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:

• Manipulating and transforming point, areal, and raster data,
• Bayesian hierarchical models for disease mapping using areal and geostatistical data,
• Fitting and interpreting spatial and spatio-temporal models with the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches,
• Creating 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 policymakers.

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, modelling, and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.

I Geospatial health data and INLA 1 1. Geospatial health Geospatial health data Disease mapping Communication of results 2. Spatial data and R packages for mapping Types of spatial data Areal data Geostatistical data Point patterns Coordinate Reference Systems (CRS) Geographic coordinate systems Projected coordinate systems Setting Coordinate Reference Systems in R Shapefiles Making maps with R ggplot2 leaflet mapview tmap 3. Bayesian inference and INLA Bayesian inference Integrated Nested Laplace Approximations (INLA) 4. The R-INLA package Linear predictor The inla() function Priors specification Example Data Model Results Control variables to compute approximations II Modeling and visualization 5. Areal data Spatial neighborhood matrices Standardized Incidence Ratio (SIR) Spatial small area disease risk estimation Spatial modeling of lung cancer in Pennsylvania Spatio-temporal small area disease risk estimation Issues with areal data 6. Spatial modeling of areal data. Lip cancer in Scotland Data and map Data preparation Adding data to map Mapping SIRs Modeling Model Neighborhood matrix Inference using INLA Results Mapping relative risks Exceedance probabilities 7. Spatio-temporal modeling of areal data. Lung cancer in Ohio Data and map Data preparation Observed cases Expected cases SIRs Adding data to map Mapping SIRs Time plots of SIRs Modeling Model Neighborhood matrix Inference using INLA Mapping relative risks 8. Geostatistical data Gaussian random fields Stochastic Partial Differential Equation approach (SPDE) Spatial modeling of rainfall in Parana, Brazil Model Mesh construction Building the SPDE model on the mesh Index set Projection matrix Prediction data Stack with data for estimation and prediction Model formula inla() call Results Projecting the spatial field Disease mapping with geostatistical data 9. Spatial modeling of geostatistical data. Malaria in The Gambia Data Data preparation Prevalence Transforming coordinates Mapping prevalence Environmental covariates Modeling Model Mesh construction Building the SPDE model on the mesh Index set Projection matrix Prediction data Stack with data for estimation and prediction Model formula inla() call Mapping malaria prevalence Mapping exceedance probabilities 10. Spatio-temporal modeling of geostatistical data. Air pollution in Spain Map Data Modeling Model Mesh construction Building the SPDE model on the mesh Index set Projection matrix Prediction data Stack with data for estimation and prediction Model formula inla() call Results Mapping air pollution predictions III Communication of results 11. Introduction to R Markdown R Markdown YAML Markdown syntax R code chunks Figures Tables Example 12. Building a dashboard to visualize spatial data with flexdashboard The R package flexdashboard R Markdown Layout Dashboard components A dashboard to visualize global air pollution Data Table using DT Map using leaflet Histogram using ggplot2 R Markdown structure. YAML header and layout R code to obtain the data and create the visualizations 13. Introduction to Shiny Examples of Shiny apps Structure of a Shiny app Inputs Outputs Inputs, outputs and reactivity Examples of Shiny apps Example 1 Example 2 HTML Content Layouts Sharing Shiny apps 14. Interactive dashboards with flexdashboard and Shiny An interactive dashboard to visualize global air pollution 15. Building a Shiny app to upload and visualize spatio-temporal data Shiny Setup Structure of app.R Layout HTML content Read data Adding outputs Table using DT Time plot using dygraphs Map using leaflet Adding reactivity Reactivity in dygraphs Reactivity in leaflet Uploading data Inputs in ui to upload a CSV file and a shapefile Uploading CSV file in server() Uploading shapefile in server() Accessing the data and the map Handling missing inputs Requiring input files to be available using req() Checking data are uploaded before creating the map Conclusion 16. Disease surveillance with SpatialEpiApp Installation Use of SpatialEpiApp ‘Inputs’ page ‘Analysis’ page ‘Help’ page Appendix A R installation and packages used in the book A.1 Installing R and RStudio A.2 Installing R packages A.3 Packages used in the book