Hello,
The crux of the problem is the smoothing effect of kriging.
If you don't want to get artificial countour lines in your
map, you have 2 choices:
1. use stochastic simulation which generates maps that
are consistent with (reproduce) the variability of your data.
2. use a non-exact interpolator, that is filter the
noise at data locations. An alternative is to slightly
shift the interpolation grid so that no interpolation
grid node coincides with a sampled location.
Pierre
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On Fri, 14 Dec 2001, Martin Roseveare wrote:
> Chaosheng Zhang said
>
> "Another problem is when we carry out spatial interpolation, these values
> may
> produce artificial contour lines around these sampling locations, even
> though they can be smoothed. I don't think this is the realistic situation
> in the field."
>
> This sounds like the crux of the problem. You sampled data and within it you
> have discrete large values. You have confidence in the integrity of the data
> but don't accept that for these values to be genuine you must have all these
> 'artificial' contour lines. This suggests to me that you are expecting the
> data to behave so that these large values don't exist, yet you are saying
> they should be regarded as valid. Is your sampling at a high enough spatial
> resolution?
>
> If you were to sample another point right next to one of these large values
> would you expect another large value or a more 'normal' one? If you know the
> answer to that then you should be able to decide whether the large values
> are truly errors or simply unexpected but valid data. I would suggest the
> problem here lies with understanding the underlying spatial variation of the
> data set from which the samples were taken, rather than a problem of which
> process to apply to the sampled data.
>
> Just another way of looking at it!
>
> regards,
>
> Martin
>
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