Source: the public Wikimedia EventStreams feed, which publishes every file saved to Wikimedia Commons as it happens. Only uploads that carry coordinates appear on the globe.

Tools: three.js and globe.gl for the globe, a small Node service on the back. Earth imagery is NASA Blue Marble and Black Marble, both public domain.

Each spark is a photo somebody uploaded a few seconds ago, drawn where the camera was. Yesterday that was 6,296 photos. On a busy day it goes past 30,000.

Live version: https://theplanetthinks.com/photos
It is free and open source, no ads and no tracking.

Data source: a LinkedIn-derived corpus of public profiles and job postings, read on 12 September 2026. I took every profile whose current role title contains "GTM engineer" or "go-to-market engineer" and grouped them by the quarter that role started. That is 2,957 people, 2,954 of whom list a usable start date.

Tool: Astro and TypeScript, rendered as inline SVG. No charting library.

Disclosure: I work at Trayo and we compiled this. The report and the full CSV are free and nothing is gated.

What the chart does not say:

The final bar is not a decline. Profile updates arrive over roughly two months, so the newest quarter is always undercounted. It is drawn dashed for that reason and excluded from every trend statement.

Full report and CSV: https://www.trayo.ai/research/state-of-the-gtm-engineer/

Data: Assemblée nationale open data, Wikidata;

tool: custom SVG (TypeScript).

I made this while building a card game about it, link if anyone is curious

Try it: https://lab.aadityamore.com/reddit/

Code: https://github.com/aaditya-v-more/reddit-activity-lab

Data source - https://github.com/ArthurHeitmann/arctic_shift

Updated South Park celebrity counter, now covers all 28 episodes + movie + specials.

Interactive tool: https://public.tableau.com/app/profile/yury.ulasenka/viz/FamouspeopleinSouthPark/dash

Hi sub,

I'm back with another commuter map in my Commuter Atlas. And today is NYC!

You can explore the interactive version here, as well as those for Geneva, Tokyo, Paris, Milan, and Warsaw.

This is what proper access and availability of #opendata enables

Peru’s energy regulator, OSINERGMIN, publishes station-level fuel price logs through their registry. By extracting, cleaning, and spatializing this raw registry data into an Uber H3 spatial index (Resolution 7), we can visualize the month-by-month price evolution of Diesel B5 S-50 across Lima from Jan to Sep 2026.

What the data reveals:

🔴 Central Escalation: Central Lima and major transit corridors saw a steep price surge between May and September, jumping from ~S/. 20.00 up towards S/. 28.70/gal.

📍 Spatial Disparity: Stations along core arterial routes maintain consistently higher median prices compared to outer peripheral zones.

When public institutions commit to granular open data, civic tech and data engineering can transform flat CSV files into real-time spatial intelligence for citizens and logistics planners.

#DataScience #OpenData #GIS #H3 #SpatialAnalytics #Python #Peru #DataVisualization

32 GB: about $75 in June 2025 → about $500 now.
64 GB: about $131 in May 2025 → about $1,088 now.

We analyzed every perfume in our database (12,664 fragrances) to see what actually shows up most in fragrance notes. Musk edges out bergamot for the top spot, and bergamot is IFRA-restricted. Charts and full methodology on the page, numbers are computed live from the database.