On 16-03-22 16:46:03, Christoph Hormann, wrote 1.5K characters saying:
On Tuesday 22 March 2016, Frank Villaro-Dixon wrote:
Can you give me an example ?
I probably could (after all i am on record for saying waterbody mapping
in OSM is a practical case of the infinite monkey theorem) but right
now i don't have the time to look for a good real world example.
In principle many cases where the way you'd remove a tag from has
additional tags the meaning of those tags will change when you remove a
tag.
For example ? I'm only focusing on natural= and water=
Also cases where the way in question forms the division between two water
areas you will usually run into trouble.
No, because they are part of a relation multipolygon.
On a principal level removing an ambiguity will always mean you remove
possible interpretations of the data and your bot simply cannot know if
the interpretations it removes are actually incorrect and the remaining
interpretation is the correct one.
No,
If you have a lake with multiple ways grouped with a multipoly as shown:
R [natural=water water=lake]
W [water=lake intermittent=yes]
W [intermittent=yes]
W [natural=water]
It will be transformed as:
R [natural=water water=lake]
W [intermittent=yes]
W [intermittent=yes]
W []
Where's the other possible interpretation here ?
This algorithm won't loose any information. It's merely deleting
duplicates, so where is the problem ?
You are only looking at it from a formal side and based on a specific,
subjective interpretation of tagging rules which is not appropriate.
See also
http://wiki.openstreetmap.org/wiki/Automated_Edits_code_of_conduct
As others have said the best way to improve waterbody data in OSM is by
actually doing mapping work and correcting errors based on local
knowledge or imagery. You can believe me when i say that factual
errors and inaccuracies are much more common and harmful problems
w.r.t. water area mapping than basic tagging inconsistencies.
Try exporting all waterbodies them; you'll see that in the actual state,
it's a royal pain in the ass. It's a shame because the quality of OSM is
good, but it's really hard to work with this data…
--
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