OpenStreetMap statistics that can be updated with GitHub actions

Hacker News

Hey there, I'm an OpenStreetMap enthusiast and I was curious what kind of impact the app I am using has (I use StreetComplete[1], an easy-to-use Android app to add missing information). I couldn't find data for all my questions and some were not up to date, but I found a few starting points on how to do it myself[2][3]. Soon I realized that many more interesting questions about OpenStreetMap in general could be answered with the data and I created a website ( https://piebro.github.io/openstreetmap-statistics/ ) to show them. Since I want the data to be up to date, I added a script to update everything easily and packed it in GitHub Actions so I can update the data with a few clicks every month. If you have questions or feedback, feel free to comment here or open an issue at the repo. [1] https://github.com/streetcomplete/StreetComplete [2] https://github.com/matkoniecz/StreetComplete_usage_changeset... [3] https://github.com/amandasaurus/2021-osm-street-complete-edi...

Visit product View on Hacker NewsNov 30, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, open · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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