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

[Albin Blaschka]

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 
> <http://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 
> <mailto: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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