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Geospatial Health Data - Modeling and Visualization with R-INLA and Shiny (Hardcover)
Loot Price: R2,718
Discovery Miles 27 180
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Geospatial Health Data - Modeling and Visualization with R-INLA and Shiny (Hardcover)
Series: Chapman & Hall/CRC Biostatistics Series
Expected to ship within 12 - 17 working days
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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.
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