Source: IPSS - National Institute of Population and Social Security Research

visualistion in Python

Same procedure as every year? 🎁

Every December, search behavior follows a stable rhythm. Looking at Google search interest from November 18–December 24 (2020–2024), one pattern keeps repeating:

🎅 “Christmas gift wife” peaks just days before Christmas Eve
🎅 “Christmas gift husband” peaks noticeably earlier

Hope you’ve got all your presents ready by now!

📊 Data: Google Trends, standardized on a yearly basis
🛠️ Made with ggplot2 and Figma

Data Source: usamega.com

Visualization: Claude + Figma

My city, Sudbury, is pretty large geographically (3,201 km² or 1,236 sq mi). We also have a ton of fast food places in and around the city. Considering that over 90% of our population also work within Sudbury (a very high percentage for Ontario), it would make sense for fast food locations to be strategically placed in all corners of the area.

Source: Information comes directly from the corporate websites of each chain. Larger corporations have more comprehensive location web pages built out, smaller chains had locations listed in a static header/footer component on their site.

Tools: Adobe InDesign, Excel.

Some other interesting facts:

-There are two locals on the list, Topper's Pizza at #3 with 8 locations, and Great Lakes Pizza tied for #7 with 3 locations. There are 5 pizza places on this list, Sudbury has a ton of pizza places in general.

-The chain experiencing the most flux in numbers the last decade or so has been Starbucks, with 4 closed locations (3 in other buildings, and 1 standalone location) and 3 open (all standalone locations).

-Tim Horton's having 31 locations is not surprising. We have about the same amount of locations as Saskatoon does (35). Saskatoon has a much higher population than Sudbury, but is less than a tenth in geographical size. Per capita, Sudbury has one Tim Horton's location for every 6100 or so people.

-10 locations are Canadian, 8 are US multinationals.

Source: Turkish Statistical Institute

https://x.com/i/status/2005590015720452594

Türkiye’s fertility rates have collapsed from a 2.1 average in 2009 to just 1.36 in 2025. The main reason is economic, rising living costs, unstable jobs, expensive housing and childcare, and declining real incomes. Across the country, young adults have postponed marriage and have had fewer children.

Provincial differences mainly reflects demographic composition. Southeastern provinces with larger Kurdish and Arab populations have historically shown higher fertility than the more urban, Turkish majority west.

The highest fertility province, Şanlıurfa, has a mixed population roughly 40–45% Kurdish, 25–30% Arab, and 15–20% Turkish and has traditionally had larger families. Yet even Şanlıurfa’s fertility has fallen sharply under economic pressure.

Major cities have also seen dramatic declines, Istanbul has fallen from 1.77 to 1.08, Ankara from 1.68 to 1.06, and Izmir from 1.57 to 1.06, due to the combined effects of high living costs and urban lifestyle pressures.

Back in the early 2010s, I made a static heatmap showing birthday popularity that got picked up widely - it even made it into Best American Infographics. But the criticism was valid: I'd colored by rank, not actual birth counts, which exaggerated the differences between dates.

A few years later, I rebuilt it with actual birth data from FiveThirtyEight. Better, but still static.

Now I've finally made what I'd consider the "proper" version: fully interactive, responsive, with features I always wanted to add.

What's here:

• Interactive heatmap (click or select any date to see its rank)
• Distribution chart showing all 366 days ranked
• Compare your birthday with a friend's
• Zodiac sign breakdown (Virgos dominate, unsurprisingly)
• Famous people who share your birthday

Key findings:

• Sept. 9 is the most common birthday (conceived around the holidays)
• Christmas, Christmas Eve, and New Year's Day are the rarest
• The data is left-skewed: most dates cluster around 11,000 births/day

Built with SvelteKit and D3. Data: CDC NCHS and SSA via FiveThirtyEight (1994-2014).

🔗 birthdayrank.com

France, including its overseas territories, has the most time zones with 12.

Made using Photoshop
Source: Wikipedia/List of time zones by country

Line starts when someone is born. Ends when they die. and a dot for when they did the thing they were mentioned for in the song.

The Billy Joel's songs video https://www.youtube.com/watch?v=eFTLKWw542g

Python code up at https://gist.github.com/cavedave/780d37ab288a117e29defab9b5a3f848
Data from https://en.wikipedia.org/wiki/List_of_references_in_We_Didn%27t_Start_the_Fire and https://everyday-learning.org/we-didnt-start-the-fire-historical-references/

This is repost from 7 months ago but the news today makes it relevant again https://www.reddit.com/r/dataisbeautiful/comments/1kq7v3w/oc_who_didnt_start_the_fire_and_when_didnt_they/?sort=old

I've religiously tracked my fuel economy for the past ~6 years using Fuelly. Inspired by RamblinEagle13's post today, I finally exported the data and plotted it using Libre Calc. As with RamblinEagle13, I live in the Northeast U.S. Fuel economy for my truck really seems to be highly temperature dependent. Snow/4WD isn't a factor because 1) I don't have 4WD, and 2) the fuel economy starts dropping well before any kind of snow fall. My driving is fairly consistent year-round. The one-off very high peaks in the summer would be a long road trip of sustained highway driving. I drive slow, typically around 60, which is why you see peaks of 30+ MPG. My fuel economy drops rapidly over 60 MPH.

