Interactive live version with all updates

Sources:

• EPA ratings
• EIA prices: gas and diesel (week of Oct 19), home electricity (August 2026 residential, 18.16¢ a kWh)
• The 25 best-selling models from January to September 2026, from the carmakers' own sales reports, plus the next 5 best-selling EVs and the Prius for comparison. Each row is a version, not a model, so models sold as gas, hybrid, plug-in hybrid or diesel get a row for each. Plug-in hybrids split their miles between battery and gas by EPA's estimate for an owner who charges every night.

Tools: Kadoa for the data, Python, HTML and SVG for the chart. Claude did the car outlines from each model's published length, height and wheelbase.

EDIT:

• Prices vary a lot by state, so I added a state picker to the live version.
• The cities are only a ruler for distance/for scale.
• Added plug-in hybrids and the Prius. At 53 mpg the Prius goes 1,237 miles on $100, just behind the slowest EV (Rivian R1S, 1,278).
• Added a toggle for fast charging vs home charging
• metric units added

I use sheets of day-stickers to label leftovers when I cook; I do a lot on Tuesday & Wednesday but never Thursday apparently by this data!

This had some counterintuitive findings. I expected cities like NY and SF to top the list. The conventional wisdom is that rent should be around 30% of one's gross (pre-tax) income.

Analysis: In 2024 the typical renter household in the Miami metro spent 38.6% of its income on rent and utilities, the most of the 25 US metros with the most renters (next: Orlando 35.8%, Tampa 35.6%). Nationally the figure barely moved, 31.6% in 2010 vs 30.9% in 2024, because median gross rent rose 74% ($855 to $1,487 a month) while median renter household income rose 78% ($30,671 to $54,446 a year). Rent took a bigger share of income in 9 of the 25 metros, most in Las Vegas (+3.7 points; rent +76% vs renter income +52%) and Houston (+3.1; +74% vs +55%), and a smaller share in 15, most in Detroit (−2.2) and Chicago (−2.1). Part of why renter incomes kept pace may be who rents: Harvard's Joint Center for Housing Studies (America's Rental Housing reports) has noted growth in higher-income renter households since 2010. This data can't separate that shift from raises for people who were already renting.

Caveats: the figure is a median over renter households paying cash rent, so half pay more and half less; it doesn't show how many renters are severely burdened. Each value is a survey estimate with a margin of error of up to about a point for a single metro (Miami ±0.7), so small changes may be noise. Dollar figures are current dollars, not adjusted for inflation. 2024 is the latest 1-year ACS release.

Sources: [US Census Bureau, American Community Survey 1-year estimates](census.gov/programs-surveys/acs), 2010 and 2024: tables B25071 (median gross rent as a percentage of household income), B25064 (median gross rent), B25119 (median household income by tenure) and B25003 (renter households), read from the public ACS summary files at www2.census.gov. The 25 metros are those with the most renter households in 2024 (Puerto Rico excluded), named by their first city. Hollow dot = 2010, filled dot = 2024; red = rent took a bigger share of income than in 2010.

Tool: Artio for the data pipeline and chart; the chart is SVG drawn in Python.

Link: https://www.artio.so/dataviz/pages/2026-10-26-rent-vs-income

Blue represents net gains for Bolsonaro while red represents net gains for Lula.

The data show a net shift of approximately 2.29 million votes in Lula's favor between the 2022 election and the 2026 estimates.

The movement is geographically uneven. São Paulo (+677,000), Rio de Janeiro (+644,000), and Pernambuco (+443,000) together account for approximately 77% of the nationwide net shift toward Lula.

Conversely, the largest shifts toward Bolsonaro are concentrated in the South and Center-West, particularly Rio Grande do Sul (−208,000), Goiás (−109,000), and Santa Catarina (−106,000).

Overall, the data suggest that the national shift toward Lula is driven primarily by gains in the Southeast, especially São Paulo and Rio de Janeiro, while the South and parts of the Center-West move in the opposite direction.

Notably, Minas Gerais, the second most populous state in the country showed very little movement between these two elections demonstrating that a large population does not guarantee a large difference in absolute raw vote totals.

Data Pipeline: Apple Watch -> Marrow Health -> InfluxDB -> Visualization

Random insights:

• 3.3 Million steps, max of 29,649 in a day
• Spent 29% of the year asleep
• 21% of my sleeps started after 2 am 🥀
• Burned the most calories between 6-7pm
• Sound is 7-8 dB higher on Friday/Saturday afternoons than other days
• 167 hours over 85 dB, the level where long exposure damages hearing 😔

The python code to make the visualization is open source if you’d like to see your own rings. Marrow Health is developed by me, but you can use your own exporter as well.

