Diversity Quarterly Project/2018 Q2

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The first project, is toilets, specifically gender based marking. Where is a male, female or unisex toilets. In progress during April, May and June 2018.

Description of Problem

There are 207,000 public toilets amenity=toilets, in OSM, and only ~4% of them have any data on which genders can use them, namely the female=*, male=* and the lesser used unisex=* tag.

Get Involved

Mapping

How to map here

Finding Data to survey

Here are some Overpass Turbo queries to find data to map. Click the link, move the map to an area you want to look at, and press run (top left).

Then go out in the world, and check what the toilet is like, and update OSM as appropriate.

Double checking unisex=* tag

About 6 months ago a bug in JOSM's validator & auto fix was fixed, which suggested changing "female=yes male=yes" on toilets to "unisex=yes", which is actually quite different. It has been fixed and won't suggest that again. If you see a toilets with unisex=yes you should double check and fix it if needed.

Double check these toilets unisex=yes toilets, excluding those which have been changed since this project started.

Documentation

The wiki description for amenity=toilets etc should be double chekced and updated. Try to get OSM consensus on the unisex=* tag.

Presets

Can the presets of editors (iD? JOSM? Vespucci?) be updated to include toilet genderings?

iD

Old iD issue on this: https://github.com/openstreetmap/iD/issues/3954

StreetComplete

StreetComplete task for this? There is one or two 'quests' for wheelchair toilets & general toilets

Linking up with other sites/data

Link up and talk to other sites/projects/groups outside OSM who are doing similar things, e.g. https://www.refugerestrooms.org/

Previous proposal from RR

Statistics

Current tagging statistics

Total All objects tagged amenity=toilets, others show tag combinations. #: Number of objects with this tag. %: Percentage of all objects with this tag(s). Δ: (delta) Total change in the number since the start.

The date refers to the status at the start of the day.

Date (2018) Total female=* male=* unisex=* female=* + male=* female=* + male=* + unisex=*
# Δ # % Δ # % Δ # % Δ # % Δ # % Δ
Start (16 Apr) 207,282 9,291 4.48% 9,235 4.45% 17,454 8.42% 7,143 3.44% 714 0.34%
7 May 209,438 +2,156 9,519 4.55% +228 9,455 4.51% +220 18,129 8.66% +675 7,258 3.47% +115 774 .37% +60
6 May 209,322 +2,040 9,507 4.54% +216 9,444 4.51% +209 18,111 8.65% +657 7,247 3.46% +104 770 .37% +56
5 May 209,229 +1,947 9,499 4.54% +208 9,436 4.51% +201 18,092 8.65% +638 7,239 3.46% +96 768 .37% +5
4 May 209,150 +1,868 9,497 4.54% +206 9,434 4.51% +199 18,076 8.64% +622 7,238 3.46% +95 767 .37% +53
3 May 209,024 +1,742 9,486 4.54% +195 9,422 4.51% +187 18,034 8.63% +580 7,229 3.46% +86 765 .37% +51
2 May 208,910 +1,628 9,469 4.53% +178 9,405 4.5% +170 18,012 8.62% +558 7,215 3.45% +72 755 .36% +41
1 May 208,782 +1,500 9,441 4.52% +150 9,374 4.49% +139 17,987 8.62% +533 7,203 3.45% +60 753 .36% +39
30 Apr 208,675 +1,393 9,425 4.52% +134 9,359 4.48% +124 17,951 8.6% +497 7,191 3.45% +48 744 .36% +30
29 Apr 208,569 +1,287 9,407 4.51% +116 9,342 4.48% +107 17,933 8.6% +479 7,177 3.44% +34 739 .35% +25
28 Apr 208,502 +1,220 9,400 4.51% +109 9,338 4.48% +103 17,922 8.6% +468 7,171 3.44% +28 738 .35% +24
27 Apr 208,392 +1,110 9,388 4.5% +97 9,326 4.48% +91 17,879 8.58% +425 7,160 3.44% +17 726 .35% +12
26 Apr 208,382 +1,100 9,387 4.50% +96 9,325 4.47% +90 17,879 8.58% +425 7,159 3.44% +16 726 .35% +12

SQL query that produces the data. Here be dragons

psql -At -d osm -c "select '|-', to_char(now(), 'FMDD Mon'), to_char(total, 'FM999,999') as total, to_char(total-207282, ' FMSG999,999') as total_delta, to_char(f, 'FM999,999') as f, to_char((f*100.)/total, 'FM999.99%') as f_per, to_char(f-9291, ' FMSG999,999') as f_delta, to_char(m, 'FM999,999') as m, to_char((m*100.0)/total, 'FM99.99%') as m_per, to_char(m-9235, ' FMSG999,999') as m_delta, to_char(u, 'FM999,999'), to_char((u*100.0/total), 'FM999.99%') as u_per, to_char(u-17454, ' FMSG999,999') as u_delta, to_char(mf, 'FM999,999') as mf, to_char((mf*100.0)/total, 'FM999.99%') as mf_per, to_char(mf-7143, ' FMSG999,999') as mf_delta, to_char(mfu, 'FM999,999') as mfu, to_char((mfu*100.0)/total, 'FM999.99%') as mfu_per, to_char(mfu-714, ' FMSG999,999') as mfu_delta from (select count(*) as total, sum(case when u then 1 else 0 end) as u, sum(case when m then 1 else 0 end) as m, sum(case when f then 1 else 0 end) as f, sum(case when m and f then 1 else 0 end) as mf, sum(case when m and f and u then 1 else 0 end) as mfu from (select tags?'unisex' as u, tags?'male' as m, tags?'female' as f from planet_osm_point where amenity='toilets' union all select tags?'unisex' as u, tags?'male' as m, tags?'female' as f from planet_osm_polygon where amenity='toilets') as details) as summary;" | sed "s/|/ \n\r|/g"

Achievements

Please write down here anything that has been achieved in this project so far!

See also