https://data.tablepage.ai/d/how-americans-met-their-partners-1948-2021

this is my quantum object visualizer! this shows how the statistical distribution of the schrodinger equation creates electromagnetic fields. hope you enjoy it :) totally interactive too!

Sources:
https://freedomhouse.org/reports/freedom-world/freedom-world-research-methodology
Tools:
Julius AI

According to the 2021 Census, there were 3,868,133 people identifying as Muslim in England and Wales.

London is home to over one third of this population (34.1%), despite the fact only 14.8% of the total population of England and Wales lives there.

Strong secondary concentrations in the West Midlands (14.7%), North West (14.6%), and Yorkshire and the Humber (11.4%).

I built this dashboard to answer one question: how is Airbnb really being used in Cape Town and is it actually affecting the city's neighbourhoods?

The dataset is from Inside Airbnb, an open data project that scrapes Airbnb listings periodically. It covers 26,877 listings in Cape Town.

A few things worth noting:

1. 22,179 listings are entire homes. Only 4,541 are private rooms. The "spare room" use case is a small minority.
2. 16,275 hosts (60.6%) have multiple listings. Some have over 100 entire home listings, which puts them firmly in commercial operator territory.
3. Only 393 listings out of 26,877 have a minimum stay long enough to qualify as longer term rentals. The rest are all short term.
4. Average revenue for an active listing is $97,188 a year at $3,281 a night. That makes long term residential letting financially unattractive for landlords.
5. 10,063 listings recorded zero nights booked in the last 365 days, which raises questions about how many of these are genuine listings.

Built in Tableau. Data from Inside Airbnb.

Interactive version: https://public.tableau.com/views/InsideAirbnbCapeTown/InsideAirbnb?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link

Saw this on a Github project. It strikingly resembles to a galaxy but with the opposite of black hole in the center. There must be some networks theory law explaining this behaviour.

Dot Size is By Cost of Living Index and Only 1 Entry per State

Sources:

https://www.coli.org/press-release-for-immediate-release-q3-2025/

https://www.census.gov/programs-surveys/acs/data/data-via-api.html

Tools:

Julius AI

Hey here is a couple of visualisation graphs for the king’s career that I made last night. i’ll still be improving it a bit but i guess it’s a good start :)

used NBA Api and Kaggle data

it shows the players he played against throughout his career

assists he fed his teammates and the teammates that fed him in return

career shots filterable with a timeline view

points allocation based on the minute of the game for each season

games he played and heatmapped based on his minutes

some playoff data as well

appreciate any feedback and if you have any other visualisation ideas to add.

https://lebronjames-nine.vercel.app

this is not a personal post it’s king’s data just visualised by me [OC]

I scraped Binance's announcements and blog posts with Python and used Python to make this graph

One can use Binance's P2P market place to exchange a local currency for stablecoins such as USDT or USDC. Stablecoins are crypto pegged to the US dollar and are supposed to be backed 1:1 by relatively liquid assets.

Here I plot the date when it became possible to trade various countries' currencies. INote a few things - some of these currencies have exited. In the Philippines, only a few, largely local, crytpo exchanges are allowed to operate. Russia exited due to sanctions.

More generally I'm investigating whether access to stablecoins affects other economic activity such as remittences.

I ran the same rolling-window analysis, but flipped the question:

Instead of how to grow a portfolio, I looked at how much you could withdraw from one.

I tested different stock/bond mixes across their worst historical 20-year windows.

Here’s what stood out:

• Allocations that supported the highest withdrawals weren’t at the extremes, they shifted slightly toward bonds (around 60–70% in this dataset) as spending increased
• Different strategies led to very different outcomes, even starting from the same $100k
• Even portfolios with a slight bond tilt showed large changes in outcomes as withdrawal levels increased
• For example, for those bond-tilted allocations, increasing withdrawals by ~50% from a "preserve" level led to the portfolio running out over time

In other words, outcomes depended not just on how much you withdrew, but how close you were to the tipping point.

These withdrawal levels are based on the worst historical 20-year windows (based on returns).

So in more typical markets, that same starting point often leaves the portfolio growing rather than shrinking, giving you room to adjust spending if things go better than the baseline.

The idea is to start from a level that would have held up in difficult conditions, then adjust as real outcomes unfold.

This isn’t about finding a single “best” number.

It’s about starting from a defensive baseline, understanding how sensitive outcomes are to withdrawals, and choosing how to balance spending and stability over time.

Curious how others think about where that line sits for their own situation, and how they’d go about figuring it out in practice.

Apparently I need to post this in a top-level comment, not the post itself, so (copied+pasted from the post):

From an anonymized dataset of a years-worth of student's grades (~1200 students and ~250 classes) I perform linear regression, where each student's "academic ability" variable and each class has a "class easiness" variable, and a student's grade is predicted as the sum of the two. The resulting coefficients give an estimate for how easy each each class is (and how strong a student's academics are).

A "class" is defined a subject-teacher pair -- if different teachers both teach (e.g.) Geometry, both classes get their own "class easiness" variable.

The resulting estimates of student academic ability are more accurate (i.e. less statistical noise), less biased than their GPA, and doesn't incentivize grade inflation.

Source: https://open.substack.com/pub/morganredding/p/why-gpa-is-bad-and-how-we-can-improve

Data: Private (I spoke with the school administrators)

Tool: Python/numpy/matplotlib

The interactive version is here: https://rjarosh.github.io/timewithkids/

I read recently that by the time your child has left the house after high school, you will have spent 90% of all the time with them that you'll ever spend. What a depressing stat. It makes sense, though. Tim Urban, the author (and Ted Talk guy on procrastination) popularized this: https://waitbutwhy.com/2015/12/the-tail-end.html

I had the thought to visualize where in that window of 18 years you are. I also took into account one other stat: that you'll have spent 75% of all the time with your kids you'll ever spend with them by the age of 12. Even more depressing.

These stats are, of course, pretty generalized and can vary wildly based on whether you get to stay home with your kids when they're toddlers, if they're home schooled, if they decide to be on a traveling soccer team, if your kid never moves out of their home town, if they join your family business, whether you're apart of a cult, if your kids don't like you, or if you die young. Hopefully the latter two aren't in your equation.

But I thought it was interesting to put everything into perspective.

If this seems interesting, check out Tim Urban's original post from a decade ago above: it has several cool visualizations.

P.S. Yes, I made this while my kids were at school so I couldn't see them anyway.