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Toronto Public Washroom Import - Lessons Learned

Do tallcoleman đăng vào 21 tháng 09 năm 2024 bằng English.

A project I worked on, the Toronto Public Washroom Import recently finished up and I thought it would be interesting to do a quick lessons learned:

Top three things that went well:

  • Having help from other people makes things much easier — I am grateful to RockTeam for helping with the changesets and for Jarek_Piórkowski and a couple members of the Civic Tech Toronto community for their input on the import plan.
  • Doing a couple “test” imports and recording an instruction video significantly improved the written import plan and instructions. It’s also very helpful to have mapped a couple of the relevant features via survey as well.
  • For the script that converted the City data to OSM tags, setting and validating assumptions about the City data using Pandera helped give me a lot of confidence that the output wouldn’t be affected by upstream changes to the City data.

Top three things I would change if I did it again:

  • Increased the time assumption for how long it would take to complete all the changesets. I had originally expected it would take two weeks at most, but it took closer to three (even with two people doing changesets).
  • For the initial data profiling, I might try using e.g. Tableau public, since it would be a little faster and easier than e.g. using .value_counts(dropna=False) in pandas.
  • Have the data transformation script estimate how many washrooms should be conflated vs. net new in each changeset - this would provide a simple comparable metric to ensure that the conflation plugin was configured correctly for each changeset.

Top three resources that were helpful:

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Vị trí: Yonge-Bay Corridor, University—Rosedale, Toronto, Golden Horseshoe, Ontario, Canada