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Top-level
 @alan These are critical metrics in order to understand what is involved in maintaining #OpenStreetMap data. In the 2013-9 period we could manage maintenance through 1 hour mapping events before our pub meeting roughly ensuring systematic annual coverage of city centre POIs. It's harder to cover suburban POis in the same way. We could do with an explicit way of capturing "I've seen this POI, and it's still there" without bumping the version with a check_date tag a la @streetcomplete. 2/3 4 comments
 @SK53 Yes I can imagine that would be a problem for scaling up an analysis like this. My sample here is small enough, and I know the area well enough, that I was able to look at the changes individually and see that none of them were due to remote mapping. There were some manual typo fixes but that's inevitable, there is no way to check the name of a shop especially if it's not a brand (and most of these shops are not). @SK53 I think I have more or less reconciled myself to check_date, Everydoor also uses it. I had my doubts originally as it feels a little odd to have this kind of metadata in the tags. But I have found it has significant practical benefits when trying to systematically survey an area. I now realise that in the past I had been repeatedly visually confirming some shops while ignoring others. Even the side of the street I tend to walk down turned out to be more biased than I thought! @SK53 @streetcomplete Yes, I hope that once I get the current data (for an area a bit larger than I have shown here) into a fairly complete position I will be able to better monitor the rate of change in the future. Here the 2020 data was not as complete as I originally thought. One other complication I found was deciding which "amenity=" values to include to capture e.g. bars and restaurants but not drown the analysis in benches and parking spaces. |
Gregory's Server
@alan Remote mapping activities such as Maproulette challenges, typo fixing bots and tagfiddling, can artificially inflate the actual rate of change, and often add no new information. These can be difficult to identify if one is trying to analyse the underlying rate of change 3/3