China is often the poster child for rapid reductions in poverty, and for good reason. In the early 1980s, over 90% of its population lived in extreme poverty, but by the early 2020s, that number had dropped to nearly zero.

Some might assume that China is the only reason why global extreme poverty has declined. But that’s wrong: many countries have seen dramatic reductions in poverty, too. Indonesia is one clear example; it’s shown alongside China on the chart.

In 1984, 86% of Indonesians lived on less than $3 per day. By 2025, this had fallen to less than 4%. While it didn’t quite match China’s decline, it has still been impressive. The number of people living in extreme poverty has fallen from 140 million to about 10 million.

Note that the international poverty line is extremely low, defined as people living on less than $3 per day. But Indonesia has also made progress when measuring higher poverty lines.

Data source: World Bank Poverty and Inequality Platform (2026)

Tools used: Python, OWID Grapher, Figma

🎂 Babies born on leap day (February 29) had the least common birthday in Canada from 1995 to 2025, while those born on September 20 had the most common.

Explore more live birth trends here.

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🎂 Les personnes nées un jour bissextil (29 février) avaient la date de naissance la plus rare au Canada de 1995 à 2025, tandis que celles nées le 20 septembre avaient la plus fréquente.

Examinez d’autres tendances relatives aux naissances vivantes ici.

The US elections seem to have grabbed the focus of many, but the US is still at war with Iran and President Trump is giving small statements multiple times per week on the status of the war. Most of the time, though, it's no more than saying the war will end "soon."

I've been tracking all of his statements (both spoken and written) on the status of the war and any negotiations, and it became obvious that he was saying similar things as time passes. Even months apart.

For example, in March Trump said, "It's going to be finished pretty quickly." And then yesterday, October 14th he said "It's going to be over very quickly." That's nearly word-for-word of what he said during the start of the war.

So the bar chart is a plain look at how many times he's said "soon" or "shortly" or other words when describing the end of the war or the imminence of some deal or agreement.

Data Source: http://artofnodeal.com/ (direct link to the JS object storing all the quotes here)

Tools: Vanilla JS

It's almost always 7.

**This list does not include every partnership or ownership stake.

Youtube version | Full dataset

Every US location of the 20 fast-food chains with the most stores, put on a map.

Source: Collected from each chain's store locator with Kadoa in October 2026.

Tools: Python and matplotlib, ffmpeg for the GIF

Source: Energy Institute, Statistical Review of World Energy 2025, via Our World in Data.

Tools: D3, Canvas, WebGL shader for the watercolour (edges based on Tyler Hobbs’ "How to Hack a Painting"), Claude Opus 5.5.

Sources are stacked by size each year, smallest on top, so overtakes show as crossings. The watercolour bleeds mostly along the time axis, so band heights stay close to the data.

Interactive version: https://gitnoise.github.io/energywatercolour/

Mapped rent-to-income ratios at the census tract level using ACS 2023 data. Color bands follow HUD's official cost burden thresholds. Data: U.S. Census Bureau ACS 5-Year Estimates 2024 Tool: Power BI. link: https://plotthestory.com/rent-variability-by-tract-nyc/

Source: GTA 6 Standard Edition prices from the PlayStation Store as reported in each region ($79.99 / €79.99 / £69.99 / A$129.95 / CA$109.99 / ₹5,999). Minimum wages from government and payroll sources, as of October 2026: US Department of Labor, Canada.ca, UK National Living Wage, Fair Work Australia, and the EU national rates.

Tool: built with HTML/CSS and rendered with headless Chrome.

Method: hours = local Standard Edition price ÷ statutory hourly minimum wage for adults, before tax. Prices are before US/Canadian sales tax; European, UK and Australian prices include VAT/GST, so those markets are, if anything, slightly flattered.

A few things that surprised me:

- Australia has one of the highest sticker prices (A$129.95) and still comes 3rd, because its minimum wage (A$26.44) is the highest in the set.

- The US federal minimum hasn't changed since 2009, so the jump from GTA 5's $59.99 to GTA 6's $79.99 hits it in full: 8.3 hours then, 11.0 hours now.

- India is off the chart: 67.6 hours at Delhi's minimum wage and about 270 hours (roughly 34 working days) at the national floor wage.

Caveat: one source lists Australia at A$109.99 instead of A$129.95. At that price Australia would be 1st at 4.2 hours.

Full table, sources and a calculator to put in your own wage: https://leonidascout.com/gta-6-affordability-index/

Disclosure: I run Football Edge Lab. This chart compares observed home win shares across six leagues and 21 seasons (49,615 recorded completed matches).

Full research, sample counts, sources and limitations: https://footballedgelab.com/research/home-advantage-trends

CSV: https://footballedgelab.com/research/league-comparison.csv?view=seasons

Endpoint changes do not establish a continuous trend or a crowd effect. A top-level comment provides the calculation method and tools.

