Proposal Description

Spatial data arise in many fields including health, ecology and environment. In this course, we will learn statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. We will also learn how to create interactive maps and dashboards 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 visualizations and dashboards.

The course materials are based on the following books:

"Spatial Statistics for Data Science: Theory and Practice with R" by Paula Moraga (2023, Chapman & Hall/CRC) which is freely available at https://www.paulamoraga.com/book-spatial.html

"Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny" by Paula Moraga (2019, Chapman & Hall/CRC) which is freely available at https://www.paulamoraga.com/book-geospatial.html

Prerequisites: It is assumed participants are familiar with R and it is recommended a working knowledge of generalized linear models. Participants should bring their laptops with R and RStudio installed.