- Hi Dean,
Average over a moving window can help you show how the mean (and variance)
changes with location... This may indicate that a stationary random function
model will not be appropriate for your data. You can also use indicator maps.
An approach to handle the "trend" is to use an estimation or simulation method
with locally varying mean, or to use a secondary variable as external drift.
IRF-k methods are also an alternative. Removing the trend and working with the
residuals carries some problems: you may end up overfitting the trend and
loosing all the correlation in your residuals.
You can look at almost any book in Geostatistics to find more details:
GSLIB: Geostatistical Software Library and User's Guide, by Deutsch and
Geostatistics for Natural Resources Evaluation, by Goovaerts (1997)
Geostatistics: Modeling Spatial Uncertainty, by Chiles and Delfiner (1999)
Hope this helps,
Julian Ortiz C., Ph.D.
>===== Original Message From Dean Monroe <monroea@...> =====say one variable of interest and two dimensions? In time series, one can plot
> How is the best to graphically show non-stationarity in spatial data with
realizations over time, what is the spatial analog?
> Second, what may a person do to correct non-stationarity? Again, in timeseries a common practice is to use first, second, etc differences.
>Oklahoma State University
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- dear elisa
I think it is possible to check the relation between your data and these are outside the study are.
you can investigate all the data using geostatistical analyst (semivariogram - covariance cloud), selecting after only the new data you can see if they are located in this diagramm (near the other point or father by them) and then you can decide if they are connected with your data or not.
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