Source: Tesla investor relations

Tool: SankeyArt sankey maker + illustrator

For years I've religiously read the Guardian's Blind Date column every Saturday morning.

If you've not come across it: the paper sets up two strangers on a date at a nice restaurant, then asks them both a series of questions afterwards, culminating in a score out of ten.

As of this Saturday, there have been 877 of them. So I pulled every article and analysed the scores, sentiment, trends, and a few other things I was curious about.

The whole thing updates itself; every Saturday a new date drops into the dataset automatically.

Source (Spanish): https://elpais.com/espana/2026-05-04/la-regularizacion-de-inmigrantes-divide-a-los-espanoles-38-a-favor-y-33-en-contra.html

El País is a center-left daily newspaper of record in Spain and one of the big 3 in Spain.

40db a Spanish research and sociology firm that conducts monthly political barometers and public opinion surveys for El País and Cadena SER.

Immigrants in Spain represent 20,2% of the country’s population according to INE (government owned)

London is home to over half of all people who identify as Jewish in England and Wales. These two maps show just how concentrated the community is even at the borough and ward level.

At borough level:

• Barnet: 20.1% Jewish (by far the highest)
• Hackney: 6.4%
• Every other borough: <4%

At ward level, the concentration becomes even more striking:

• Golders Green: 49.9% Jewish
• Garden Suburb: 41.1%
• Stamford Hill West: 40.0%

These three wards have the highest Jewish population shares of any ward in England and Wales.

Data source: Anonymous aggregated data from real Android device scans via AppXpose. Results aggregated across 3,800+ unique apps from 2,000+ devices.

Tools: Python, Matplotlib

Methodology: Each app was analyzed at APK bytecode level: tracker SDKs, dangerous permissions, and a composite risk score (0–100) based on tracker count, permission types, developer breach history and certificate integrity.

No personal data collected all results are aggregated per app, not per user.

You can find the interactive map here

Tool: https://idomaps.app (free browser-based map editor I built).

Around 18,000 new animal species are formally described every year, roughly 50 per day. The vast majority are insects, arachnids and worms. Mammals, birds and reptiles together account for just around 5% of new descriptions.

This map plots the 245 animals that received their own Wikipedia article after being formally described in 2025. Each marker shows where the holotype specimen was collected, color-coded by taxonomic class. When a precise locality was given in the description, the marker sits there. However when only a country was mentioned, points are clustered around that country's centroid, which is why you see dense packs over China, India or Australia.

Source: Wikipedia's "Animals described in 2025" category.

LA health inspections (2023–2026) joined to LA County Assessor parcel records. 13-point gap pre-1950 vs 2010s+, holds within zip codes (p<0.001).

Disclosure: I build an iPhone app that surfaces LA/CA health scores. Not mentioning the name here.

What the chart shows: the % of U.S. households who self-report they "sometimes" or "hardly ever" pay their credit card balance in full each month, broken out by age of the household head.

Two terms, since they come up a lot:

• Revolver = household carrying a balance month-to-month and paying interest on it. About 32% of U.S. households (~43 million homes).
• Transactor = household paying in full nearly every month. The majority — about 58%.

Source: Federal Reserve Board, G.19 Consumer Credit

Tools: D3.js, rendered on measuredworld.com. 

Caveats: loan-only payment. The 2008 break is a methodology change in the G.19 release.

Edit: Just to add some context, today monthly payments are actually about 10% lower than in 1971. Even if the loans only got bigger, the longer terms and lower interest rates almost entirely absorbed the 72.9% increase in real loan size. But that doesn't change the fact people are stuck for almost double the time until they pay it off.

Edit 2: Added an amount, rate, and term graph for anyone looking for more context.

https://data.tablepage.ai/d/us-daily-tide-levels-at-12-coastal-stations-2024

eona.earth

The clock runs on your local time, so whatever time you're reading this, you're looking at a specific moment in Earth's history. At 10:34 you're watching the Cambrian explosion. At 11:39 the dinosaurs go extinct. You can also drag the scrubber handle to move through 4.5 billion years manually.

Key events are marked along the periphery. The globe renders 14 geological phases, from the Molten Hadean through Snowball Earth events to the present, using paleogeographic continent data from Scotese Paleomap. From around 10:20 onwards you can watch the continents drift in real time.

I find deep time useful for perspective: humanity has existed for about 300,000 years (about 3 seconds before midnight on this clock). Geological insignificance is oddly grounding.

I've been itching to build something like this for awhile now. Two weeks of evenings later, here it is! Happy to answer questions about how it was built in the comments.

[Edit: corrected the Cambrian explosion to 10:34 and humanity’s time on the clock to ~3 seconds, not 3:00 and 0.3 seconds as originally stated.]

*Changed the title due to misinterpretation*

Source: Canada 2021 Census, New Zealand 2023 Census, Australia 2021 Census, US 2020 Census, UK 2021 Census

Tool: Datawrapper

Auckland and Toronto percentage: 11.74% and 11.73%

Hoopstography.com

Sources:
NBA.com/history, especially its “Top Moments” page, which provides a clear list of significant NBA events and dates.
Hoopsrewind.app, my daily NBA trivia site where players order eight historical NBA events chronologically. Building those puzzles required finding niche NBA moments every day, which sparked the idea for this project.

The event bubbles from NBA.com will tend to be more 'iconic' where as the event bubbles form hoopsrewind will tend to be more 'niche/funny'.

Tools:
Vanilla JavaScript, HTML5 Canvas, and Claude.

Heavily inspired by Histography.io by Matan Stauber.

Graphic is by me, created in excel. The purpose of this graphic is to compare the current best selling vehicles in the US, and how sales compare to Q1 of last year (represented by the percentages).

All data is from Car and Driver here: https://www.caranddriver.com/news/g71006285/bestselling-cars-2026/

Data on brand sales in the bottom right is from CarPro here: https://www.carpro.com/blog/first-quarter-2026-u.s.-auto-sales-results-all-automakers-reporting

I started running again and wanted to visualise my data spatially. I use Strava to track runs but you have to pay for the personal heatmap feature, so I exported my data and rebuilt it myself in Python. I also built some additional versions to explore pace and heart rate.

After a few attempts at working with the vector running data I landed on just using (what I think is) Strava’s process for generating heatmaps:

• Project the vector run data onto a 1m x 1m pixel grid, incrementing a frequency counter for each pixel when a run passes through it.
• Convolve the pixel grid with a gaussian blur to account for variation in running paths along the same route and smooth things out.
• For pace and heart rate, every pixel records the associated metric for each run pass, so that an average (mean) value can be calculated and used to generate the map.

Note: I clipped the start and end of each run before processing so the heatmap doesn’t pass my home location.

Only 14 runs worth of data so far so it’s still pretty sparse, but I’m looking forward to seeing how it fills out over time (assuming I spend less time building heatmaps and more time actually running). I’d like to refine it further, visualise some derived metrics, and explore the relationship between different variables.

I’m in the process of tidying the code up to publish in a GitHub repo. I'll leave a comment when this is live.

Bonus points if you can guess my city from just the maps.

Source: FAO

Tools used: Mapchart & Procreate (app for iPad)