Hi Lionel, Those are good approaches. I think the problem is that Nikos doesn't have any measurement data yet (because he has to pay for every single station). So in this case, I don't think these methods can be used.
Best wishes, Anita On Tue, Jan 25, 2011 at 2:39 PM, Lionel Roubeyrie < lionel.roubey...@gmail.com> wrote: > Hello, > in first approch you can compute the general mean of your observations > and select only stations with rain mesureaments failing inside a > determinated tolerance around that mean. Another common use would be > to randomly remove some stations and look at the resulting variance > against the general variance (with all stations) and stop when a > tolerance is reached. The best approach would be to play with kriging > your observations and look at the resulting map when some stations are > removed. > Cheers > > 2011/1/25 Albin Blaschka <albin.blasc...@standortsanalyse.net>: > > > > Hello, > > > > another idea, unfortunately no solution, would be to ask at the r-sig-geo > > Mailinglist[1] - a list on the statistic-package R, which can be used > > directly in QGIS via the ManageR-Plugin...if you know a little bit of > > statistics and R... but could also be a little bit of overdoing if you > have > > never worked with R before... > > > > Maybe an idea... > > Albin > > > > [1] > > > > r-sig-...@r-project.org > > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > > > > > > Am 25.01.2011 12:52, schrieb Anita Graser: > > > > Hi Nikos, > > > > I think this would be a perfect question for gis.stackexchange.com > > A manual approach (135 stations is not that much) would be to decide on a > > maximum distance between stations (depends on your analysis requirements > I > > guess) and then buffer the station points accordingly. You can then > manually > > remove points with redundant/overlapping geographic coverage. > > But I'm sure there are better solutions :) > > Best wishes, > > Anita > > On Tue, Jan 25, 2011 at 12:09 PM, nikos <vesni...@gmail.com> wrote: > >> > >> Hello List, > >> > >> > >> I have an interesting problem, and I'd really appreciate if you could > >> give a couple insightful tips on how you'd solve the following: > >> > >> I want to order some meteo (rain mm/month for PSDI) data from my > >> corresponding national agency, but in their infinitive wisdom they > >> choose to place their weather stations in a pattern that follows > >> anything but a canonical distribution. > >> > >> I know this because they sent me a shp file with all their 136 stations > >> which are available to the general public - for a fee of couse. > >> > >> Now my problems arise on how I choose which possible combination of my > >> dataset of stations correspond to a canonical distribution of the > >> mainland - keeping the set of the stations to a minimum as with each > >> station the cost goes up? > >> I want to be as much efficient one can be using GIS technology ;) > >> > >> > >> My initial thinking is to find a subdataset which have their voronoi > >> polygons created with the same area. But im not so sure if thats the > >> correct approach > >> > >> Any tips on how to solve this problem is greatly appreciated! > >> > >> > >> > >> > >> Ves Nikos > >> > >> _______________________________________________ > >> Qgis-user mailing list > >> Qgis-user@lists.osgeo.org > >> http://lists.osgeo.org/mailman/listinfo/qgis-user > > > > > > _______________________________________________ > > Qgis-user mailing list > > Qgis-user@lists.osgeo.org > > http://lists.osgeo.org/mailman/listinfo/qgis-user > > > > > > _______________________________________________ > > Qgis-user mailing list > > Qgis-user@lists.osgeo.org > > http://lists.osgeo.org/mailman/listinfo/qgis-user > > > > > > > > -- > Lionel Roubeyrie > lionel.roubey...@gmail.com > http://youarealegend.blogspot.com > _______________________________________________ > Qgis-user mailing list > Qgis-user@lists.osgeo.org > http://lists.osgeo.org/mailman/listinfo/qgis-user >
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