243 country and territorial flags, sorted and animated by visual features like colors, bands, symbols and crosses..

Hello! I analyzed % of net revenues coming from smokeless products from the largest tobacco producers in the world ("Big Tobacco"). JTI was excluded as I couldn't find full time series for them but it's around 4% currently.

Sources: Publicly available annual and quarterly reports of Big Tobacco companies

Tools: Excel, PowerPoint

For years, I saw tobacco as an industry slowly burning out. Fewer people were smoking, taxes and restrictions kept growing, and young people were turning away from cigarettes.

Then three things happened.

First came IQOS: heated tobacco in sleek devices, sold in polished, Apple-like stores.
Then vaping exploded, first with JUUL in the US and then with cheap, colourful disposables.
Finally, nicotine pouches quietly took over offices and campuses, one lip at a time.

Turns out, reports of tobacco industry’s death were greatly exaggerated.

But not every tobacco giant is making the switch to smoke-free at the same speed. To see who’s leading and why, I analyzed the annual reports of four of the largest listed players* (“Big Tobacco”).

Here are key takeaways:

1️⃣ PMI is leading the smokeless transition

First, it launched IQOS heated tobacco and marketed it as a cleaner, more elegant alternative to cigarettes.

Then, it bought the owner of ZYN, the leading US pouch brand. Pouches took off because they’re invisible: no smoke, no smell, no device.

PMI moved early, picked the right products, and built one advantage while buying the other.

2️⃣ BAT is catching up, but still far behind

BAT bet on cigarettes for longer. Today about 20% of its revenue is smokeless, half of PMI’s share.

It paid $49bn for Reynolds American (mostly US cigarettes) in 2017 then wrote down ~$35bn.

glo never caught up with IQOS, and Vuse was hurt by illegal disposables, though it’s now recovering.

Velo is the bright spot: its US pouch revenue more than tripled in H1 2026.

3️⃣ Altria and Imperial are lagging behind

Both have grown their smoke-free share since 2017, but slowly: Altria from 10% to 13%, Imperial from 1% to 5%.

Altria bet big and lost almost every time: JUUL ($12.8bn in 2018, worth $250m four years later), IQOS (US rights handed back to PMI after a patent ruling) and NJOY (main vape banned for infringing JUUL’s patents). Limited to the US, it can’t grow abroad while waiting on slow FDA approvals.

Imperial launched vapes and heated tobacco in many markets at once, then retreated when sales disappointed: out of Japanese heated tobacco in 2021, now out of US vaping. It chose cigarette cash and shareholder returns over big smoke-free bets, and has no leading brand in any category.

𝐖𝐡𝐚𝐭’𝐬 𝐧𝐞𝐱𝐭?

The biggest threat to the industry is regulation catching up. The EU wants to set union-wide minimum taxes on vapes, heated tobacco and pouches, Poland plans to ban disposable vapes and flavoured pouches, US authorizations can take years, and cheap illegal vapes keep eating into sales.

How far regulators go will depend on the health evidence. “Smoke-free” doesn’t mean harmless: nicotine is addictive and carries risks for the heart. 

The WHO says heated tobacco hasn’t been shown to lower health risk.

UK health authorities say vapes are much less harmful than smoking for those who switch completely, but not risk-free.

And pouches, the fastest-growing category, are too new for long-term studies.

The cigarette may be fading. Nicotine isn’t.

At least not yet.

#tobacco #nicotine #PMI #BAT #strategy #business #dataviz

*JTI excluded due to incomplete data; latest smoke-free share ~4% (H1 2026)

Used Python. Sources

https://www.cdc.gov/nchs/products/databriefs/db522.htm https://www.cdc.gov/nchs/products/databriefs/db549.htm https://www.cdc.gov/nchs/pressroom/releases/20260513.html

Follow-up to last week's post, where the top comment asked where each chain is over- or under-represented 🙂

Each state on the map shows the chain whose share is furthest above that chain's share nationwide. The distribution of the individual top 50 chains (by sales) are visible on the subsequent images.

Video version: https://youtu.be/oj1KEANS8X0

Source: Fast food restaurant web dataset. Each chain's store locator, collected with Kadoa on Oct 20, 2026.

Tools: Python, matplotlib

Hello!

This post features two ridgeline charts illustrating the evolution of age at marriage in Mexico, one for males and one for females, from 1995 to 2025, at three-year intervals. The data are based exclusively on civil marriage records.

