Spatio–Temporal Methods in Environmental Epidemiology with R (Hardcover, 2 ed)

1. An overview of spatio-temporal epidemiology and knowledge discovery. 2. An introduction to modelling health risks and impacts. 3. The importance of uncertainty: assessment and quantification. 4. Extracting information from data. 5. Embracing uncertainty: the Bayesian approach. 6. Approaches to Bayesian computation. 7. Strategies for modelling. 8. The challenges of working with real-world data. 9. Spatial modelling: areal data. 10. Spatial modelling: point-referenced data. 11. Modelling temporal data: time series analysis and forecasting. 12. Bringing it all together: modelling exposures over space and time. 13. Causality: issues and challenges. 14. The quality of data: the importance of network design. 15. Further topics in spatio-temporal modelling.