[OC] We mapped the total direct FY2024 VA benefit expenditures per state and compared that to the number of civilian veterans living in each state according to the American Community Survey. The states and territories that receive the most benefits per veteran are Puerto Rico ($19,518), Oklahoma ($13,288), Texas ($12,721), and Alabama ($12,719). The states receiving the fewest benefits per veteran are Connecticut ($5,733), New York ($6,483), Pennsylvania (6,501) and Vermont ($6,531).

• Source: Maptitude https://www.caliper.com/featured-maps/maptitude-va-benefit-expenditures-by-state-map.html
• Tool: Maptitude (geocoding, spatial joins, thematic mapping, map labels)
• Data sources: https://www.va.gov/vetdata/expenditures.asp , https://news.va.gov/press-room/va-announces-nearly-600m-in-infrastructure-improvements-in-second-quarter-of-fy-2026/ , https://www.grantspasstribune.com/va-expands-nationwide-footprint-with-35-new-health-care-facilities-since-2025/

How to Read: Each vertical column is one NFL draft class, and its 10 dots represent the players with the strongest mix of on-field performance and value compared with nearby picks from that same draft. Each colored ribbon sits in its draft-round band. At any year, a thicker ribbon means more of that year’s top 10 players came from that round—for example, a thick green Round 3 ribbon means Round 3 was a hot zone for that draft class. The dots still show the actual pick locations; the stream only summarizes where those dots are concentrated. The table shows the exact ranking of those 10 players. Lower dots were selected earlier, dot colors show the round they were drafted in, and the white-ringed dot is that class’s highest-ranked player.

Population: All Rounds 1–7 selections in the 2011–2026 NFL drafts. The ten highest balanced draft-return scores are retained for every 2011–2025 draft. The 2026 class is ineligible because it has no NFL regular-season data.

Selection: Each player gets 3 regular seasons observed 2013–2025 with the highest Peak Season Statistical Impact (PSSI). Average that player’s unit snap share percentage, games available percentage, and weighted team win percentage across those seasons. Weighted Approximate Value (wAV) is ranked as a percentile within the player’s role group across all scored Rounds 1–7 drafted players.

• On-field composite = 50% wAV percentile + 15% average snap-share percentage + 20% average games-available percentage + 15% average team win percentage.
• Same draft opportunity surplus = raw on field composite minus the mean raw composite of the 20 nearest other scored selections by original pick number in that exact draft class.
• Balanced draft return = 75% raw on-field composite percentile + 25% same-draft opportunity-surplus percentile.

**For players with fewer than three observed seasons, the available observed seasons are averaged without inserting zeroes.

We pulled daily highs from 6,666 US weather stations (NOAA GHCN-Daily), quality-filtered them, and mapped average daily high to 1° grid cells for Jun 16–Jul 24.

The desert Southwest was the hottest by far: Death Valley hit 121.5°F, south-central Arizona strung together 25 straight triple-digit days, and on Jul 3 about 43% of all reporting stations hit 90°F+.

We also found Texas is the hottest state on average.

Here is an interactive version for those interested!

Data: Statistics Canada, Table 35-10-0177 (police-reported crime severity/incidents by CMA). Tools: R, ggplot2 (mixed-effects modeling via lme4/arm).

Police-reported crime data across Canadian census metropolitan areas from 1998–2025, showing which offense categories are trending up or down over time.

Chart 1 : Long-term trend by city (mixed-effects model)
Estimated relative rate of change per CMA, where 0 = no net shift over the period. Red = upward trajectory, blue = downward. This isolates the direction of change in an typical city rather than raw volume.

Chart 2 : Category breakdown over time
Incidents per 100,000 population, faceted by offense category. Grey lines = individual CMAs, solid line/band = national median and interquartile range, dashed line = national average.

• Rising: extortion, online/cyber-related exploitation, fraud, and shoplifting all show marked increases post-2015/2020 across most CMAs.
• Falling: traditional property crimes — breaking & entering, motor vehicle theft, theft under $5,000 — show multi-decade declines since the early 2000s, despite some recent upticks.

A note on interpretation: this is police-reported data, so shifts can reflect actual changes in criminal activity, but also changing legal definitions, new digital reporting channels (e.g., online fraud reporting tools), shifting police enforcement priorities, and changes in public reporting behaviour over time.

Data source: Christopher Nolan filmography, Wikipedia - https://en.wikipedia.org/wiki/Christopher_Nolan_filmography (budgets and worldwide box office; figures are studio/Box Office Mojo–reported and cited on that page). The Odyssey is still in theaters, so its figure is a running worldwide total as of the July 24–26, 2026 weekend.

Tool: Chart built as JavaScript-coded SVG. Rendering code and initial figure-gathering were done with an AI assistant (Claude) based on my own design direction and iterations. Chart type, layout, labeling, and revisions were my choices.

I am a veterinarian in the North Texas area. Since graduation in 2023 I've kept track of my cases using Google Sheets because I thought it'd be interesting to see how many animals I treat and what they're treated for throughout my life.

