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Spatio-Temporal Methods in Environmental Epidemiology

Chapter 14 - NEW FRONTIERS

Summary

In this chapter the reader will have encountered a selection of new frontiers in spatio–temporal epidemiology including
the following:

  • A number of areas that are currently under active development.
  • Two modern approaches to addressing the problem of non-stationarity in random spatio–temporal fields; warping
    and dimension expansion.
  • How dimension expansion can be used to dramatically reduce non-stationarity and suggest its possible causes.
  • A powerful approach combining both physical and statistical modelling within a single framework.

  • R CODE

    Example 14.7


    DATA

    Illinois Ozone Data

    Shape files (ZIP folder)

    • ©Gavin Shaddick and James V. Zidek 2015
    • Chapter 1: Why spatio-temporal epidemiology?
    • Chapter 2: Modelling health risks
    • Chapter 3: The importance of uncertainty
    • Chapter 4: Embracing uncertainty: the Bayesian approach
    • Chapter 5: The Bayesian approach in practice
    • Chapter 6: Strategies for modelling
    • Chapter 7: Is `real' data always quite so real?
    • Chapter 8: Spatial patterns in disease
    • Chapter 9: From points to fields: Modelling environmental hazards over space
    • Chapter 10: Why time also matters
    • Chapter 11: The interplay between space and time in exposure assessment
    • Chapter 12: Roadblocks on the way to causality: exposure pathways, aggregation and other sources of bias
    • Chapter 13: Better exposure measurements through better design
    • Chapter 14: New frontiers
    • Statistical methods in epidemiology
    • Spatio-temporal Methods in epidemiology
    • Advanced statistical modelling in space and time
    • BUC1 (CIMAT) When populations and hazards collide: modelling exposures
      and health
    • BUC2 (UNAM) Thinking Globally: The Role of Big Data
    • Detecting Pattens in Space and Time (CMM)
    • BUC4 (Bath) New Frontiers: Advanced Modelling in Space and Time
    • Big Data in Environmental Research
    • Statistics and Data Science in Research: unlocking the power of your data
    • Bayesian Hierarchical Models
    • BUCX (UNAM) Quantifying the Health Impacts of Air Pollution
    • WinBUGS
    • INLA
    • EnviroStat