Interactive map of the convenience store "turf war" in Japan

Hacker News

Technologies used: Leaflet (frontend) Turf (Geojson generation and Voronoi generation) I noticed that my neighborhood is all Lawsons, so I got the location of all Conbinis and ran some basic analysis to see if these pockets of brand territory are common. I haven't worked much with web frontends before, so feedback is welcomed. I also have some ideas to maybe expand upon, like making the territory calculations based on streets and other geographical features rather than just beeline distance. The site isn't tested too much on mobile yet, but should be ok. Currently the frontend code and geojson files can be found at the public repo: https://github.com/kikkia/ConbiniWars . I will upload the backend code soon as I am cleaning it up and reorganizing it.

Visit product View on Hacker NewsSep 23, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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