1. Statistical Significance: Tests for Spatial Randomness

2. Whether or not there are true geographical differences in risk, there will always be some geographical patterns apparent to the naked eye. As in all medical research, it is important to evaluate whether observed patterns/results are likely to be due to chance or not.

3. Breast Cancer Incidence, Relative Risks Age-Adjusted, Indirect Standardization

4. Brain Cancer Mortality, Children 1986-1995

5. Brain Cancer Mortality, Adults 1986-1995

6. Tests for Spatial Randomness Null Hypothesis: The risk of disease is the same in all parts of the map.

7. Covariate Adjustments For incidence and mortality analyses, it is important to adjust for age, and sometimes for other variables as well. This is done using indirect standardization, so that a covariate-adjusted expected number of cases are obtained for each census area. Can be used with any test for spatial randomness.

8. Tests for Spatial Randomness Global Clustering Tests Cluster Detection Tests Focused Tests Three Different Types: Complementary. Used for different purposes.

9. Global Clustering Tests Evaluates whether clustering exist as a global phenomena throughout the map, without pinpointing the location of specific clusters.

10. Global Clustering Tests Moran’s I, 1950 Mantel-Bailar’s Test, 1970 Cuzick-Edwards k-NN Test, 1990 Tango’s Maximized Excess Events Test, 2000 etc.

11. Cluster Detection Tests Determine the location and statistical significance of clusters without prior assumptions about their locations.

12. Cluster Detection Tests Turnbull’s CEPP, 1990 Spatial Scan Statistic, 1995 etc.

13. Focused Tests Determine whether there is a cluster around a pre-specified point or linear feature.

14. Focused Tests Fixed Cut-Off, Lyon et al. 1981 Isotonic Regression, Stone 1988 Lawson-Waller’s Score Test, 1992,93 Bithell’s Linear Rank Score Test, 1995 etc.