In this short course, organized by Centro de Estatística e Aplicações (CEAUL), we will learn statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. We will also learn how to create interactive dashboards and Shiny web applications that facilitate the communication of insights to collaborators and policymakers. We will work through several fully reproducible data science examples using real-world data such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. We
will cover the following topics:
- Spatial data including areal, geostatistical and point patterns;
- 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.
The course materials are based on the book “Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny” by Paula Moraga (2019, Chapman & Hall/CRC) which is freely available at https://paula-moraga.github.io/book-geospatial/.
Dates: 22, 23 and 24 March 2023.