This indicates Heather was the most polarizing candidate (which most voters either voted on top of the list or as last/not at all) while Joost was the most broadly accepted candidate (which most voters put on one of the upper places of their list).

The really interesting information would of course be the geographic distribution of the votes. Unfortunately this is not possible to determine of course.

Let me take the opportunity to thank you for your work. From what is visible from the outside you contributed quite a lot to a better transparency of the OSMF work during the past years which - independent of the help you provide within the the OSMF - is of direct benefit for the whole OSM community.

I imagine being the first person in the role of an administrative assistant on the OSMF can be both a blessing and a curse and for sure is not always easy and pleasant.

Ok - since we have the board elections these days and you list one of the candidates it is important to be as clear as possible not to lead to misunderstandings among the voters. Thanks.

For clarification: It seems the people listed have not actually actively contributed to the document shown in the sense they endorse it. It would be great if you could specifically clarify that.

Checking with other Czech mappers and the data provider is a good idea - free-to-use services do not generally mean they are free for data extraction like tracing in OSM.

Otherwise nice example of the usefulness of relief data for mapping.

Are you sure it is allowed to use this for mapping in OSM? Neither the WMS nor the info on the data set on

http://geoportal.cuzk.cz/(S(lhmhzafvgs5vuq3qgjkbphev))/Default.aspx?lng=EN&mode=TextMeta&side=vyskopis&metadataID=CZ-CUZK-DMR5G-V&head_tab=sekce-02-gp&menu=302

indicate this is available under a free license compatible to OSM.

Nice.

But don’t make the mistake to just map forests just because it is one of the pet landcovers that are shown earlier than the rest in OSM-Carto.

Some interesting observations can be made from these diagrams but i also find two things somewhat misleading.

First Graph 1 looks wrong since the x-axis looks like a time axis but in fact is not - as i understand this it is a combination of separate diagrams for every year with the x-axis indicating the month after first contribution.

Second considering the days of participation a measure of activity is problematic. In particular the very active weekend mapper mapping a lot during the whole day on weekends will have a lot less days of participation than the casual end-of-day mapper who maps a few things every evening to relax.

Is it just my impression or does Graph 4a indicate that contributions from veteran mappers (who started mapping several years ago) has increased quite a bit in 2017?

Good example for the usefulness of open data imagery for mapping in OSM.

The limiting factor in this area by the way is not the recording frequency - both Sentinel-2 and Landsat have newer images available than from September 7. But cloud cover during summer leads to most of the images not being useful.

And care should be taken to process the data into images well readable by the mappers so they can correctly interpret what they see. Here a rendering of the same image that probably better resembles the on-the-ground impression:

@RobJN - I think we can only agree to disagree. Everyone else can form their own opinion.

I give very little on who is shouting the loudest on the mailing lists but if you think that the mailing lists are less representative than a conference with at least several hundred, often probably more than thousand dollars participation threshold for most potential participants you have a really strange perception. But i understand last year’s craft mapper initiative is kind of inviting some compensatory action this year.

Robot mapping is as important as craft mapping based on local knowledge…

…says the majority of the the international OSM jetset visiting SotM - most of them either wealthy enough to pay for an international conference visit or being paid for their visit by their employer.

Most of the mappers forming the backbone of OSM do not post on the international mailing lists but many more of them and a much more representative selection of them than those who visit SotM.

Selling a survey among SotM visitors here as a representative survey of the OSM community is incredibly manipulative and tendentious.

I understand your concern here but as i think i said elsewhere before the solution to this is not to put presets in iD under supervision of some kind of committee or so but to ensure more diversity in presets - iD could then have the option to choose between a number of different presets maintained by different people and any of them makes a certain choice it would only affect the fraction of iD users that use these presets.

And in particular in areas where all of these as well as Bing and Mapbox only offer either clouds, snow or images from the stone age (in the US in particular Alaska - look for example in the Talkeetna Mountains or the Neacola Mountains) there is a whole lot of open data imagery available some of which you can also find in ID and other editors.

