Philippe
A relatively simple approach:
You could slice the time and prepare kernel density maps for each time period.
For each of these time periods, perform K-function at different scales,
identify at which scales the clustering is the strongest (using L-function),
and use that as an input
Dear AI Geostats List,
Just looking for suggestions on how to backtransform the ordinary kriging
variance (SE) produced from kriging log10 observations.
Thanks for your help,
Robert
Hi
Some of my own thoughts on backtransforming the variance go as follows:
the backtransform for the variance in lognormal theory is exp{logarithmic
variance-1} times the square of the mean. In kriging this would adapt to
exp{logarithmic kriging variance-1} times the estimated value squared.
