Hi Stephan,
That is very interesting, and I see interesting potential uses for that.
Does it work for any type of input vector (points, polygons, lines)?
I am willing to test it and give you feedback.
Some time ago I helped a friend develop something alike, but (unfortunately) he was working on ArcGIS, so although the plugin is free, the main software is paid. We wanted to translate it into a free platform but we didn’t have time to do that.
The rationale is the same, but it performs statistics form vector layers instead - area, counts, etc.
Take a look here:
https://github.com/LEEClab/MSBuffer
Best
Bernardo
Em sexta-feira, 24 de agosto de 2018 04:02:18 BRT, Stefan Blumentrath Stefan.Blumentrath@nina.no escreveu:
Hi Rich,
My application for the module is characterizing sampling sites in ecological research.
It is mainly a port of https://grass.osgeo.org/grass64/manuals/addons/v.what.rast.buffer.html to Python - with some enhancements like multiple buffers, DB output, tabulating classified raster data (e.g. land cover maps). The purpose is “to provide local environmental context to” a series of input geometries.
As mentioned, the module is not incredibly fast, but might help to replace users/researcher time with CPU hours…
So, what is appropriate, depends on how much data you throw at the module, your patience and how efficient your alternative workflows are…
Would be cool if you could share your experience, in case you try it…
Cheers
Stefan
-----Original Message-----
From: grass-user <grass-user-bounces@lists.osgeo.org> On Behalf Of Rich Shepard
Sent: torsdag 23. august 2018 23:20
To: grass-user grass-user (grass-user@lists.osgeo.org) <grass-user@lists.osgeo.org>
Subject: Re: [GRASS-user] New addon v.rast.bufferstats
On Thu, 23 Aug 2018, Stefan Blumentrath wrote:
I just uploaded a new addon “v.rast.bufferstats” that extracts
different raster statistics in multiple buffers around vector
geometries. It is looping over input geometries and thus not very
performant with lots of input geometries. But it can be convenient for
e.g. characterizing the surrounding of study sites…
Stefan,
This looks interesting. For what applications would it be appropriate?
Regards,
Rich
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