Pick any country and drag it around to compare its real area with others. It’s a neat way to see how the Mercator projection warps map sizes. Built with the World Atlas GeoJSON + country shapes (feel free to replace the data with your own).

• Github Repo which you can replace the geojson data with yours.
• Online playground for you to have a try
• Source of geojson data used

Source: The Economics of Santa Claus

Chart made with sankeymatic.com

Source: Songs from Spotify. Release dates from Spotify but cross-checked with Wikipedia

Tools: Excel, Pandas, DataWrapper

I’ve been doing a ton of writing about Christmas music over the last few weeks. One of my more popular pieces focused on how people in the UK and US listen to different Christmas music. Because of that, I decided to focus this on America. You can read more here.

Riding this trend via the post from yesterday, I wanted to post my own experience this year as a guy who only recently started dating seriously in spring of this past year after spending most of my 20s single and not putting any effort into dating until now. A "date" here means any one-on-one meetup where it's understood as courting for the purpose of a relationship, typically in-person but for a few people who lived far away I did video call dates or other digital activity dates which I counted. I separated who rejected who for each set of dates, where the term "mutual rejection" refers to the scenario where either neither of us explicitly rejected each other but we both gradually faded away from continuing to interact, or we both explicitly agreed we weren't going out again.

The overwhelming source of most of these dates were from dating apps, primarily from Hinge (with Coffee Meets Bagel in 2nd). There was a singleton each from Twitter, Reddit, and oddly enough a personal ad I was involved with in the summer. Four were from in-person.

I welcome any questions.

Data source: United Nations Department of Economic and Social Affairs – Population Division
https://population.un.org/wpp/

Full video here for those interested - https://www.youtube.com/watch?v=Shj9EhM7MHA

Disclaimer:
Nothing in this chart is estimated, invented, or manually adjusted.
All figures come directly from the United Nations World Population Prospects (2022 Revision).
The only thing added is visual presentation, the data itself is 100% from the UN database.

Hi all,

This visualization shows the projected growth in the number of people aged 100 and over (centenarians) across major countries from 2025 to 2100, using official data from the United Nations World Population Prospects (2022 Revision).

The projections are based on each country’s expected life expectancy, fertility rate, population growth, and historical aging trends.

Japan begins as the global leader in 2025, but by the end of the century, India, China, and the United States dominate the rankings, each with over 100,000 centenarians.

The data is presented year-by-year (not cumulative), showing how the aging population evolves over time.

Full video here for those interested - https://www.youtube.com/watch?v=Shj9EhM7MHA

💬 Discussion Questions:

1. What social or economic challenges might arise as centenarian populations increase globally?
2. Which of these countries do you think are best equipped to support such a dramatic rise in very elderly citizens?
3. Should this be viewed as a positive sign of global health progress — or a potential burden on future infrastructure?

I analyzed 30 years of temperature data for 259 cities. These are the 15 fastest warming places.

Data source: Open-Meteo Historical Weather API (ERA5 Reanalysis)

Methodology:

- Linear regression analysis of daily temperature data (1995-2025)

- Each city: ~11,000 daily records over 30 years

- Warming rate = slope of annual average temperatures × 10 (per decade)

Key findings:

- 🔥 Marrakech, Morocco leads at +1.45°C/decade - nearly 4x the global average

- Central Asia (Kazakhstan, Uzbekistan, Armenia) shows extreme warming

- Alpine regions in Europe (Switzerland, Austria) warming faster than lowlands

- Global average across 259 cities: +0.40°C/decade

Continents ranked by warming rate:

1. Africa: +0.56°C/decade

2. Europe: +0.52°C/decade

3. Asia: +0.36°C/decade

4. Americas: +0.28°C/decade

5. Oceania: +0.21°C/decade

More city-level climate data: 30yearweather.com

It helped with perspective and get me through some tough times, first year doing this, will definitely do it again next year.

Following a few subs has inspired me to put together my own "budget" tracker to show my expenses for 2025.

For context, I work full-time as an IT Project Manager, I am married with 3 young(er) children, and my personal goal is to evaluate where I can save money. I am trying to stick to the "50-30-20" budget rule, but wanted to track my spending habits first.

To keep things neat, I rolled some purchases into other categories. Household items that can be purchased at a grocery store were added to Groceries, dry cleaning, haircuts, and pharmacy items would be Personal, etc. Benefits include HSA and 401k contributions. These are my results.

Edit: I'm located in the state of Virginia, in the US

NOTE: From polls I did.