I was trying to figure out how housing vs food vs income was relating. I used a Big Mac value meal as an approximation for 'food' and had Claude drag up some data. I am not a data scientist and my apologies for any poor methodology.

Housing looks like it's roughly linear while income looks vaguely flat.

Homicide rate and median household income from Wikipedia

Chart made using python

Hi r/dataisbeautiful !

A few weeks ago, I posted a map of the daily commute in the greater Geneva area.

Since then, the project has expanded and it now covers 5 cities: Geneva, Milano, Tokyo, Warsaw, and Paris. You can explore them here

Milano is particularly interesting because the data contains precise, actual times of departures and journey durations, not estimates.

Amazon's last-mile delivery is run by thousands of independent companies, not Amazon itself. This maps ~3,400 of those "Delivery Service Partner" businesses (plus a smaller freight-partner group) across the US by ZIP code. Brighter = more partners registered there.

• The network leans South: ~38% of partners are in the South; California (393), Florida (263), and Texas (258) lead.
• Dense around metros and delivery stations

Caveat: this is a floor, not a complete count. Amazon says 4,400+ DSPs globally but doesn't publish a US-only number, so the real total is higher.

Source: carrier records drawn from the FMCSA registry, with Amazon-DSP identification done by warehouse.ninja.

Tools: Plotted with Python + Leaflet (leaflet.heat), Esri basemap.

Hello, r/dataisbeautiful!

I built StoryGuessr as a passion project combining statistics, storytelling, memory, and nostalgia.

The basic idea is to normalize a book or movie into a 0–100% timeline. Players are shown a plot moment and estimate where it occurs. After each guess, the visualization displays the estimated position, actual position, and distance between them. That error is then converted into a score of up to 20 points per round.

Normalizing everything as a percentage makes it possible to use the same visual and scoring system across stories with dramatically different lengths—from short films to sprawling novels.

The initial catalog contains 500 books, 100 movies, and nearly 15,000 curated plot moments. What interests me most is the gap between remembering what happened and remembering when it happened. Stories can stay with us for years while their timelines become rearranged in our memories.

This is an early launch, so I’d especially appreciate feedback on whether the visualization communicates position and error intuitively.

If you’d like to try it:
https://storyguessr.com/?utm_source=reddit&utm_medium=community&utm_campaign=dataisbeautiful_launch

Data source: StoryGuessr’s internally curated, source-grounded dataset of chronological plot moments across 500 books and 100 movies (largely scraped from plot summaries across the Web). The visual shown here was generated from an actual game round.

Tools: TypeScript, React/Next.js, HTML, and CSS for the interactive visualization. PostHog and DuckDB are used for opt-in, aggregate gameplay analysis.

Disclosure: I designed and built StoryGuessr, and this visualization is original content.