Geospatial health data : modeling and visualization with R-INLA and Shiny /: modeling and visualization with R-INLA and Shiny. (2019)

• Record Type: Book
• Further Information: Note: Paula Moraga.
• Authors: Moraga, Paula
• Contents: 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 Paraná, 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;
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 614.420285; Medical mapping; Medical geography -- Maps -- Data processing; Geospatial data; Information visualization; R (Computer program language)
• Languages: English
• ISBNs: 9781000732153; 9781000732030; 9781000732092; 9780429341823
• Related ISBNs: 9780367357955
• Notes: Note: Includes bibliographical references.; Note: Description based on CIP data; resource not viewed.
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