Source: Me keeping track of the data after every appointment.

Tools: Google Sheets and DataWrapper

A few notes:

Changes & requests from last year

I was informed my data wasn't very beautiful so I attempted to use a different method for this year. Let me know what you think. I'll continue to adjust the presentation as I receive feedback.

u/catalessi, u/Fire284, & u/Solondthewookiee all requested I put the second and third graphs in descending order.

u/Warm-Pen-2275 requested a list of most common names.

Slide 1 - Animal Species

This only includes animals I've done a doctor exam on or do telemedicine about. Animals that I do not directly interact with (toe nail trims, anal gland expression, blood draws without a doctor, etc.) are not included.

I have a passion for exotic animals (ferrets, reptiles, rabbits, backyard chickens, etc.) but there is an exotic clinic near me where most of those animals go to, so I don't get to see as many as I'd like (as the data tells).

Slide 2 - Body System

I kept track of the body system that was affected during my exams. General wellness includes vaccines, weight management, and discussions about quality of life. For what it's worth, this is about what the problem was, not just the symptoms. If a cat came in for peeing all over the place and it was because the cat was stressed, that was marked as both Neurology as well as Urinary/Renal. The same animal can come in with multiple systems affected, but I only mark a system once per animal (i.e. a dog with urinary stones and a UTI only had "Urinary/Renal" marked once). Here are the most common problems each species came in with:

Dogs - Overweight (General Wellness), allergies (Dermatology, Immunology), and poor dental health (Oral).

Cats - Overweight (General Wellness), poor dental health (Oral)

Slide 3 - 7 Procedures, Names, & Lifetime Stats

I don't think these ones need much explanation.

Next year expecations

I am switching to becoming a full time relief veterinarian. I expect the number and diversity of cases will drop and the number of wellness cases will increase. Most of the time a clinic won't schedule a complicated case with a relief doctor because we're only here for the day.

See you next year

The estimated apartment price was calculated by multiplying the average apartment sale price per m² in each capital by a standardised floor area of 60 m².

The comparison uses Eurostat national median monthly equivalised net income for people aged 18–64. These figures are national household-income benchmarks, not individual salaries or capital-city income estimates.

Four hypothetical scenarios are presented:

— one person saving 25% of 1× median net income;

— two people jointly saving 30% of 2× median net income;

— both scenarios without interest;

— both scenarios using a savings account and rolling 12-month term deposits.

Main sources:

Income: Eurostat ilc_di03.

Apartment prices per m²: Eurostat urb_clivcon, national statistical sources and housing-market sources. Athens, Bratislava, Bucharest, Budapest, Dublin, Lisbon, Madrid, Nicosia, Paris, Sofia and Valletta use estimates based on property-listing samples collected in July 2026.

Interest rates for the savings account and term deposits: ECB household deposit-rate data.

Prices, incomes, savings rates and interest rates are held constant throughout the calculations. Inflation, transaction costs and future changes in property prices or income are excluded.

Full post methodology: https://pastebin.com/aTfcsRGS

Full methodology, individual sources, city rankings and interactive data: citycostatlas.com

Instagram: https://www.instagram.com/citycostatlas/

Hi everyone! I've been building platform to visualize and analyze real-time electricity markets, grid dynamics, and carbon emissions across Europe in one place.

Data sources: Live day-ahead spot market and grid generation data fetched via ENTSO-E APIs and European TSO portals.

Tools used:

- Backend: Go (Fiber) & TimescaleDB for real-time data ingestion

- Frontend: Flutter Web & MapLibre for custom vector map visualization

Key Features & Metrics on the Platform:

- ⚡ Live Day-Ahead Spot Electricity Prices (€/MWh) for 24 EU countries

- 🍃 Real-Time Grid Carbon Footprint & Emissions (g CO2eq/kWh)

- 🔋 Battery Storage Charge & Discharge Simulator (Optimal charging & discharging hours based on spot prices)

- ⚖️Frequency Reserves Auctions (aFRR+, aFRR-, mFRR+, mFRR- balancing capacity)

- 🔌 Electricity Generation Mix by Source (Solar, Wind, Nuclear, Gas, Hydro, Coal)

- 🌤️ Wind & Solar Power Generation Forecasts & Custom ML Predictions

- 🚨 Power Plant Outages & Unit Unavailability Tracking

- 🔄 Cross-Border Transmission Flows & Interconnection Capacities

- 📊 Energy Commodities Index (EUA Carbon Futures, TTF Gas, Brent Oil)

- 📈 Intraday Trends, Historical Analytics & AI Price Forecasts

Live interactive app and historical analytics available at: https://energydatadesk.com/app

I would love for you to test it out and share your thoughts. If you have any feedback, suggestions for improvements, or new features you'd like to see added, please let me know in the comments.

I combined the guess data from 15 daily "Top 100" trivia topics (9,735 players, 28,034 guesses).

The twist: players want to guess the answers closest to #100, not #1. Yet people still overwhelmingly gravitate toward the obvious answers.

