Book

Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny


Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics:

  • Manipulate and transform point, areal and raster data,
  • Bayesian hierarchical models for disease mapping using areal and geostatistical data,
  • Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, and the R-INLA package,
  • Create interactive and static visualizations such as disease maps and time plots,
  • Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers.

The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.

The book will be published by Chapman & Hall/CRC Biostatistics Series. The online version of the book can be read here, and it is licensed under CC BY-NC-ND 4.0.






Data


Chapter 10 Spatio-temporal modeling of geostatistical data. Air pollution in Spain

To reproduce the example of this chapter, we need to download the file `dataPM25.csv` which contains PM 2.5 levels at monitoring stations in Spain in years 2015, 2016 and 2017. Data are obtained from the European Environment Agency.

Download PM2.5 data

Chapter 15 Building a Shiny app to upload and visualize spatio-temporal data

To build the Shiny app shown in this chapter, we need to download the folder `appdir`. This folder contains the following subfolders:
  • `data` which contains a file with the data of lung cancer in Ohio, and a folder with the shapefile of Ohio
  • `www` with an image of a Shiny logo
Ohio data and map are obtained from the SpatialEpiApp package (Moraga, 2017).

Download appdir folder