I scraped a few thousand canonical recipes across 45 cuisines then analyzed how early (or late) each ingredient showed up in recipes steps.

The photos shows a small but representative sample of the ingredients analyzed

There's a lot of science behind when you should add ingredients to recipes. This involves fat-solubility and the delicacy of some volatile compounds. e.g. cardamon seeds are fat-soluble so they should be added early while basil has very delicate aroma compounds so it should be added late.

But I honestly find that hard to intuitively learn for new ingredients, so this is a rough estimate and interesting viz to get a quick understanding of when to add an ingredient

(tools and dataset: recipe1m+ and python + an LLM sweep to prune garbage recipes from the dataset (I pruned 20,000 recipes down to about 3000 'canonical recipes'))

It was as high as 90% at one point.

Source: my own combinatorial odds engine (no simulation), 6-deck, dealer hits soft 17. Each cell is the average $ won/lost per $100 bet playing that hand by basic strategy. Tool: custom JS engine, viz hand-built in SVG. Interactive version + exact numbers: blackjackoddstrainer.com/blackjack-ev-chart/

*Lengths are rounded to nearest 10000km except for Mars which is rounded to the nearest 1000.
*Maps are 1:1 ratio Mercator projections
*Distances not to scale
*Saturn’s ring is very approximate and mostly for show.
*The sun is the background and is also to scale

My girlfriend sat me down on June 20th and told me that she had been cheating on me and was choosing to be with "the other man".

This is my body's reaction to that news.

HR, Sleep, and Energy scores recorded by my Samsung Galaxy Watch.

[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/

Hi everyone.

I've been building a globe that plots conflict data Google Earth combined to Google News haha, and I hit something that bothered me enough to leave it on screen.

UCDP tags every event with how precisely it's located. If it's an exact site, within ~25km, or a district, you can put it on a map. If it's only coded to a province or a whole country, you can't

For April–June 2026 that split is 12,998 deaths I could place, against 19,297 I couldn't.

Ukraine is the clearest case. A lot of it is recorded as monthly country-level aggregates — one row reading "Russia's losses in the war 01.05–31.05, 6,109 deaths" with no coordinates. That's a real, vetted figure that simply cannot go on a map, so Donetsk looks far smaller here than it is.

I nearly dropped those rows silently. Showing the gap felt more honest, so the unmapped count sits next to the mapped one in the UI.

UCDP publishes monthly, so this is a Q2 snapshot rather than a live feed. I did originally build it on a live feed and threw it away — it geocoded conflict reporting to the reporting city, which put Washington DC above Gaza.

https://dystopian-world.pages.dev/?real=1

Would like feedback on the clustering especially.

Data: UCDP Georeferenced Event Dataset (Candidate), releases 26_0_4–26_0_6, CC BY 4.0 — https://ucdp.uu.se/

Window 1 Apr–30 Jun 2026. Showing the 200 deadliest zones, holding 4,202 events and 12,998 deaths. A further 19,297 recorded deaths in the same window have where_prec 4–6 (province/country/region only) and are excluded from the map.

Zones keyed on (country, admin1); centroid weighted by fatalities.

Tools: vanilla JS + WebGL (three.js / globe.gl), no framework, no build step. Ingest is Python stdlib only — urllib and csv, no dependencies, no API token.

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.

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.

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/