Each ridge represents the distribution of marriage ages for a given year, allowing us to compare how these distributions have shifted over time. The ridges extend from the minimum to the maximum age observed in the data for each year, showing the full observed range of the distribution.

You may notice that both the mean and median ages at marriage have increased substantially for males and females, while the number of marriages has peaked and subsequently declined.

Population ageing, behavioural changes, and rising living costs may contribute to these trends. While the charts cannot establish the causes behind these changes, they provide a visual overview of how marriage ages have shifted over time.

I hope you find it interesting!

Data source: https://www.inegi.org.mx/programas/emat/#microdatos

The full source data and cleaned dataset are available here: https://figshare.com/articles/dataset/Civil_Marriage_Records_in_Mexico_1993-2025_/34335813

The charts were created using the Plotly library for Python.

Been digging through real estate board reports for a while and this one surprised me.

Toronto sold 121,712 homes in 2021. in 2025 it was 62,433. Vancouver went from 43,999 to 23,800. so both sold roughly half as many homes as they did in 2021.

The US cities dropped too, just not as much. LA -38%, Chicago -35%, NY -33%. Dallas (-15%) and Montreal (-14%) held up the best.

Calgary is the odd one out. it's down 23% from its 2022 peak but still sold 41% more homes in 2025 than in 2020.

2026 isn't turning it around either. Jan to Sep is lower than the same months last year in all 8 cities.

Before anyone asks:

- every % compares a city to its own past. the board areas are different sizes (Toronto's board covers the whole GTA, Calgary is just the city) so I'm not comparing Toronto's count to Calgary's

- yes 2021 was a crazy year. here's 2020 vs 2025 instead: Toronto -34%, Vancouver -23%, LA -23%, Chicago -24%, Dallas -15%, Montreal -15%, NY -4%, Calgary +41%

- these are mostly resale homes sold through MLS. new builds sold straight from developers aren't in here, which matters a lot for Toronto condos

- a sale isn't the same as a new homeowner. investors and people moving up count too

- Canada numbers are from each board's year end reports, US numbers are from redfin. both are MLS sales but not the same source, so trust the trends more than the exact counts

sources and full numbers in the comments.

what I can't figure out: rates went up in both countries around the same time, so why did Toronto and Vancouver fall so much harder than the US cities?

This chart shows each city's net gain vs net loss of migrants to elsewhere in Canada. International migrants not counted. Data is shown annually since 2002. Cities sorted West (top) to East (bottom) to reveal regional patterns.

Data source: Statistics Canada, Table 17-10-0149-01.

Interactive version.

My wife and I are in the fortunate position to be able to take one all-out holiday a year. I've been not so surprised to see this expense spike since COVID: it feels like we're basically spending what people would spend on a car, once a year, to go away.

It's meant to be relaxing. The not-so-relaxing part is when you feel like you've paid a lot and still get fleeced on site. It's not about the cost, it's about the principle.

So I did what anyone would do: I pulled ~316,000 reviews from Tripadvisor and ~45,000 from Google, had an AI model (Jev) flag every review that was about water, and had Claude (Sonnet) check that when people complained about paying for water, it was in the room or at the pool, where it should be free.

And here's what I told her: paying more does not equate to more freebies, though it does slightly correlate.

4,723 hotels --> r = −0.17 (nightly price vs. the score)

Sources: in the comments, and dynamic on isthewaterfree.com

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).

For this work, I obtained a data set from Professor James A. Serpell at UPenn, based on his 2003 work with now-Professor Yuying Hsu: https://www.academia.edu/download/112413207/dd6ffbecd1c63cdd4447bb1b7ae8afefea09.pdf

and his 2008 work with Hsu and Deborah Duffy https://topdogtips.com/wp-content/uploads/2017/05/Breed-Differences-in-Canine-Aggression.pdf

You can ask me questions about methodology and I will be able to do a decent job of answering them but please don't assume that the study's authors are idiots and haven't thought of most obvious things (pet peeve: mine). The only main weakness I'm aware of is that this is an American sample based on dogs that are owned by people. The questionnaire is designed to weed out subjectivity and its questions have been processed through factor analysis and those factors have been independently validated by behavioral clinicians, but yet, there's bound to be some subjectivity in answering questions about your own dog. I am in no way affiliated with this study but all discussion and plots here were generated by me.

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.

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.

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"

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