Only recently you mean the past 50 years? About the example of vending=condom vs vending=feminie_hygine it cannot be reduce to say that people do not seem to map stuff in their toilets, it is a clear example that the number and gender of OSM contributors do matter, and that things that are priority on women’s life are not well represented in the map.

Actually no, these numbers do not give you a clear indication of a bias within OSM in that regard. Unless you can proof that vending machines for feminie hygine articles are universally as frequent as vending machines for condoms the observed bias could be fully explained by bias outside of OSM. This does not mean there is no such bias in OSM but these numbers do not proof such a bias. It is a well known phenomenon that in OSM (or in any geodata collection for that matter) there is preferential treatment of common features over less common features. That is a problem of bias and discrimination in itself but it is not the same as gender/sex discrimination.

What would indeed be a very valuable thing to study is if there are systematically different preferences in what things people consider worthy or important to map between men and women. But this is extremely hard to properly study in a neutral, unbiased way - because if you take men and women who already map in OSM and study their mapping preferences this is obviously not representative for men and women in general.

By the way there is a fairly obvious geographic bias in mapping of vending=condoms: https://taginfo.openstreetmap.org/tags/vending=condoms#map

First of all: great idea, in a recent discussion on osmf-talk i suggested that there is a lot of room for improvements in the way OSM presents itself to scientists and i am glad you are starting an initiative along these lines.

Your main focus seems to be academic research where OSM is a subject of study - either as a community or as a database. Apart from that we should not forget that there is also academic research where OSM is used as either a data source (like for cartographic research or any kind of geo-sciences) without the data itself being a subject of study or as an infrastructure to enter data that has been recorded during field work. Both cases are generally of interest for us as well. For example we should think about how to better recruit scientists doing field work as mappers for OSM.

One type of research i think is generally valuable when done diligently is studies on the quality of OSM data in comparison to other data sources. This is however relatively unattractive for researchers.

One attractive but difficult topic that comes to mind is studying saturation effects and maintenance problems in well mapped areas. I mean looking at what happens in an area when mapping of certain type of feature reaches completeness (like buildings, roads, addresses). How editing activities change, if the data is kept up-to-date, if there is further improvement either semantically or in geometric accuracy and on what factors this all depends.

So in 2015 you estimate road mapping in OSM to be 90% complete. Now you have improved your methodology and have a revised estimation that it is 83% complete.

I would predict that in 1-2 years we will get yet another improvement in methods and you then predict about 75% completeness. ;-)

Seriously: The value of such research should not be measured in terms of the sophistication of the methods used but based on the actual predictive power of the estimations made. Most of the “90% and more” estimations for individual countries i would regard pretty useless (for countries like Niger this is quite clearly wrong). But the information which countries still miss a large portion of the road network is potentially quite useful.

To be fair it should also be pointed out that the level of completeness can actually decrease in reality as new roads are built/established. This can be quite significant as an influence especially in developing countries.

I could not find any definition what you consider a road. The distinction between roads and highway=track in OSM is for example often not so simple. Tagging something that is actually highway=service or highway=unclassified with surface=unpaved as highway=track is quite common in OSM.

Vielleicht solltest Du place=city dabei nicht weglassen - wobei das dann am Ende oft natürlich falsch getaggt it. Siehe http://overpass-turbo.eu/s/qSQ

Your survey description says:

However, once you have submitted your responses, your username will be replaced by your OSM personal ID number (openly available in OSM) to protect any inferred identity from your username.

You should be aware that resolving OSM user IDs to user names is trivial, you can for example do this using Pascal’s tools:

http://resultmaps.neis-one.org/osm-discussion-comments?uid=1138944

If participation is not anonymous anyway i wonder why you do not require an OSM login via OAuth to make sure every participant participates with her/his user account and not that of someone else.

Ok, but if your hypothesis is that crowdsourced geodata reproduces the bias in conventional geodata gathering to actually verify or falsify that you would need to know (a) what the nature of the conventional bias is and (b) what non-biased geodata looks like. Otherwise you’d end up with a relatively meaningless statement like “The Japanese do a lot of things in ways that are similar to the ways of the Americans”.