AI-GEOSTATS: Generating an autocorrelated random error field.
- Hi everybody
I'm writing a diploma thesis about error propagation in digital terrain
models and I want to use monte carlo methods to simulate elevation errors
in the data points and their effect on various gis operations.
My data are irregularly distributed points (not grid data!) which I
triangulated using java as programming language. I found lots of
suggestions in literature how to simulate autocorrelated error fields
(Heuvelink, Ehlschlaeger, Goodchild, Wechsler,
Haining/Griffith/Bennet) but as far as I was able to understand them the
only practicable two (for my task) where:
1) Generating uncorrelated random field and swapping until a
level of autocorrelation (Moran's I) is reached. (Goodchild, 1980)
2) Same as 1) but prior to swapping the random numbers have to pass a
series of statistical tests like a test for
multivariate-normality. (Haining, Griffith, Bennet, 1983)
Am I right saying that I need measured errors at some points if i want to
apply interpolation techniques to simulate an autocorrelated error
field? (Ehlschlaeger (1994) used a formula depending on the spatial
autocorrelative effect) I'm asking that, because the only thing I have is
the RMSE which is 0.4 m. I haven't got the points from which this error
was empirically derived. But I could randomly set a few start points and
derive all other points from them.
So my first question is:
What technique shall I apply to simulate an autocorrelated random error
My second set of questions is:
How can I determine suitable parameters for the error fields
autocorrelation? What is the minimum distance of spatial independence? How
should I determine a suitable distance decay exponent if I haven't got any
sample error points to estimate a variogram from?
I hope my questions are not too stupid and thanks for any help!
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