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    • 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
  • Courses
    • 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
    • Environmental Health Impact Assessment using Rv
  • Computing resources
    • WinBUGS
    • INLA
    • EnviroStat
  • Book's webpage @ CRC

Spatio-Temporal Methods in Environmental Epidemiology

Big Data in Environmental Research

course outline

The following are the online resources for the course entitled 'Big Data in Environmental Research', which is part of Pre-World
Congress Meeting of New Researchers in Statistics and Probability.

This course will be delivered at the The Fields Institute for Research in Mathematical Sciences, Toronto, Canada on 7th July 2016.

The slides, problem sheets, solutions and other relevant resources can be found below.

Slides

Course Slides


Lab Sessions

Lab Sheet

Lab Sheet Solutions


Data

Data.zip


Required R Packages

INLA

CARBayes

spdep

shapefiles

rgdal

gcmr

maptools

classInt

lattice

sp

Matrix

  • ©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
  • Environmental Health Impact Assessment using Rv
  • WinBUGS
  • INLA
  • EnviroStat