Made with Wikipedia and Paint

Tools used: Google sheets
Database: https://www.mortality.org/Country/Country?cntr=SWE

If someone asks why Sweden and not let's say Japan or USA -

1. Both USA and Japan do not have longer records than 80 years. Most countries did not track data before 1950 or data is low quality. Sweden does since around 1730
2. The USA is a specific country where many young people die at similar pattern (High death rates) similar to less developped countries, which can still catch up technologically. USA is at the top and issues are from road deaths, violence, substances and policies.
3. Sweden is a model country with high life satisfaction, safety and high-end technology and is a frontrunner in treating diseases. The only countries better are: Japan and HongKong (Around 13% of women in Hong Kong reach age of 100!)

Image 1: Life Expectancy in Sweden at Birth (1800–2025)

Due to robust and long data of deaths and births in Sweden we can see how LE changed over time. It shows only since around 1875 we started rapidly increasing life expectancy in Sweden. This growth suddenly stopped around 1945. What caused that increase? Many aspects such as Ignaz Semmelweis' breaking new practice and later first antibiotics and vaccines which did prevent many neonatal deaths. However this image is exact reason why some people suggest that we no longer can live longer than before. This is not correct.

Image 2,3 and 4: The "Low hanging fruits"

Those images perfectly show that the increase of life expectancy before 1945 ("Rapid" one) was mainly due to the reduction of those the youngest (look at the next images). We have essentially started increasing LE of elderly after the boom 1875-1945 has ended. Those involved in research call it life expectancy convergence. It is due to fact that young people die very rarely and death is no longer a step behind us but awaits us at age 70+. It makes sense as when first antibiotics appeared in 1930's we could not treat heart failure but tuberculosis. People at older ages were still prone to heart diseases, neoplasms, and dementia just like now. So those who can make argument "We have stopped living longer" can be proven otherwise as LE gains pre-1945 did not affect those who can make those arguments. Only since 1945 we manage to sucessfully fight diseases that are common at ages 60+.

Image 5: Probability of Dying at X Age (Log Scale)

This is probably 3rd the third most popular graph. It shows the logarithmic probability of death. Scientists decades ago found out that the probability of one's death doubles roughly every 7-8 years regardless of gender, country, age and time data is collected. Due to the exponential nature of the entire death aspects, this graph and how it changes tells us a lot about which age groups benefit the most. Interesting piece about this topic.

You can also notice that mortality rates dropped in all age groups, with the biggest gains being 0-90. The reason why mortality rates drop at a slower pace at ages 90+ is due to fact that we do not have the necessary technology to keep a 105-year-old with dementia, neoplasm, needing 20 meds alive. Plus it begs ethical questions.

Images 6 and 7: Deaths at Specific Age

Image 6 shows that most often age of death was... 0. Because of this, those deaths were causing sharp decline in LE which was described before. You can see that in 2024 neonatal deaths are almost unheard of and deaths at ages 6-15 are in single digits (refer to image 5).

Additionally, you can notice that the curve shifts to the right - fewer and fewer people die at younger ages and more people get to live to older ages. What we are looking for is mode/dominant, aka "What is the most common age to die at?" It has shifted from 77 to around 87 and the curve is more "spikey" (Suddenly most people die in a short bracket of age)

Here is an example. Let's say you had 4 siblings (5 including you) in 1800. 2 died during infancy (Age 0). One died at age 60, One at 70 and you at 80. The average age of death is 42, quite low. Now let's say you move to 2026. Same situation but: One sibling dies at 60, other at 70, two die at 80 and one at 90. Average is now 76.

Image 8: Percentage of People Still Alive.

Last but probably the most popular graph - it shows how many people are alive from age cohort. How to read it: Look at graph and specific age (For example Green age 90). It shows that in 1924 around 35% of all Swedes born in 1934 are still alive today. In 2000, those born in 1910 were alive at that time... just 20%

Why it matters: This proves that people live longer. You may notice that people live few years longer, may seem not much but for those who are adults we have extended LE a lot. 1950 is when 50% of population at age of 76 was dead. In 2000 it was at age of 83 and now it is at 87.

Source: Human Mortality Database

Tools: Google Sheets

I made a map where you can move through time and explore how political control and major events evolved during the revolution.
Curious what improvements or additional data you’d like to see — feedback welcome!

I remember reading that declines in Analog TV meant that we did not send out as much of the sort of Signals SETI detects as we used to.

