this post was submitted on 06 May 2024
308 points (96.7% liked)

Technology

59223 readers
3383 users here now

This is a most excellent place for technology news and articles.


Our Rules


  1. Follow the lemmy.world rules.
  2. Only tech related content.
  3. Be excellent to each another!
  4. Mod approved content bots can post up to 10 articles per day.
  5. Threads asking for personal tech support may be deleted.
  6. Politics threads may be removed.
  7. No memes allowed as posts, OK to post as comments.
  8. Only approved bots from the list below, to ask if your bot can be added please contact us.
  9. Check for duplicates before posting, duplicates may be removed

Approved Bots


founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
[–] [email protected] 3 points 6 months ago

This is the best summary I could come up with:


The game is currently in the process of adding monsters from Scarlet and Violet, and that's where this story begins.

Two of the latest additions to the Pokémon Go roster are Wiglett and Wugtrio, riffs on the designs of Diglett and Dugtrio, who live on beaches and look kind of like garden eels.

OpenStreetMap contributors have discovered "beaches" that were actually located in residential backyards, golf courses, and sports fields.

Entire blog posts, wiki entries, and presentations from OSM mappers exist to bridge the knowledge gap, explaining the purpose of OpenStreetMap data to Pokémon Go users and breaking down Pokémon Go game mechanics for frustrated OSM contributors.

As that OSM blog post implies, not every user who discovers the OpenStreetMap project via Pokémon Go ends up messing with the data.

Though many users are "truth-stretching" vandals who create nonexistent parks, beaches, and footways to encourage specific Pokémon to spawn, others become "very careful, trustworthy" OSM users who "make many worthy additions to the map" by accurately mapping out places where OSM's data is patchy or outdated.


The original article contains 406 words, the summary contains 172 words. Saved 58%. I'm a bot and I'm open source!