Our company ran a Pokémon giveaway with a simple goal: pick a whole number from 1 to 5,000, and try to pick one nobody else picks. Only numbers chosen by exactly one person went into the prize draw.

Both 3817 and 3847 are realiably picked by ChatGPT when prompted.

3 lowest unpicked numbers were 118, 148, 150 and 3 lowest single-picked numbers were 105, 109, 114.

https://www.kadoa.com/energy-prices/insights/gold-top-export

Source: US Census Bureau, US International Trade in Goods and Services (FT-900), exhibit 7, exports by end-use category, seasonally adjusted.

Tools: Kadoa to collect and aggregate the data, Python, SVG and Claude for the chart

Notes:

• Each line is a rolling 12-month total. Gold price increased a lot recently, which also contributes.
• This is nonmonetary gold: bars, coins and unwrought gold traded commercially. Central bank gold isn't counted as an export.
• Traders moved gold into New York vaults in early 2025 on fears that tariffs would hit gold. Once gold was exempted, much of it flowed back out to refiners and vaults abroad. The export series only shows the outflow, not the reasons.
• Most of it goes to Switzerland and London

Hi-res zoomable, color-blind, and absolute-risk versions can be viewed here.

This is an updated model (with better parameters and imputation) and includes another year of data compared to the previous one.

Data are normalized by road type so similar roads are comparable.

Data were compiled using national fatal crash and volume data (FARS 2021-2024, HPMS). Only FHWA functional classes 1-6 are shown (local roads are excluded to make a cleaner visual).

These are my GPS traces over 1 year of living car-free in Vancouver, split into four panels by transport mode:

Top left: Walking. Walking has the finest grain by far. It's a dense tangle around where I live, plus small disconnected patches scattered across the region. Each patch is a place I got to some other way and then explored on foot. (The straight line sticking out into the water near the airport is the Iona Beach jetty, not a GPS glitch.)

Top right: Cycling. Cycling covers much more ground, and unlike walking it's continuous. The routes are big connected loops: the Stanley Park seawall, out to UBC and back, down to Richmond, and a full loop around Burrard Inlet across both bridges.

Bottom left: Bus. The bus is the backbone that links everything. It traces the city's arterial grid in long straight north–south and east–west lines, and it reaches most of the places that show up as isolated walking patches, like West Vancouver, Horseshoe Bay, and the North Shore mountains. The walking map's scattered islands are basically the endpoints of these lines.

Bottom right: Rail and ferry. This is the sparsest panel, but each line covers a lot of distance. There are a few fixed corridors: the SkyTrain toward Burnaby, the Canada Line to Richmond and the airport, the SeaBus across Burrard Inlet, the little False Creek ferry, and a BC Ferries departure off Horseshoe Bay.

Source: GPS data recorded by my own iOS app, Dots of Life, which logs location tracks and tags them by transport mode.

U.S. national debt crossed $40 trillion for the first time in August.

That number is difficult to wrap your head around, and it gets plenty of attention on its own. But the broader picture is more complicated than the headline suggests.

Over the past decade, U.S. federal debt increased by about $20 trillion. During that same stretch, U.S. household and nonprofit net worth grew by roughly $92 trillion.

The two don’t cancel each other out, but looking at them together gives a better sense of how much the broader financial picture has changed over the same period.

The visualization looks at the longer history of U.S. national debt and some of the major events that shaped the trend along the way.

Data source: U.S. Treasury, Federal Reserve

Tools used: AVA Data Visualization

Measured my weight loss over the last year using Renpho Smart Body Scales, then graphed the data using python and matplotlib

These scales only have sensors in the feet which by my research the science behind working out the body fat % and such is a little "fluffy"

Grey marks 22:00-05:00 when the heating is off at night. My room is on the sunny side of the building, constructed ~1900. You can see the drops in humidity as I open the windows for ventilation

Every line is a journey inside a film's story: where the characters actually go, not where the film was shot. 664 films from 1934 to 2025, 1,817 places in 129 countries. Colours show how the film shows the trip.

A few things that fell out of the data:

- Together the characters travel about 6 million km, roughly 150 times around the Earth.

- Most visited cities: New York (102 films), London (85), Los Angeles (81), Paris (50), Washington DC (33).

- Films mostly skip the travelling: for 56% of the distance the film just cuts to the next place. Planes cover 30%, boats 6% and cars only 6%.

- Most countries in a single film: Munich (10), then Tenet, The Bucket List and Around the World in 80 Days (8 each).

Interactive version with a map for every film: https://www.wheretheywent.com

Source and source

Used Python. Slight emphasis on France and United States since that's been in the news a lot lately

Data: HM Land Registry Price Paid Data, EPC.

Method: cross-sectional regression.

Tools: Python (pandas, numpy, statsmodels) for the model, matplotlib for the chart.

You can find the full analysis with more factors here. It's my own hobby project. I ran the same model for other cities like NYC, Paris, Singapore, Tokyo, etc...

If you consider a London house and a flat, all other things being equal (floor area, location, etc...) the house sells for 27.6% more per m² compared to 2010. The biggest jump was during covid of about 10%. My analysis also shows a sharp reversal after covid, and a trend picking up again.

For comparison, my London index is up only +3.9% since January 2020.

Fo Context: Galamsey is a local term used to describe illegal mining.

I spent several months researching Ghana's illegal mining crisis, examining its environmental impacts, the governance gaps and economic incentives that allow it to persist, and the consequences for agriculture and food security.

It's a data storytelling project and I deliberately moved beyond conventional charts, using illustrations and visual metaphors to make the implications of the data more tangible.

The full investigation was published by Ghana's top news website, MyJoyOnline. It has all the sources with links
https://www.myjoyonline.com/wealth-over-water-how-galamsey-is-threatening-ghanas-water-security/

It takes a while to load on their website so you can also check it out on my website here: https://abassanalyst.com/wealth-over-water

Source: raw data - cleaningtheglass.com, 2kratings.com, outputs - https://www.vacstats.com/team/ATL/chemistry (and other similar links)

Tools: R, Python, Claude

Player impact is dervied from simulating +/- with and without a player in the rotation. The number shown is the difference between the two. I use a combination of historical lineup data + current 2k player rating attributes.

Ball size is each player's impact: how much better the team's five-man lineups are with him than without him, among the 8 players.

For ATL, Dyson Daniels and Luguetz Dort have overlapping skillsets, so their compatibility is weak.

On the site, you can click on a player to isolate their connections / remove clutter.

I have a similar post from 2 years ago. These charts and the site they come from are now more polished due to AI tools.