Abstract
This chapter discusses spatial variation in risk. Epidemiological disease investigations should include an assessment of the spatial variation of disease risk, as this may provide important clues leading to causal explanations. The objective is to produce a map representation of the important spatial effects present in the data while simultaneously removing any distracting noise or extreme values. The resulting smoothed map should have increased precision without introducing significant bias. The method used to analyse the data depends on how they have been recorded. Smoothing based on kernel functions, smoothing based and on Bayesian models, and spatial interpolation are discussed.
| Original language | English |
|---|---|
| Title of host publication | Spatial Analysis in Epidemiology |
| Editors | Dirk U. Pfeiffer, Timothy P. Robinson, Mark Stevenson, Kim B. Stevens, David J. Rogers, Archie C. A. Clements |
| Publisher | Oxford University Press |
| Chapter | 6 |
| Pages | 67-80 |
| ISBN (Print) | 978-0-19-850988-2, 978–0–19–850989–9 |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
Research Keywords
- spatial variations
- spatial epidemiological analysis
- map representation
- spatial effects
- smoothing
- kernel functions
- Bayesian models
- spatial interpolation