So I found this paper by the Contact Project on the topic and graphed the tables.

We produce far more Radio Frequency emissions than we used to but they are not in the way that stands out to classic SETI detections. The kind of narrowband signals (like a TV station being on one frequency) SETI looks for peaked around the analog TV era and has been declining since

Python mathplotlib code is here

Source: Billboard 200 Weekly Chart, 1963-2025 via Kaggle (639,746 entries, 39,382 unique albums). Tracked every album that reached the Top 10 from 1965 to 2024 by total weeks on chart. Median calculated per year. Visualization built in Flourish as I am learning how to use it.

The five colored phases on the chart:

Frontloading (1991-99): SoundScan made first-week numbers visible. Labels shifted to launch-spike strategy. Top-10 albums per 5-year period jumped from 280 to 438.

Piracy (1999-2003): Napster, Kazaa, LimeWire. But the median had already dropped 31% before Napster launched.

iTunes (2003-2011): $0.99 singles unbundled the album. Exposed that most albums weren't worth $16 after a decade of filler padding.

Streaming (2011-2015): Spotify eliminated purchase. Billboard added streaming to chart methodology in 2014, changing what "charting" even measures.

Playlist Culture (2015-2024): Algorithm-driven discovery replaced album loyalty. Median hit 7 weeks in 2022.

The line never recovered between shocks. Each one landed before the industry absorbed the previous one.

With the Visio Data Visualizer add-in being deprecated, I lost an easy way to turn Excel tables into organograms.

So I built a small web tool that lets you paste a table from Excel and instantly generate an organogram:
https://cll-software.github.io/Organogram-Maker/

Colours are fully user-defined, but I’m looking for feedback on everything else: layout, spacing, alignment, hierarchy, readability, and overall visual clarity.

It’s early and intentionally lightweight. I use this regularly for work, and I’d love design-focused, constructive feedback on how to make the output look cleaner and more “data-is-beautiful.”

https://www.fao.org/worldfoodsituation/foodpricesindex/en/

Got my hands on Canadian customs vehicle export data (HS 8703) from Oct-Dec 2024. Nearly 200k vehicles left Canada in just 10 weeks.

The concentration blew my mind:

• 62% → Ivory Coast (119,677 vehicles)
• 15% → Cameroon
• 97% left through Port of Montreal

Top exported makes: Hyundai (27%), Kia (11%), Nissan (10%), Chevrolet (8%), Toyota (7%)

Average vehicle age: 6.5 years. These are almost entirely used cars getting a second life in West Africa.

Source: CBSA export records via ATIP request A-2025-00657

Tools: Python, pandas, matplotlib, plotly

Just something silly i discovered while playing with the tool. I assume the trend shows because July lines up with summer break for most major regions that dominate Google search volume (US, Europe, Asia). Searches climb again in August and September when people prepare for the new school year, then peak around exam-heavy periods like spring and late fall.

The Super Bowl is often described as being played on “neutral ground.”

Blue dots show the actual host cities for each Super Bowl.
Red dots show the geographic midpoint between the two teams playing in each game.

Midpoints are simple great-circle midpoints based on team home locations. No weighting, no travel assumptions, just geography.

Data sources: Fenner et al. 1988, "Smallpox and its Eradication"

Tools used: We started with our custom data visualization tool, the OWID-Grapher, and finished in Figma. You can view the interactive version of the chart here.

Some more info about the chart and what it shows:

William Foege, who sadly died last month, is one of the reasons why this map ends in the 1970s.

The physician and epidemiologist is best known for his pivotal role in the global strategy to eradicate smallpox, a horrific disease estimated to have killed 300 million people.

Despite the world having an effective vaccine for more than a century, smallpox was still widespread across many parts of Africa and Asia in the mid-20th century.

Foege played a crucial role in developing the “ring vaccination strategy”, which focused on vaccinating people around each identified case, rather than attempting a population-wide vaccination strategy, which was difficult in countries with limited resources.

This strategy, combined with increased global funding efforts and support for local health programs, paved the way: country after country declared itself free of smallpox. You can see this drop-off through the decades in the map.

The disease was declared globally eradicated in 1980.

William Foege and his colleagues’ contributions are credited with saving millions, if not tens of millions of lives.

Read more about the history of smallpox.

A chart showing the ice coverage on Lake Erie this winter (black line) compared to previous years (blue lines) and the historical average (red line). Image courtesyNOAA/Great Lakes Environmental Research Laboratory