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
BestBernardo
   Em sexta-feira, 24 de agosto de 2018 04:02:18 BRT, Stefan Blumentrath 
<[email protected]> 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 <[email protected]> On Behalf Of Rich Shepard
Sent: torsdag 23. august 2018 23:20
To: grass-user grass-user ([email protected]) 
<[email protected]>
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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