Spatial data arise in many fields including health, ecology, environment and business. This course presents statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. It also shows how to create interactive dashboards and Shiny web applications that facilitate the communication of insights to collaborators and policymakers. The course combines theory and practice using real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping and real state analyses. The course covers the following topics:

- Spatial data including areal, geostatistical and point patterns
- Coordinate reference systems and geographical data storages
- R packages for retrieval, manipulation and visualization of spatial data
- Statistical methods to describe, analyze, and simulate spatial data
- Fitting and interpreting Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches
- Communicating results with interactive dashboards and Shiny web applications