Hi Jorge,
I am always in favour of more interpolation methods,
but could you explain a little more about the advantage
of your weight term versus that of classical IDW?
A bit of web searching for "Interpolation Distance Weight" or
"Exponential Interpolation" (i.e. related to GIS and spatial interpolation).
did not produce anything meaningful for me.
Could you point me to some literature where I can find more
information on this?
Please see also my comments/questions below.
Thanks,
Ben
----- "E. Jorge Tizado" <ejtiz...@ono.com> wrote:
> Hi
>
> A few lines permit add a new type of interpolation in v.surf.idw, the
>
> exponential interpolation similar to "Geographically Weighted
> Regression":
IDW is already a geographically weighted method, is it not?
And it also uses an exponential weight, so why would your version
be more similar to a regression model? Or do you mean that the
weight term is more similar to that used by GWR?
>
> weigth = exp( - distance^2 / bandwidth^2)
So do I understand right that the weight gets smaller
as the bandwidth gets larger (given constant distance)?
>
> I attach the diff files, the core is
>
> for (n = 0; n < nsearch; n++)
> {
> d = exp( - list[n].dist / bw );
Is list[n].dist already the squared distance?
And bw the squared bandwidth?
> sum1 += list[n].z * d;
> sum2 += d;
> }
>
> P.S. idw = interpolation distance weight, better than inverse distance
> weight
> to future more type of interpolations
>
> --
> E. Jorge Tizado
>
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