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    • 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
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    • Chapter 12: Roadblocks on the way to causality: exposure pathways, aggregation and other sources of bias
    • Chapter 13: Better exposure measurements through better design
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Spatio-Temporal Methods in Environmental Epidemiology

Chapter 9 - From Points to Fields: Modelling
Environmental Hazards over Space

Summary

This chapter contains the basic theory for spatial processes and a number of approaches to modelling point-referenced
spatial data. From this chapter, the reader will have gained an understanding of the following topics:

  • Visualisation techniques needed for both exploring and analysing spatial data and communicating its features
    through the use of maps.
  • Exploring the underlying structure of spatial data and methods for characterising dependence over space.
  • Second-order theory for spatial processes including the covariance. The variogram for measuring spatial associations.
  • Stationarity and isotropy.
  • Methods for spatial prediction, using both classical methods (kriging) as well as modern methods (Bayesian kriging).
  • Non-stationarity fields.

  • R CODE

    Code for mapping with plotgooglemap

    Code for GGMapping

    example 9.1

    example 9.2

    example 9.3

    example 9.5

    example 9.6

    example 9.9

    example 9.10

    example 9.11

    example 9.12

    example 9.13

    Meuse River Site Map

    New York


    DATA

    Maximum Daily Temperature at California sites

    California Temperature sites MetaData

    New York Sites Data

    New York Sites MetaData (Site locations in lambert Projection Coordinates)

    • ©Gavin Shaddick and James V. Zidek 2015