BlockAtlas – a very detailed interactive map of US Population Density

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The US Census publishes block-by-block population & housing data[1], and I couldn't find an online map of this, so I built one. It also includes lots (10k+) of other indicators published from the Census' annual survey, aggregated to larger levels. Some queries that should work: - A zip code (should be fastest, shows block-by-block population density) - An address (block-level population density within the surrounding zip code) - A demographic/population/housing indicator (e.g. "$500k+ homes", "percent of people who drive to work" - shows US States and you can double-click to see more detail) - A comparison between two indicators (e.g. "median income vs homeownership rate") - this shows two maps which zoom & pan together You can also try to search for an indicator and a place, e.g. "population density by zip code in Los Angeles County" - this only works sometimes though, I'd like to made it more reliable. Searching these demographic indicators is tricky and I haven't totally solved it, but I think this works reasonably well. The search backend uses LLMs for query parsing, result selection, & embeddings, and uses postgres full text search + pgvector for retrieval. The app is hosted on CloudFlare Workers and mostly written in TypeScript. I posted this as a Show HN about a month ago, but am posting again because I've made some pretty substantial changes since then (block-by-block data and comparisons are new). [1] Specifically, population and occupied/vacant/total housing unit counts, with some randomness added by the Census for privacy

Visit product View on Hacker NewsAug 18, 2024

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

79%79% 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, using, code · Missing: mac, agents, macos

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

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, 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

46%46% 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 · Strong signals: active · Missing: arr, mrr, revenue

16%16% 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.

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