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Mapipedia: Platform to show your data on maps and sharing with others

Hacker News

Mapipedia: Platform to show your data on maps and sharing with others

Mapipedia is a web platform I've created to make it easy for people to share goespatial time series data and display it on animated maps. There's also a social media component that allows you to write comments, follow, share and like data sets. You can also download the CSV data and embed animations into your own websites. I initially posted about Mapipedia in March 2019. I've taken a lot of the feedback on board and done a major overhaul of the site as well adding a lot of new features. I'd appreciate some feedback on overall look and feel (on mobile and desktop - there were problems on mobile previously). However it is best viewed in Chrome on desktops becuase it looks better on larger screens. I've also had some people say the website is blocked but have not been able to figure out why. If you have any thoughts on that it would be appreciated. The site also crashed last time I posted here so I'm wanting to test changes for robustness as well. The main home page is https://mapipedia.com Here are some links to try (press Play to start the animation): https://mapipedia.com/s/u/drdave/average_life_expectancy_by_country_since_1800.html https://mapipedia.com/s/u/drdave/forex_comparison_history.html https://mapipedia.com/s/u/drdave/united_states_of_america_formation.html https://mapipedia.com/s/u/drdave/seven_wonders_of_the_ancient_world.html https://mapipedia.com/s/u/drdave/captain_james_cook.html (Cook's first voyage sailed him around the world) https://mapipedia.com/s/u/drdave/nobel_prizes.html https://mapipedia.com/s/u/drdave/fall_migration_of_white_storks_in_2014.html Thanks for your help! All feedback is appreciated. Cheers David PS Feel free to email me at dnphilpot@hotmail.com

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
78%78% 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: new, email · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
57%57% 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
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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.

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