Why “updated”: the first version of this was removed by the mods because the chart was really a table of names, not a chart. Fair. Same data, now drawn properly: bar length is how many times more common that age is in the sub than across Reddit, women compared with women, men with men.

A few of you asked for more, so the gallery adds two charts from the same data: Reddit’s 735 biggest subs by median age and share of women, and how Reddit got older and more female since 2019.

Only people who said it themselves count, things like “I’m 34” or “(29F)”: 17.7M accounts.

Top 5 for every age group and the CSVs: https://kissmypost.com/research/reddit-age-gender-by-subreddit

How ages and genders are found, how accurate it is, and the full methodology for each chart are in the first comment.

FAQ from the first thread:

Q: How do you know people’s ages?
A: Only people who said it themselves count, like “I’m 34” or “(29F)”. The age can come from anything they wrote anywhere on Reddit, so a D2 player who said “I’m 35” in a fitness sub counts in r/Diablo_2_Resurrected.

Q: Isn’t that a biased group?
A: Yes, it’s self-selected: in 2025 about 13% of accounts that posted or commented had a known age. That’s why every comparison stays inside the group, r/osrs men vs all men who said their age.

Q: How does r/retirement win for both women and men?
A: Women are compared with women and men with men, so one sub can top both columns. 21% of the women with a known age in r/retirement are 63 to 67, vs 1% of women across Reddit.

Q: Menopause is obviously women, isn’t that cheating?
A: A sub can’t win for being mostly women, since women are only compared with other women. r/Menopause wins because women 48 to 52 are 8.5x more common there than across Reddit.

Q: Why not r/gaming or other big subs?
A: They have every age in them, r/gaming’s median author is 27, so no single age stands out. The chart shows where an age stands out most, not where most people that age post. Chart 2 has the big subs.

Q: Why r/osrs and not r/2007scape?
A: r/2007scape is the main sub, but it skews younger (median 27), so the smaller r/osrs wins the 28 to 32 row.

Brooklyn’s home-price index rose 22.1% between 2019 and 2025. Over the same period, U.S. consumer prices rose 25.9%. In purchasing-power terms, that leaves Brooklyn down 3.0%.

The Bronx, Queens and Staten Island remained above their 2019 levels after inflation. Manhattan did not.

Each comparison shows the same neighborhoods before and after inflation, with a consistent color scale across all five boroughs. Hatching indicates greater uncertainty, gray indicates limited data, and some neighboring areas share a grouped figure.

These are home-price changes, not an owner’s total return: rent, financing and ownership costs aren’t included. They also don’t measure affordability or the value of living somewhere you love.

Data: NYC Department of Finance, FHFA, NYC Planning neighborhood boundaries, FRED, and Good Investment.

Tools: Python, GeoPandas and Matplotlib. Analysis and visualization by Good Investment

See the rest?

Explore all five boroughs in the full article and interactive maps: https://goodinvestment.io/resources/manhattan-home-prices-by-neighborhood

There are more free maps and plain-English housing guides in our free resources: https://goodinvestment.io/resources

Over the past few months, we have been developing a project called 1830belgium.com, with the goal of providing the clearest possible picture of the Belgian political landscape.

One of our initiatives at 1830 involves visualizing the 2024 municipal elections at the level of individual polling stations and vote-counting bureaus. By using data released by the Flemish government—which, unfortunately, was made available only for the municipal and provincial elections—we were able, for the first time, to create a visualization showing exactly how each neighborhood voted.

We began with my hometown, Ghent. Credit where credit is due: the province of East Flanders—and by extension, the city of Ghent—was significantly more open regarding their polling stations and counting bureaus than any other province (though Antwerp also performs quite well in terms of transparency). After spending several weeks carefully weighing the data and piecing the puzzle together, we arrived at a result for Ghent that we are truly satisfied with. We then applied the same approach to Antwerp, and I must say, I am very proud of the outcome. We used data from Statbel to assign the correct names to the defined sectors; I hope I have labeled them accurately and haven’t caused any offense—rest assured, the distribution of votes is entirely accurate. That said, any feedback or nuance you might offer would be more than welcome.

I hope you enjoy exploring it and appreciate the work we have put in over the last few weeks.

Here is the link: https://1830belgium.com/Antwerpen-verkiezingen-2024

Tools used:

HTML, CSS, JavaScript and SVG for the interactive map. JSON and geographic data files for storing and processing the election results and projections.

Data sources:

• Official election results: Antwerp municipal election of 13th of october 2024, municipal-level results published by the Flemish government, department of internal affairs.
• Geographic data: StatBel
• Population data: StatBel

Every drop that is 14% or more since Sep 2003, visualized as a painting. The mountain outline as the real S&P price. Saw someone visualize one of the stocks on reddit and thought i'll try it out too.

Data: athenic.com/finance
Tool: claude.com/design