spatial data analysis using R, ideal for students and researchers in related fields.

This textbook serves as a thorough introduction to applied spatial data analysis, utilizing R as its primary tool. Each chapter guides readers through various methods, providing detailed explanations on how to interpret results and draw meaningful conclusions. The structured approach allows for a gradual build-up of knowledge, ensuring that readers can follow along easily and apply what they learn in practical scenarios.

Applied Spatial Statistics and Econometrics covers essential topics such as unsupervised learning, causal inference, spatial weight matrices, and spatial econometrics. The authors have carefully curated the content to include discussions on heterogeneity and bootstrapping, making it a well-rounded resource for understanding complex spatial data. Additionally, the book is supplemented by a suite of data and R code available on GitHub, allowing readers to practice techniques through replication and exercises.

This text is particularly valuable for advanced students in fields like econometrics, spatial planning, and regional science. It also caters to researchers and data scientists engaged in spatial data analysis, making it an essential addition to their libraries. With its comprehensive coverage and practical resources, Applied Spatial Statistics and Econometrics is an excellent tool for both learning and application in the field of spatial analysis.