Now in its second edition, Applied Spatial Statistics and Econometrics offers a modern and accessible introduction to spatial data analysis using R. Emphasising reproducibility, real-world datasets, and practical workflows, this comprehensive guide introduces spatial thinking from a critical analytical perspective, highlighting the importance of location, distance, and neighbourhood effects in shaping social and economic phenomena.

Readers are guided through foundational concepts, including spatial data structures (areal, point, and grid data), visualisation techniques, and spatial econometric models such as spatial lag, spatial error, and spatial Durbin specifications. Updates reflect the substantial evolution of spatial models and R packages, such as the transition to sf and terra, enhancements to spatstat, new tools for spatial sampling and bootstrap, and fully reproducible analyses with complete R code. Topics include geographically weighted regression, spatial point pattern analysis, DEGURBA classification and spatial principal component analysis.

Accompanied by datasets and complete R code on GitHub and RPubs, the book enables readers to replicate analyses and adapt methods to their own research. It is an essential resource for advanced students of econometrics, spatial planning, and regional science, as well as researchers and data scientists seeking to harness the power of spatial analysis for evidence-based insights and policy recommendations.