[Osmf-talk] seeking feedback on the Welcome Mat

Christoph Hormann chris_hormann at gmx.de
Sat Jul 21 15:05:03 UTC 2018

On Friday 20 July 2018, Mikel Maron wrote:
> We're seeking your input on the Welcome
> MatĀ https://osmfoundation.github.io/welcome-mat/, a communications
> project to help organizations to familiarize themselves with OSM, the
> project, the community and especially to learn about our expectations
> and their responsibilities. The idea developed at the 2017 Board F2F
> (https://blog.openstreetmap.org/2017/06/27/summary-of-boards-face-to-
>face-meeting/) and has been slowly cooking since then
> (https://wiki.osmfoundation.org/wiki/Welcome_Mat_to_organisations)
> Many folks have contributed, and provided good review and feedback so
> farĀ  -- and we want more! 

Thanks for introducing this to the public discourse.

Want to mention two things in particular where broader input would be 

* 'Who uses OpenStreetMap?':


currently has a fairly extreme US and English language bias.  This 
should be a list of examples covering the diversity of OSM data use, 
both thematic and geographic.  I suggested to base this on the "one 
example per category per country" idea and it would be great if we 
could collect sufficiently many and sufficiently meaningful examples 
along these lines to make this work.  And yes, IMO this includes for 
example a small local business from South America or elsewhere with an 
interesting local application displacing a multi billion dollar 
international corporation that happens to use OSM based maps in their 

Maybe the various local communities can discuss what the most remarkable 
uses of OSM data they have locally and contribute the result.

The issue for that topic is:


* 'How good is OSM data?':


is currenly not sufficient to give the reader a useful impression of the 
quality of OSM data from diverse perspectives.  There is a lot of 
material - both community written and in the scientific literature - on 
this topic but it can be a bit overwhelming so it would be good to 
distill what is most meaningfull and understandable of this into a 
short list of annotated references.

This is essentially doing a bit of review work on existing data quality 
analysis and writing up the results in a compact and understandable 


Christoph Hormann

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