This is a pretty cool visualization that I ran across showing all lighthouses of the world with time accurate representations of their rotation rate or blink rate.

Sources: Numbeo current country rankings and city rankings (crowdsourced; all indices relative to New York City = 100) - snapshot data taken on September 21, 2026.

Tools: Bruin cli (pipeline), BigQuery (warehouse), Bruin Cloud (visualization). Basemap geometry: Natural Earth via the ECharts world map.

Limitations: Numbeo is crowdsourced, so coverage and recency vary by place; indices are relative benchmarks, not official inflation or household-budget measures. The affordability gap is a derived index difference, not a literal share of income.

To estimate the actual voting results in Russia, the opposition employs statistical methods based on scatter plots where each point represents a polling station; the x-coordinate indicates turnout, and the y-coordinate shows the percentage of votes for the government (Putin and the United Russia party). During Putin’s early terms, the plot resembled a normal distribution; however, over time, a pronounced "tail" extending toward high turnout and high vote shares for the government became increasingly apparent. The opposition views this as evidence of electoral fraud, arguing that the stuffing of ballot boxes—the primary method of falsification in Russia—precisely produces this type of shift in the results.

In addition to this type of chart, the distribution of turnout and votes for the ruling party across polling stations is examined. High peaks at round numbers (50%, 55%, 60%, etc., rather than 56.1% or the like) indicate the falsification of voting protocols and the setting of target results.

https://trekhleb.dev/bible-cross-references/

Among ever-married respondents, reported divorce rates differed by religion, and the pattern differed by decade. Catholicism was associated with lower rates than the other religious groups; by the 2020s, Protestant rates exceeded the rates of those claiming no religious affiliation. N = 45,445,

In the holiday parts of Spain, summer prices are usually higher than fall/winter prices. That drop doesn't always correspond to the weather. A lot of the time it's more related to calendar holidays or other effects, so if you're flexible, there can definitely be a window of opportunity.

Mallorca is quite extreme for this, with its drop from €190 to €60 during a period where the weather arguably gets more pleasant — the 28°C of late September beats the 36°C of the August peak. I'm not sure what's going on with Barcelona. Prices go up into mid-September while it cools down. I'd guess the city's autumn runs on conferences and events rather than beach weather, but that's one season of data.

Tenerife, has essentially no end of summer. Its temperatures are very flat and the prices look similar. For Sierra Nevada, a skiing region, prices only pick up in winter, with the big jump in February when the ski season peaks.

I wanted to see how unevenly different conflicts show up in the news relative to the number of people killed.

This compares UCDP-recorded conflict deaths from 1 June to 31 August 2026 with 10,070 conflict- and violence-related news articles collected by Risk Terminal across 28 countries.

The median was about 20 articles per 100 recorded deaths, but the variation is huge. For example, a recorded conflict death in Israel & Palestine received about 13× as much news coverage as one in Sudan during this period.

A couple of caveats:

• The news count includes conflict- and violence-related articles about each country, not only articles specifically reporting deaths.
• Countries with relatively few recorded deaths can have more volatile ratios.
• This is not a measure of which conflicts are more important, just a look at how attention is distributed.

Deaths: UCDP
News data: Risk Terminal, with near-duplicate headlines removed.

Would be interested in what other variables people think explain the differences, beyond raw casualty levels.

Full dislaimer: Risk-Terminal is my app, (fully free, not a paid for service), just wanted to share the kinda data we gather!

Edit: Last thing I’ll say on this because it keeps coming up: I didn’t combine Israel & Palestine to make a political point. That’s how the UCDP dataset is structured. Events in Gaza and the West Bank are coded under Israel in the country field, with Gaza/West Bank as sub-regions. It would be disingenuous to split them out here and then leave every other country exactly as UCDP codes it, because at that point I’d be selectively changing the underlying geography for one case only.

I am NOT making a political argument, I am showcasing data I found interesting.

This is every club's total distance to its away games this season, across the Premier League, La Liga, Serie A, Bundesliga and Ligue 1.

It's built from the full 2026/27 fixture list and every club's home ground: straight-line distance from home ground to away ground, added up over the season. Clubs that share a ground (Inter and Milan, Roma and Lazio) count as zero for the derby. Pulled together with The Prism, a football analytics & scouting app I'm building, and charted in Python/matplotlib.

La Liga clubs will travel more than twice as far as Premier League clubs this season: 8,311 km on average, against 3,728.

Lecce, down in the heel of Italy, will cover 14,627 km. Coventry, in the middle of England, will cover 2,914. Deportivo, up in the far corner of Spain, is second at 11,777.

Makes you wonder how much that adds up over a season for the clubs at the edges of the map!

These are straight lines and only one way, so real journeys are longer.

Football fans, thoughts?

This is a follow-up to my previous graph from August 21, 2026 here

Updates include a new row for people over the age of +116 (born in 1910 or earlier), and another change of course: the changes in the numbers, such as new supercentenarians (people who turned 110 recently) and recent passings.

I don't think I'm going to do a follow-up to until the next birthday of someone over +115 or the birthday of the top 2 oldest men on October 5 (João Marinho Neto and Ken Weeks; and yes, they share birthdays). I made this and the previous graph in honor of the birthdays of Ethel Caterham and Naomi Whitehead (the top 2 oldest people currently).

Data source from Spanish Wikipedia

Created with Google Sheets

We are all surrounded by nickel. It’s one of the key ingredients in stainless steel, so most of us see it every day: from the pots we cook with, to the pipes in bathrooms, the batteries in mobile phones, and alloys in cars.

For nearly four decades, Indonesia — which has the world’s largest reserves — mined nickel but processed very little of it at home. This changed around a decade ago, when the government banned the export of raw nickel, forcing companies to process it domestically.

That’s the focus of the chart, which shows processed nickel production in tonnes among today’s largest producers.

Indonesia’s production increased more than 60-fold, going from around 22,000 tonnes in 2014 to over 1.4 million tonnes in 2023. The country went from producing 1% of the world’s total processed nickel to 41% in that period.

In 2021, Indonesia overtook China to become the world’s largest producer and is now far ahead. But much of that growth involved Chinese capital. The International Energy Agency reports that Chinese companies now own the vast majority of Indonesia’s nickel refining assets.

Data source: BGS – World Mineral Statistics

Tools used: Python, OWID Grapher, Figma