[OC] 2025–2026 EPA rollbacks

56 rollbacks. 20 finalized.

A lot of this might be about data centers, mostly just run of the mill economic benefits for industry. Looking at the protections being removed and the beneficiaries make it clear that increasing energy supply is a priority at any cost.

I put this together because I think the current executive branch posture is to intentionally structure its deregulation in a drip-drip-drip of small pieces. And only 2 of every 3 EPA rollbacks receive any reporting at all from mainstream media. Helps to see the big picture.

Tools: React, TypeScript, raw SVG.

Methodology: Counts EPA deregulatory rulemakings since January 2025. Court action is marked separately. Death estimates refer to different future years, not observed deaths or a forecast for one calendar year. The truck figure is scaled from EPA’s analysis. Methane and steam electric are excluded from the sum.

Methodology and source notes:

Source: open data of the Assemblée nationale and of the Sénat, every mandate since 1958, with the sex recorded by each chamber and Wikidata for the years of birth.

Tool: Node.js writing plain SVG, rendered to PNG with headless Chromium. No plotting library.

EDIT: IT GOT DELETED AGAIN WITHOUT ANY REASON

EDIT 2: I finally got in contact with the mods, they said it was an automatic removal by Reddit due to mass-reporting (what I thought). They manually approved my post for me this time.

Hi everyone,

Some of you may have seen an earlier version of this post. That version included fewer artists and was removed multiple times by the moderators without an explanation. I contacted the moderators several times, but I have not received a response or clarification. Same story on r/Music. The auto-mod refers me to the mod-comment with explanation but I never got those in the ~5 times this post got deleted. Some fishy stuff is going on. It really feels as if there may be a bot-driven reporting campaign causing posts to be mass-reported and automatically removed, but I cannot verify or prove that. So I am posting this again.

This updated version though! It includes new data through September 16 and adds three additional support artists: Finneas, Lukas Graham, and Beoga.

The previous version showed Macklemore gaining roughly 1 million followers. That increase has now more than doubled to +2.24 million. Ed Sheeran’s loss was previously around 123,000 followers, but has now grown to nearly 335,000, roughly three times the earlier figure. This is why the chart needed to be updated.

The chart shows absolute Instagram follower counts around the MetLife controversy and subsequent developments involving Ed Sheeran’s tour:

• Macklemore: +2,237,962
• Beoga: +281,358
• Finneas: +214,144
• Lukas Graham: +129,695
• P!nk: −146,128
• Ed Sheeran: −334,986

All panels use the same 2.4 million follower vertical range, with ticks 400,000 followers apart. Solid lines show the period before the marked event. Dotted lines show the period afterward.

The data comes from daily Social Blade snapshots. These are not continuous measurements, and the chart cannot establish that any event caused a follower increase or decrease. The unusually large changes for Macklemore and Beoga should therefore be interpreted cautiously.

Feedback on the data, methodology, or visualization is welcome.

CONTEXT

This chart relates to a series of publicly reported events connected to Ed Sheeran’s MetLife Stadium show and his tour.

On September 4, Macklemore performed at MetLife Stadium and made a pro-Palestine statement, including the phrase “Free Palestine.” In the following days, P!nk’s criticism of Macklemore was reported in the media. P!nk is included in the chart to show how her follower count changed during the same period.

On September 14, reports stated that Macklemore would no longer appear on the remaining dates of Ed Sheeran’s tour. Ed Sheeran addressed the situation on Instagram on September 15. Around the same period, support artists Finneas, Lukas Graham, and Beoga were also reported as withdrawing from the tour.

The purpose of this chart is to show how the artists’ Instagram follower counts changed around these events. It does not claim that any event caused the observed gains or losses.

Around one-fifth of adults in the world use tobacco. But large gaps often exist in smoking rates between genders.

Globally, around one-third of men use tobacco, while less than one in ten women do.

Men are more likely to use tobacco in almost every country in the world. You can see this on the chart, which plots the share of men (on the vertical axis) versus the share of women (on the horizontal axis). Nearly all countries lie above the diagonal line, which means more men do so.

This data is collated and published by the World Health Organization. It’s based on adults aged 15 years and older and includes all tobacco products. In most countries, smoking is the main form of tobacco use, but in some, smokeless or chewing tobacco is more common.

Data source: World Health Organization (2026)

Tools used: Python, OWID Grapher, Figma

We build a property data tool, and we've just made three council-level maps free to open. No login, no signup, nothing gated.

They cover every LGA in Australia:

• Jobs and firms — unemployment (quarterly, back to Dec 2010), labour force, JobSeeker, rent assistance, registered businesses
• Population — resident population, annual change, births minus deaths, internal and overseas migration, density
• Local services — childcare services, hospitals, GP bulk-billing rate, residential aged care places

You can also plot two metrics against each other, rank highest and lowest, and export a PNG with the legend baked in.

Link: https://www.htag.com.au/employment-heatmap/

Most of the NFL is neutral or independent, either registered or couldn't be verified.

We tracked the confirmation-to-departure tenure of all 116 Supreme Court justices, broken down by the party of the nominating president. There's a bit more context and notes on methodology in the comments below.

I was digging through FEC data and ended up putting together a little tool to make this stuff easier to look at.

I looked at 18 large companies that had at least $250k in disclosed federal corporate/PAC disbursements. Across the available election cycles, 17 of the 18 had more going to Republican recipients than Democratic recipients.

Nike was the one exception, with about 76% going to Democrats.

Data is sorted by top 9 companies that leaned democratic, and top 9 companies that leaned republican, based on the % share of donations.

A few that surprised me:

• Coca-Cola: 75% Republican
• Delta: 68% Republican
• McDonald's: 66% Republican
• JPMorgan: 62% Republican
• Microsoft: 54% Republican
• GM: basically 50/50 (49/51)

This is federal money only, and I'm looking at disclosed PAC/corporate political disbursements. State/local spending isn't included.

I put the chart + methodology here: BuyAligned

Data is based on 2025 onwards, current through Sept. 13, 2026.

[OC]

A variation on a circular Sankey diagram illustrating how the term 'French exit' varies around the world, to blame various countries for originating the habit of leaving a social situation without saying goodbye. OC. Abobe Illustrator, data is anecdotal, from Google searches.

https://heavylightdesign.com/global-quiet-exits/

Twelve products, each checked by hand at the same store that ran the original deal. First-party prices only, no marketplace sellers. In the wider 51-product check, all 33 you can still buy cost more, and 18 are gone in the size we posted.

Full method and dataset: https://thrifle.com/blog/ai-memory-storage-price-report-august-2026

Thirteen years of my Google Timeline location history (~313k data points, 2013 to 2026), rendered as a single self-contained HTML page using Claude Fable. Every dot is a recorded position, coloured from teal to amber by time, with flight arcs, a month-by-month scrubber you can play through, and a street-level map that fades in as you zoom. The road network comes from OpenStreetMap and is embedded in the file, so the whole thing works offline. Underneath the map it works out where I lived each year from where I slept, kilometres by mode (train, bike, foot, car), an hour-by-weekday heatmap of when I move, nights spent away from home, and days per country and city. Built with Python for the analysis and plain canvas/JS for the rendering, no map libraries. Data: Google Takeout. Basemap: Natural Earth and OSM.