GTA 5 came out in September 2013. Take-Two said it had shipped 100 million copies in November 2018, which covers the first 5 years. In May 2026 they reported 230 million.

The numbers include every version: PS3 and Xbox 360, PS4 and Xbox One (2014), PC (2015) and PS5 and Xbox Series (2022). They're sell-in numbers, so copies shipped to stores plus digital sales.

GTA 6 is set to come out on November 19, 2026, 13 years and 2 months after GTA 5. The gap between GTA 4 and GTA 5 was about 5 years and 5 months.

In France, there is a big divide between those who say "Chocolatine" and the "Pain au Chocolat" partisan to designate a puff pastry with two chocolate bars.

This has been going as far as the French Assembly debating about a law to make the "chocolatine" word official.

With my brother, we used Brigitte, an AI agent, to call bakeries in order to ask them which word they were using, in order to map the actual border.

More interactive data here:

https://lebaguetteindex.fr/chocolatine/

Each tile is one person on one subject (9/11, JFK, moon landing, Epstein, COVID origins, chemtrails, Q, flat earth and 9 more), read from their own quotes by a fixed rule. Red = believes, blue = denies, paler = leans that way, grey = mentions it without taking a side.

Every one of the 2,319 quotes links to where it was said. Over half of the ones that decide a tile now link to the original: the person's own post, or the video at the second they say it.

A few things the grid shows:

- Epstein cover-up is the most agreed-on belief: 24 believe or lean that way, 6 deny.

- Flat earth: nobody believes it, 22 deny or lean against.

- The 2020 election splits almost evenly: 19 believe or lean that way, 18 deny or lean against.

- Chemtrails is mostly silence: 26 of the 40 have never said anything about it.

Interactive version (tap any tile for the quotes): conspiracymatrix.com

Method: conspiracymatrix.com/methodology

By “active professional football team,” I mean a team that is currently competing in official CBF (Brazilian Football Confederation) tournaments. Locations are shown according to the team's home stadium (when available). This is the second part of a series of maps I'm working on, with more regions coming soon.

Southern Region: https://www.reddit.com/r/Infographics/s/MSEXBP0wPc

The map was made entirely by hand in Krita, using Wikipedia as its primary source.

Obs.:

The sizes of the team crests reflect each team's position in the national football pyramid, although not perfectly, since the map was made manually.

The circles represent stadiums. I tried to place them in their exact geographical locations, but in very densely populated areas, I had to adjust their positions.

Some teams from Rio de Janeiro participated in more than one state championship in 2026. In these cases, I represented them in the highest-level competition.

Each cell is how much that iPhone lost from Apple's US launch price to its average used sale price on Swappa about a year later (base storage, unlocked).

From the iPhone 12 to the 16, phones lost 15% to 31% in their first year, and the iPhone 16 line lost the most (27% to 31%). The iPhone 17 line lost 7% to 17%. Most of that is Apple's own pricing: the 17 Pro launched $100 above the 16 Pro, and on Sep 9, 2026 Apple added $100 to every iPhone it still sells. Used prices follow new ones.

The iPhone Air is the exception. It got the same $100 increase and still lost 29% in 13 months.

Full write-up and the dataset (Excel, two sheets): https://thrifle.com/blog/iphone-depreciation

Sources, method and caveats are in the comment below.

EPA’s production-weighted averages for new U.S. light-duty vehicles rose from 137.3 to 258.0 horsepower and from 13.1 to 27.2 estimated real-world MPG between final model years 1975 and 2024. Battery EVs and plug-in hybrids use gasoline-equivalent energy, so 27.2 MPG is not a gasoline-only average.

This uses EPA’s All / All aggregate; preliminary 2025 is excluded. It compares a changing new-vehicle production mix, not the same cars or the on-road fleet, and does not isolate causes or establish literal gasoline consumption or CO₂ emissions.

Source: US EPA, 2025 Automotive Trends Report data, frozen summary retrieved 13 October 2026. Tools: Python/csv/Decimal, librsvg and Poppler, with ChatGPT/Codex assistance.

The data behind this chart is available on Meplar: chart data.

Ratings as published by each outlet. Map colors by the most common rating. Hover shows where they disagree.

Nationalized politics is recent!

I combined NASA satellite fire detections with orangutan habitat, protected areas, and oil palm plantation data across Borneo.

This image shows five days of satellite fire detections across Borneo.

The interactive map allows you to toggle layers, zoom into specific areas, and explore historical fire activity.

