[Qgis-user] How to Identify the best subdataset of a dataset of points which follow a canonical distribution ?

[Anita Graser]

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.roubeyrie at 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.blaschka at 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-Geo at 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 <vesnikos at 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
> >>
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> >
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>
>
>
> --
> Lionel Roubeyrie
> lionel.roubeyrie at gmail.com
> http://youarealegend.blogspot.com
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