We will learn how to develop and interpret spatial models with R-INLA, and how to create maps and other visualizations to facilitate the communication with collaborators and policymakers. The course is hands-on with three parts: - Introduction to geospatial data and R-INLA - Modeling areal data (lung cancer risk in Pennsylvania, USA) - Modeling geostatistical data (malaria prevalence in The Gambia) We will focus on health applications but the methods covered are applicable to many other fields that deal with georeferenced data such as ecology, demography or criminology