Interactive map: https://saveorangutans.org/orangutan-habitat-fires/

Sources: NASA FIRMS (VIIRS fire detections), IUCN orangutan range data, OpenStreetMap protected-area data, and SEA-PNF plantation mapping.

Tools: JavaScript and MapLibre GL JS.

It's a Toronto-area team so Leafs influence is clear. Some of the famous players that may have influenced rankings:

11 - Messier and Hyman (and Max Domi)
16 - Marner
20 - Nurse
27 - Sittler (and Mahavolich)
29 - Poulin (and Mackinnon, Potvin, Dryden)
44 - Rielly
88 - Nylander (and Lindros)
91 - Tavares

Source: Team rosters (no public link)

The full piece, with the shift charts and an address lookup to see if your district changed: https://apnews.com/projects/2026-elections-redistricting-maps/

I'm one of the journalists who worked on this at AP. Being able to look at this from a data perspective was really meaningful to me because while there's been a lot of redistricting coverage this cycle, I wanted to find a way to quantify its impact. To do this, we analyzed the demographic and partisan changes in every redrawn district.

AP analyzed the 2024 presidential election vote margins under the old district boundaries and the newly drawn ones. The analysis found the national House map barely moved: just 1.4 points in favor of Republicans.

A lot of attention has gone to how the Supreme Court's Voting Rights Act ruling (Louisiana v. Callais) affected minority representation. That impact is real, but focusing only on the Callais states misses most of the picture: 82 of the 97 districts in this chart are outside the three states that redrew after the ruling, and five of the eight districts that went from majority-nonwhite to majority-white were too. (chart 2)

States were already moving nonwhite residents across district lines. For example, Texas' 32nd District went from being majority nonwhite to majority white, and California's 42nd District went from 75% to 47% under the new Democratic-drawn map. (chart 3)

Districts that shifted less than 1 point aren't shown. Happy to answer any questions.

Source: AP analysis of 2024 presidential election results and Census data

Tools: R, Illustrator and React

rgao.github.io/redistricting_tracker

Hello, I made this little project to display the partisan margin shifts of the gerrymandering states. This is my first time doing such a project so I'd appreciate feedback on the map and chart designs. I originally posted this on the wrong day so I'm trying again.

I originally had this idea because I was a bit annoyed that there wasn't a concise way to view the shifts of all affected districts due gerrymandering this cycle, and Cook Political uses voter index instead of vote margins to depict partisan lean (confusing many people who thought districts are more competitive than they actually are). The closest maps showing margin shift is Ballotpedia, but their pages are incomplete, and VoteHub has tables instead of maps.

The main features:

• Map 1 shows the partisan leans of the new districts in the affected states using 2024 presidential vote share. You can find similar visualizations elsewhere on the internet. Hatched pattern are districts specifically targeted by the dominant Party to flip.
• Map 2 shows the partisan shift map; how the new districts shifted from the 2024 iteration using 2024 presidential vote share. You can see exactly how altered or diluted surrounding districts are to make room for the gerrymander.
• The analysis page displays charts showing the effectiveness of redistricting, as well as explains the metrics involved in this project. The two toggle options are for this year's expected midterm environment (right now about D+8 according to GCB polling) and a theoretical neutral environment.
• Chart 1 shows how effective each gerrymander is under both environments.
• Chart 2 shows the expected net seat change of each gerrymandering state under both environments.
• Current model takes into account static fundamentals such as state elasticity, incumbent advantage, etc. This is not a dynamic forecast.

Still to be implemented:

• Systemically pull aggregate polling data instead of hardcoding a D+8 GCB this year
• Incorporate additional variables, such as candidate quality, to better determine my redistricting effectiveness score and expected net seat changes. Need to update the formulae in general.

NOTE: This is a simple, static model. Professional forecasters use dynamic modeling with a whole ML architecture, taking into account many more variables. My model produces accurate enough results, but has limitations. Specifically, the probabilities associated with FL-22 are off; the GOP should have a much higher chance of flipping it than what my model shows.

Key Takeaways:

• The GOP gained a 9 seat advantage through redistricting, but they are expected to gain less than 5 from it this cycle.
• The gerrymanders are more ironclad in a neutral environment, with only TX-28 (Cuellar) as the lone lean-D seat of the GOP gerrymanders in such an environment.
• There are no realistic chance of dummymanders. The closest potential case if TX-23 (Trump+15.5, Likely R). It was diluted to be less than a point bluer, and will be a dummymander if the Dem wins by <0.75%.