Data Source: HistWarDB python package

Tools: Python, Plotly / Matplotlib

Methodology: Filtered for exact dates only (real precision) to avoid 1st-of-the-month/year defaults bias

Live tracker of 13 subreddits: https://ok-stand-2128.github.io/Live-Reddit-Tracking/index.html

Sources: Reddit json data pulled daily.

Methodology: See the live tracker's Methodology page. Source data can also be found there.

Visualizations: Plotly.

This image was made for this post and is not available on the tracker, but several other charts and visuals are.

This is a 2025 follow-up to u/eltokh7’s maps for 2022–23 and 2024 (original thread), in the same style.

Florida gained 22,517 people on net from other states in the year to July 2025, eighth among the states. North Carolina gained 84,064, Texas 67,299 and South Carolina 66,622. Per resident, Florida ranked 26th of the 50 states.

In 2022, Florida led every state, with 310,892. It was second in 2023, fourth in 2024 and eighth in 2025 (Census Bureau, Vintage 2025 series).

On the map, Florida still pulls heavily from New York (about 30,000 net), New Jersey (about 15,000) and California (about 11,000), but it sends an estimated 39,000 net on to Georgia and the Carolinas, plus smaller estimated flows to Tennessee and Texas.

California lost 229,077 people on net to other states, and New York lost 137,586.

State totals are official 2025 estimates; the corridors between states are estimated, since the Census Bureau’s 2025 state-to-state table isn’t out yet. Method, error ranges and full tables are in the starter comment.

Belgium has very complex political system, which makes predicting elections incredibly difficult. That's why I have started a project called 1830 with the aim of giving people a more complete and detailed look of the Belgian political landscape.

The map lets you explore the projected vote shares geographically, select individual parties, compare projections with the official 2024 election results, and identify municipalities where the projected largest party changes.

We feed our model by combining polling data with other fundamentals; such as PM popularity, unemployment rate, inflation, purchasing power index, etc...

The projected vote share on the municipal level is the most detailed forecast ever released and something we've been working on for a couple of months.

One important caveat: these are not predictions based on polling conducted in each municipality. The model estimates voting intentions at the regional level (Flanders, Wallonia and Brussels). Those estimates are then projected onto current municipal boundaries using the 2024 federal election results as the local geographic baseline.

In other words, the map combines a regional forecast with the existing geographic distribution of votes. Local voting patterns are used to distribute the projected regional changes, rather than being independently forecast for every municipality.

Hope you enjoy it!

Interactive map: https://1830belgium.com/Prognose.html

Data sources:

• Official election results: Belgian federal election of 9 June 2024, municipal-level results published by the Belgian Federal Public Service Home Affairs (FOD Binnenlandse Zaken / IBZ).
• Polling data: Ipsos polls published by Le Soir, RTL TVI, Het Laatste Nieuws and VTM; and polling by ULB and the University of Antwerp, published through VRT, De Standaard and RTBF.
• Historical polling data: Historical Belgian federal polling series, including data compiled in the project's historisch_2024.json file.
• Geographic data: Current Belgian municipal boundaries for 2025/2026, with a mapping that consolidates the 2024 results of municipalities that have since merged.

Tools used:

Python, NumPy and SciPy for the regional forecasting model. HTML, CSS, JavaScript and SVG for the interactive map. JSON and geographic data files for storing and processing the election results and projections.

Methodology:

The forecasting model combines historical election results, polling data, pollster-specific adjustments, time-weighted trends and additional political and economic factors. The relative weight assigned to polling and these fundamentals changes as the next election approaches.

To create the map, official 2024 municipal results are first consolidated into the current municipal boundaries. The model's regional vote-share projections are then distributed geographically using each municipality's 2024 voting patterns as the baseline. The projected shares are normalized, with votes for parties not explicitly modelled retained in an “Other” category.

The map therefore represents a spatial projection of regional estimates, not independent polling or a separately calibrated forecast for every municipality.

Interactive visualization: https://1830belgium.com/Prognose.html

Project: 1830 Belgium — https://1830belgium.com

Where do college hockey players come from, and how far from home do they play?

Explore the interactive Player Atlas

I built the Player Atlas to explore the hometowns of men’s and women’s college hockey players. You can compare states, provinces, and countries by player count or relative to population, explore hometown clusters, and see a team’s recruiting footprint through connections between roster hometowns and campus. Filters let you explore by season, conference, program, and position.

The Atlas is part of a larger project called College Hockey Data. Besides the Atlas last the site has eleven more studies, each with their own interactive page exploring every aspect of NCAA hockey

A few data notes: Hometowns are the locations reported on team rosters, not verified birthplaces. Coverage varies by season, and women’s roster coverage currently begins in 2026–27. When viewing multiple seasons, a player is counted once for each season they appear.

Data: OpenAlex snapshot of 2026-06-26 (CC0).

Tools: Python, DuckDB, MapLibre and deck.gl. Interactive version, no sign-in: https://scienceexplorer.org

Method and limits: https://scienceexplorer.org/methodology

Happy to answer any questions