Geospatial health data : modeling and visualization with R-INLA and Shiny
著者
書誌事項
Geospatial health data : modeling and visualization with R-INLA and Shiny
(Chapman & Hall/CRC biostatistics series)(A Chapman & Hall book)
CRC Press, c2020
- : hardback
大学図書館所蔵 全2件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references (p.265-272) and index
内容説明・目次
内容説明
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.
目次
1. Geospatial health. 2. Spatial data and R packages for mapping. 3. Bayesian inference and INLA. 4. The R-INLA package. 5. Areal data. 6. Spatial modeling of areal data. 7. Spatio-temporal modeling of areal data. 8. Geostatistical data. 9. Spatial modeling of geostatistical data. 10. Spatio-temporal modeling of geostatistical data. 11. Introduction to R Markdown. 12. Building a dashboard to visualize spatial data with flexdashboard. 13. Introduction to Shiny. 14. Interactive dashboards with flexdashboard and Shiny. 15. Building a Shiny app to upload and visualize spatio-temporal data. 16. Disease surveillance with SpatialEpiApp.
「Nielsen BookData」 より