Program
The short course begins at 9:00 (day 1) and continue 9:00-13:00 (day 1), 8:30 - 13:00 (day 2) and 12:45 (day 3). A thirty-minute coffee break is planned for each day.
Day 1 - March 22
09:00–10:30 Lecture 1: Introduction to Spatial Data Science using R
Coffee break
11:00–13:00 Lecture 2: Areal data: spatial autocorrelation and modeling. Making maps with R
Day 2 - March 23
08:30–10:30 Lecture 3: Geostatistical data: spatial interpolation
Coffee break
11:00–13:00 Lecture 4: Geostatistical data: model-based geostatistics. Interactive dashboards with flexdashboard and Shiny
Day 3 - March 24
08:30–10:15 Lecture 5: Point processes: simulation and complete spatial randomness
Coffee break
10:45–12:00 Lecture 6: Point processes: intensity and clustering.
Day 1 - March 22
09:00–10:30 Lecture 1: Introduction to Spatial Data Science using R
Coffee break
11:00–13:00 Lecture 2: Areal data: spatial autocorrelation and modeling. Making maps with R
Day 2 - March 23
08:30–10:30 Lecture 3: Geostatistical data: spatial interpolation
Coffee break
11:00–13:00 Lecture 4: Geostatistical data: model-based geostatistics. Interactive dashboards with flexdashboard and Shiny
Day 3 - March 24
08:30–10:15 Lecture 5: Point processes: simulation and complete spatial randomness
Coffee break
10:45–12:00 Lecture 6: Point processes: intensity and clustering.
Abstract
Spatial data arise in many fields including health, ecology, environment and business. 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 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:
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/
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. They should also install the following R packages:
install.packages(c("sf", "sp", "spdep", "raster", "rgdal", "rgeos", "ggplot2", "tmap", "leaflet", "DT", "dplyr", "rnaturalearth", "rmarkdown", "flexdashboard", "SpatialEpi", "geoR", "spocc", "wbstats"))
install.packages("INLA", repos = "https://inla.r-inla-download.org/R/stable", dep = TRUE)
- 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/
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. They should also install the following R packages:
install.packages(c("sf", "sp", "spdep", "raster", "rgdal", "rgeos", "ggplot2", "tmap", "leaflet", "DT", "dplyr", "rnaturalearth", "rmarkdown", "flexdashboard", "SpatialEpi", "geoR", "spocc", "wbstats"))
install.packages("INLA", repos = "https://inla.r-inla-download.org/R/stable", dep = TRUE)
