I'm investigating the possibility of importing NSW Public Schools data <https://data.cese.nsw.gov.au/data/dataset/nsw-public-schools-master-dataset> into OpenStreetMap in line with the Import Guidelines <https://wiki.openstreetmap.org/wiki/Import/Guidelines>.
At this stage I'm seeking buy in from the local community as well as feedback on my plan, before taking it to the imports list. Please refrain from jumping the gun by importing this data before this review has been completed. The data is CC BY 4.0 licensed (although the link above says only CC BY, I've confirmed via email that it is CC BY 4.0) and the OSMF CC BY waiver has been completed <https://wiki.openstreetmap.org/wiki/File:CESE_NSW_AU_MasterSchools.pdf>. 1. Attribute Mapping Here is a sample of the attribute mapping I've applied, I'd appreciate any feedback on this. amenity=school access=private // Although generally on public land, access to public school grounds is similar to any other private property, you can be there by invitation and you can walk up to the front door but the owner/school can ask you to leave at any time. addr:city=Crows Nest // suburb addr:postcode=2065 capacity=927 // Based on enrolment numbers, although they aren't necessarily equal I think in lieu of better information it serves as a good placeholder contact:email=northsydbo-h.sch...@det.nsw.edu.au website=http://www.northsydbo-h.schools.nsw.edu.au // derived from the email, but looks fine contact:fax=+61 2 9957 6310 contact:phone=+61 2 9955 1565 fee=no Public schools don't have compulsory fees to attend, unlike most private schools. isced:level=2-3 Upstream uses Infants, Primary, Secondary which are mapped to the https://wiki.openstreetmap.org/wiki/Key:isced:level levels 0, 1, 2-3 respectively. grades=7-12 https://wiki.openstreetmap.org/wiki/Key:grades name=North Sydney Boys High School operator=NSW Department of Education // being part of the NSW Public Schools dataset implies they are operated by the NSW Department of Education which in tern implies they are public schools ref:au.gov.nsw.cese=8132 ref:au.gov=7614 // these references although not necessary might make it easier to keep data in sync with future upstream updates school:gender=male // mixed, male, female school:selective=yes // yes, no, partial school:specialty=comprehensive // agricultural, languages, arts, comprehensive (most) start_date=1915-01-01 A question is should we apply the source tag to the changeset or the object. What should we do with the existing source tag? If it's obvious it relates to the geometry I suggest it be moved to source:geometry, otherwise I'd suggest it be deleted (it's still there in the history). Though a specific source:name tag should be retained. I'm proposing to use on the changeset tag: source:url=https://data.cese.nsw.gov.au/data/dataset/nsw-pub lic-schools-master-dataset source=NSW CESE Public Schools Master Dataset Along with a comment pointing to this thread. 2. Import Plan To make importing the data easier, I've put together a basic web application at https://andrewharvey.github.io/au-nsw-public-schools-to-osm/diff.html. Based on matches identified by distance to nearest school within 200m you can see which tags will change. It uses the JOSM Remote Control to load the change into JOSM where the final upload(s) will take place. My import plan is to go through this and and apply the changes or edit them manually as necessary. In cases where tags conflict I plan to open changeset comments to ask the author to determine what to do. I plan to use an dedicated imports account. I plan to add the attribution to the Contributors page. Most public primary schools have interchangeable names "Foo Primary School" and "Foo Public School". If a different name is already tagged, I propose we move it to alt_name, that makes the names consistent, but also means that it might not match the name used on the ground. What do people think about this? All the code and cached versions of the data files are available at https://github.com/andrewharvey/au-nsw-public-schools-to-osm. Some stats... Total features from OSM: 2636 (1649 matched, 987 unmatched) Total features from Upstream: 2209 (1713 matched, 496 unmatched) Of most interest are those 496 features from the upstream dataset not found in OSM, but the other 1713 are still of interest as they add a lot of missing tags to the existing objects in OSM. I haven't yet looked through the "schools" we have in OSM but the dataset doesn't have, because the vast majority will be private schools, and not things we need to investigate further. I'm aware there are a number of "schools" in the upstream data we might not consider schools for OSM for example "Field of Mars Environmental Education Centre", "Royal National Park Environmental Education Centre" as the students attending here are likely on excursion. However given the wiki says "place where pupils, normally between the ages of about 6 and 18 are taught under the supervision of teachers.", I think we should include them. There are a number of schools for the disadvantaged, disabled, etc. and in hospital schools not currently mapped in OSM, importing this data means we can include more of these kinds of schools. Unfortunately they aren't tagged as such so they appear the same, but I still think it's better to have them than not.
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