1. Introduction
2. Original ILL breast cancer mortality rates (-9: missing)
3. ILL breast cancer mortality rates contour
4. ILL breast cancer mortality rates circle plot
5. To find out whether there is trend and decide whether to use ordinary or universal kriging accordingly, we check the perspective plot
6. To test for normality, check QQ plot
7. variogram cloud
8. sample variogram
9. To apply kriging method, we'd like to check whether the underlying process is isotropic , i.e., the variogram cloud at different directions are similar, 'cause then theoretical variogram model can be handled easily. We achieve that by viewing variogram plot at 4 ranges of angles.
10. Theoretical (spherical) variogram fitted according to sample variograms with Splus, where the parameters are found to be: range=8, sill=0.35, nugget= 2.8
11. Globally Krigged ILL breast cancer mortality rates , by which I mean the rate is considered distributed all over ILL (instead of at counties (centers) only); standard error surface ; and 95% confidence surfaces.
12. Krigged mortality rates where there are no observed values, essentially, an imputation with kriging
13. Finally, graphical display of krigged ILL breast cancer mortality rates
14. Discussions:
    1. Another type of kriging: noiseless, not available in Splus
    2. Some doubt on Normality: Transformation might give better results, but adjustment is needed to ensure unbiasedness.
    3. Or even better, Data essentially (irregular) lattice discrete data (counts). Therefore,
       1. Conditional Auto Poission model (ref: (6.5.6) (6.6.7) of Cressie 1993).
       2. Hierarchical Poisson model: Underlying mortality rate \lambda(s) is a continuous process, given which as parameters, the counts independently follow Poisson distribution.

The data and scripts used to produce these Splus analyses are available.
The postscript report.