I'd be keen to see something like this too, if time permits I might even work on it myself (if I convince my colleagues it's worthwhile for our project).
J On Sun, Jul 27, 2008 at 2:30 AM, Andreas Philipp <[EMAIL PROTECTED]> wrote: > > I think Dimos does not look just for "normal" interpolation of a > variable just using the spatial information of the variable itself, but > for a procedure using additionally a multiple regression scheme of a set > of "independent" variables. As far as I know there is no tool yet to do > that easily within GRASS at the moment (please correct me if it is there > already). > > Because I was also thinking how to realise a regression based > interpolation scheme in GRASS and might be not the only one, I try to > describe the problem: > > Those independent variables can be elevation, lat and lon of the station > or relief parameters which have a stable average influence on the > spatial distribution of the target variable. > E.g. in order to interpolate temperature accurately you > calculate a regression coefficient from height-above-sea-level-values > onto the temperature-values (over the whole space and time domain). Then > you subtract the fitted temperature values from the observed > temperatures and receive a temperature anomaly field (the residuals) > which would be realised if no height differences would be given but only > the spatial temperature variation due to meteorological reasons. Then > these anomalies (residuals) are interpolated in space with splines or > whatever onto a regular grid. Afterwards this grid is scaled to height > again by using the regression coefficients, et voila. This takes into > account that the target variable is not only dependend of spatial > autocorrelation (interpolated temperature of a mountain is then not as > high as the station record in the nearby valley). This is a procedure > which is commonly recommended in climatology. > However, in order to calculate the regression coefficients, longer time > series should be used (not only the time slice or map which is intended > to be interpolated) in order to get the pure average dependence of > temperature to elevation. Further it might be necessary to do the whole > analysis for each month of the year separately in order to account for > effects of large scale climatological differences in the annual cycle. > > Instead of elevation only also latitude, longitude and other parameters > can be used in a multiple regression scheme. An especially interesting > independent parameter is a typical relief type of the surrounding of > each gridpoint (achieved by principal component analysis of the > neighbour grid point heights in a moving window. May be to be plugged in > into r.li?). > > If I'm right this is not easy to do within GRASS at this time. May be R > can be used as a work around? Anyone has an idea? > I think it would be great to have such a tool originally in GRASS (I'm > sure a lot of climatologists would grep for GRASS just for that feature) > but it is a bit of work to program it (I think the main problem would be > the multiple regression, if only there was a fortran90 interface ...). > > However to use a single independent variable may be r.mapcalc could be > used if the vector point observations are transformed to raster maps > (bivariate regression is rather simple even if quite a few raster maps > are neccessary). > > Hm, quite a long mail, but may be some others are interested and willing > to solve ... > Please correct me if I'm missing something. > > Andreas > > > Am Freitag, den 25.07.2008, 23:35 -0700 schrieb Dimos: >> Searching at the Grass online manual, I have found two >> modules that are relevant to my question: >> >> 1)v.surf.rst >> 2)v.vol.rst >> >> So, maybe these could be used to create a 1km climate >> surface with independent variables of Lat, Long and >> Elev? (v.vol.rst needs similar input on the 3D x,y,z >> files) >> >> Thanks, Dimos >> >> >> --- Dimos <[EMAIL PROTECTED]> wrote: >> >> > Dear Grass Users, >> > >> > How to do thin plate spline interpolation with Grass >> > GIS (ex. Anusplin >> > >> http://fennerschool.anu.edu.au/publications/software/anusplin.php) >> > using: >> > >> > -Lat, long and elevation as the 3 independent >> > variables >> > -Temperature or precipitation as dependent variable >> > -1 KM elevation grid >> > -Irregularly spaced 30 year climate records (POINT >> > DATA) >> > >> > To create a: 1 KM resolution climate surface for >> > each >> > dependent variable >> > >> > Thanks, >> > >> > Dimos >> > >> >> _______________________________________________ >> grass-user mailing list >> [email protected] >> http://lists.osgeo.org/mailman/listinfo/grass-user > > > _______________________________________________ > grass-user mailing list > [email protected] > http://lists.osgeo.org/mailman/listinfo/grass-user > _______________________________________________ grass-user mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