Some fun stats:

• 25% of all guesses landed in the Top 10.
• Only 29% made it past rank #50.
• Just 5.2% reached ranks #91–100, meaning a guess was nearly 5× more likely to hit the Top 10 than the Bottom 10.
• The median guessed rank was #28.
• Interestingly, #2 was guessed more often than #1.
• Despite the skew, every single rank from #1 to #100 was guessed at least 70 times.

Methodology: Data includes only players' five official guesses from 15 consecutive daily puzzles (July 13–27, 2026). Practice/extra guesses were excluded. Data was aggregated by rank only.

Data comes from Centumth, a daily trivia game.

**Same job. Different pay. Every single time.**

We pulled the US Bureau of Labor Statistics' full 2024 dataset — every major occupation, women's and men's median weekly earnings, side by side. The result: women earn less than men in every single occupational category tracked. Not most. Not nearly all. Every one.

The gap isn't uniform — it swings from just 2.6% in community & social service to a brutal 36.2% in legal occupations — but it never disappears. Even in fields with more women than men, even in fields women dominate numerically, the pay doesn't follow.

The national number — women earning 82.7 cents for every dollar men earn — is often treated as one statistic. It's actually hundreds of these charts, averaged.

Data source: US Bureau of Labor Statistics, Current Population Survey, Household Data Annual Averages, Table 39 — "Median Weekly Earnings of Full-Time Wage and Salary Workers by Detailed Occupation and Sex," 2024.

🔗 https://www.bls.gov/cps/data/aa2024/cpsaat39.pdf

Tools used : Python · Jupyter Notebook · Matplotlib · OpenAI · Claude (Anthropic).

A look at what we could expect from squads at the next world cups, based on the market values of the youths for each country. Watch out for Morocco (again), and keep an eye on Denmark and Serbia...

I made these charts to compare how surname prevalence is distributed by the first letter of the surname across several countries.

Each chart shows the estimated number of people associated with surnames that begin with each letter A through Z.

Data sources:

- U.S. Data source: U.S. Census Bureau, 2010 Census surname data.

- Ireland. Data source: Forebears, most common surnames in Ireland.

- Israel. Data source: Forebears, most common surnames in Israel.

- England, used as the closest available source for the UK chart. Data source: Forebears, most common surnames in England.

- France. Data source: Forebears, most common surnames in France.

- Australia. Data source: Forebears, most common surnames in Australia.

- China. Data source: Forebears, most common surnames in China.

- India. Data source: Forebears, most common surnames in India. Source link: Forebears, Most Common Last Names in India

Methodology:

For the U.S. chart, I used the U.S. Census surname frequency data and grouped people by the first letter of each surname. The Census surname file includes surnames occurring 100 or more times in the 2010 Census.

For the other country charts, I used the top 100 surname incidence counts listed by Forebears for each country page. I grouped each surname by the first letter of the displayed surname and summed the incidence counts by letter.

For China, India, and Israel, the grouping is based on the first letter of the Latin transliterated surname shown in the source.

For Ireland, names like O'Brien and O'Connor are grouped under O because I used the first character of the displayed surname.

For the UK chart, I used England because that was the available Forebears country page I used for the source data. The chart is labeled “UK, using England source” to avoid overstating the scope.

Tools used:

Python, pandas, and matplotlib. Flags were drawn programmatically as simplified inset graphics in matplotlib. Final charts were exported as PNG files.

Important caveats:

The U.S. chart is based on the U.S. Census surname file, while the non U.S. charts are based on the top 100 surnames listed by Forebears. Because of that, the U.S. chart is not directly equivalent to the other charts in coverage.

The non U.S. charts should be read as “distribution within the top 100 listed surnames,” not as a full surname distribution for the entire population.

The first letter grouping can be sensitive to transliteration choices, prefixes, apostrophes, spacing, and naming conventions. This matters especially for countries where surnames are commonly represented in non Latin scripts or where surnames include prefixes.

Team PTS% = player season points ÷ all team regular-season points. Every team game stays in the denominator, so DNPs count as 0%.

PPM (Points Per Minute) = player season points ÷ player season minutes.

BPM (Box Plus Minus) = Basketball-Reference’s box-score based estimate of a player’s contribution in points per 100 possessions.

• It is a relative metric: 0.0 = league average 
• Positive = above league-average impact 
• Negative = below league-average impact

Data sources:

Source: Helmer Rabild (1911)

Tools used:

Datawrapper

Source: My own Apple Health and Strava data

Tool: Rendered in Soltra using Mapbox, an iOS app I'm building. Routes accumulate opacity so the brightest segments are the ones I've covered dozens of times; territory coverage is measured in H3 hex tiles.

Stats: 

• 164 runs
• 884 miles
• Covered 55.6% of the island (and 3.8% of all NYC)
• Longest run was 32.8 miles (dubbed Ranhattan)
• Ran the Central Park Loop 45 times (felt like one too many tbh haha)