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Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (Chapman & Hall/CRC Biostatistics Series) de Paula Moraga

Descripción - Reseña del editor 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. Biografía del autor Paula Moraga is a Lecturer in the Department of Mathematical Sciences at the University of Bath. She received her Master’s in Biostatistics from Harvard University and her Ph.D. in Statistics from the University of Valencia. Dr. Moraga develops innovative statistical methods and open-source software for disease surveillance including R packages for spatio-temporal modeling, detection of clusters, and travel-related spread of disease. Her work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries.

Detalles del Libro

Name: Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (Chapman & Hall/CRC Biostatistics Series)
Autor: Paula Moraga
Categoria: Libros,Libros universitarios y de estudios superiores,Medicina y ciencias de la salud
Tamaño del archivo: 18 MB
Tipos de archivo: PDF Document
Descargada: 435 times
Idioma: Español
Archivos de estado: AVAILABLE

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Geospatial Health Data: Modeling and Visualization with R ~ Welcome. The book Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny has been published by Chapman & Hall/CRC Biostatistics Series, and can be bought from CRC Press or .. The online version of the book can be read here, and it is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Geospatial health data : modeling and visualization with R ~ Geospatial health data : modeling and visualization with R-INLA and Shiny / Paula Moraga. Author/Creator: Moraga, Paula author. Publication: Boca Raton : CRC Press, [2020] Series: Chapman & Hall/CRC biostatistics series Chapman & Hall/CRC biostatistics series Format/Description: Book 1 online resource (xix, 274 pages). Subjects: Medical mapping.

Geospatial Health Data: Modeling and Visualization with R ~ T1 - Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. AU - Moraga, Paula. PY - 2019/11/20. Y1 - 2019/11/20. M3 - Book. SN - 9780367357955. BT - Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. PB - Chapman and Hall/CRC. ER -

Geospatial health data : modeling and visualization with R ~ Get this from a library! Geospatial health data : modeling and visualization with R-INLA and Shiny. [Paula Moraga] -- "This book shows how to model disease risk and quantify risk factors using areal and geostatistical data. It also shows how to create interactive maps of disease risk and risk factors, and describes .

Geospatial Health Data / Modeling and Visualization with R ~ Modeling and Visualization with R-INLA and Shiny. Geospatial Health Data. DOI link for Geospatial Health Data. Geospatial Health Data book. Modeling and Visualization with R-INLA and Shiny. By Paula Moraga. Edition 1st Edition . First Published 2019 . eBook Published 21 November 2019 . Pub. location New York . Imprint Chapman and Hall/CRC . DOI .

Preface / Geospatial Health Data: Modeling and ~ Preface. 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. After a detailed introduction of geospatial data, the book shows how to develop Bayesian hierarchical models for disease mapping and apply computational approaches such as the .

Geospatial Health Data: Modeling and Visualization with R ~ 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

Chapter 13 Introduction to Shiny / Geospatial Health Data ~ Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny Chapter 13 Introduction to Shiny Shiny (Chang et al. 2019 ) is a web application framework for R that enables to build interactive web applications.

Chapter 12 Building a dashboard to visualize spatial data ~ Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny Chapter 12 Building a dashboard to visualize spatial data with flexdashboard Dashboards are tools for effective data visualization that help communicate information in an intuitive and insightful manner, and are essential to support data-driven decision making.

Chapter 8 Geostatistical data / Geospatial Health Data ~ Chapter 8 Geostatistical data. Geostatistical data are measurements about a spatially continuous phenomenon that have been collected at particular sites. This type of data may represent, for example, the disease risk measured using surveys at specific villages, the level of a pollutant recorded at several monitoring stations, and the density of mosquitoes responsible for disease transmission .

[PDF] Spatial And Spatio Temporal Bayesian Models With R ~ 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 .

Geospatial Health Data: Modeling and Visualization with R ~ Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (Chapman & Hall/CRC Biostatistics Series) eBook: Moraga, Paula: : Kindle Store

Areal data / Geospatial Health Data / Taylor & Francis Group ~ Geospatial Health Data. DOI link . Visualization with R-INLA and Shiny. Geospatial Health Data. DOI link for Geospatial Health Data. Geospatial Health Data book. Modeling and Visualization with R-INLA and Shiny. By Paula Moraga. Edition 1st Edition . First Published 2019 . eBook Published 21 November 2019 . Pub. location New York . Imprint .

Chapter 1 Geospatial health / Geospatial Health Data ~ 1.1 Geospatial health data. Health data provides information to identify public health problems and respond appropriately when they occur. This information is crucial to prevent and control a variety of health conditions such as infectious diseases, non-communicable diseases, injuries, and health-related behaviors.

References / Geospatial Health Data: Modeling and ~ Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny References Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2019.

Chapter 5 Areal data / Geospatial Health Data: Modeling ~ Chapter 5 Areal data. Areal or lattice data arise when a fixed domain is partitioned into a finite number of subregions at which outcomes are aggregated. Examples of areal data are the number of cancer cases in counties, the number of road accidents in provinces, and the proportion of people living in poverty in census tracts.

Geospatial Health Data: Modeling and Visualization with R ~ Achetez et téléchargez ebook Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (Chapman & Hall/CRC Biostatistics Series) (English Edition): Boutique Kindle - Infectious Disease :

Read Download Spatial And Spatio Temporal Bayesian Models ~ 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 .

Paula Moraga — the University of Bath's research portal ~ I am the author of the book Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny published by Chapman & Hall/CRC Biostatistics Series. Education/Academic qualification Biostatistics, Master in Science, Harvard University

Applied Spatial Data Analysis With R / Request PDF ~ Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Book. . and GIS (geographic information system) mapping and data visualization used the raster, sp, and ggplot2 packages .

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[PDF] Download Advanced R Chapman Hall Crc The R Series ~ By reading this book, you will learn: The difference between an object and its name, and why the distinction is important The important vector data structures, how they fit together, and how you can pull them apart using subsetting The fine details of functions and environments The condition system, which powers messages, warnings, and errors The powerful functional programming paradigm, which .

Spatial and Spatio-temporal Bayesian Models with R - INLA ~ Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (Chapman & Hall/CRC Biostatistics Series) Paula Moraga. 5.0 out of 5 stars 1. Kindle Edition. $73.52. Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability Book 135)