Index made of a mash of companies over the age of 70

Over the past few years I’ve been working on a game about copying famous paintings as quickly and as accurately possible with a mouse. While showing prototypes at exhibitions, I saved PNGs of the "forgeries" produced.

I realized that taking the average of the forgeries made of a given painting could be cool—similar to Jason Salavon’s aggregated portraits (whose work I love). I love the ghostly/historical feel of these types of images.

I've also posted an image that includes miniatures of the 256 Mona Lisa forgeries averaged in order of accuracy (i.e., highest scoring at the top left, lowest in the bottom-right). I’ve just started saving brush stroke data too, so I can make time-lapse replays of paintings being made.

I’d love feedback on two things:

1. Other visualization ideas I should try? I did a sliding-window average that turned out very cool. Aggregating stroke data?

2. Other types of data I should capture for future data viz or studies? I'd need to implement it soon since it's release is coming in the next few months.

Thanks in advance!

I can share a link to the game in the comments for those curious / if it helps with feedback.

6 Years ago, I posted a graphic about American home prices: https://www.reddit.com/r/dataisbeautiful/comments/dxgshs/the_booms_and_busts_of_american_home_prices_oc/

I have received many requests to refresh the data. Now that the Census data has been released for 2024, I am updating with newly provided information. Values are adjusted for 2024 inflation adjusted dollars. For some reason, I used 2010 inflation adjusted dollars in my last visualization.

Source: https://www.census.gov/construction/chars/current.html
Tools: Excel

Graphic by me, created in Excel. All data from US Census here: https://www.census.gov/data/tables/time-series/demo/popest/2020s-total-metro-and-micro-statistical-areas.html

I've created similar graphics in the past, but usually from 2020-2024. This is not the best time frame as it combines the abnormal covid years with post pandemic movement.

This time frame (2022-2024) shows the most current and ongoing population trends of the last 2 years.

I also wanted to better categorize the cities into broad cultural regions vs the arbitrary geographic census regions.

Reposting with updated data , the 2012 composite used a different method and partial coverage, which made some regions (like Thailand) appear darker. This version uses average annual masked VIIRS data for a fairer 2014–2024 comparison.

Yesterday I created a graphic showing Canadian visitors to the US over time, today I wanted to expand that topic by also showing Canadian visitors to all other countries.

The top graph is raw numbers by week, the bottom graphic is the percentage of US vs non US travelers. I also included total July numbers for every year in the top graphic for reference.

Created with excel. US data is combined automobile crossings and air, all other countries are air only.

Sources: https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=2410005701 And https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=2410005601

Methodology & scope:

• Universe: Publicly traded companies with ≥25% of revenue from defense‑related products/services.
• Source: Market capitalization (USD) as of Aug 2025, sourced from MarketCapWatch, Nasdaq’s 2025 defense stock review, and Forbes’ 2025 defense picks, cross‑checked with recent filings.
• Inclusions: Dual‑sector aerospace & defense firms (e.g., Boeing, Safran) where defense is a major revenue driver.
• Exclusions: Fully private/state‑owned entities (e.g., Rostec, NORINCO) without a listed arm.

During my last pregnancy, I was even sicker but never took the data -- this time, I decided to record. FWIW, there was another vomiting episode in week 18-19, but I'm limiting this to first tri only.

Graphic by me, created in Excel.

Source: https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=2410005701

As a trending news topic I wanted to investigate for myself how much Canadian Tourism to the US has recently declined, and how this compares historically.

The data source I used gives the daily number of Canadians returning from the US by car - I converted this to weekly totals as the daily graph had a lot of variance and was harder to read.

I highlighted every July for easier comparison.

Phone GPS data from my last cycling trip, which was aborted after an unanticipated encounter with a physical object of roadway non-conformance, resulting in aerial excursion and rapid uncontrolled deceleration.

Hit a rock in the middle of the cycle lane and fell off my bike.

The phone was in my pocket at the time, so all motion tracks me and not the bike. Since after a certain point in the journey we parted company.

I also did not see the rock at all, so there was no braking or loss of speed before impact. In fact, I was still getting faster due to clearing the brow of the hill. Speed would have topped out at 45kph if I'd encountered the rock any further downhill.

Dislocated shoulder with a small fracture and plenty of road rash. Wear a helmet, because I did and it definitely prevented much more serious injury.

Created using matplotlib, pandas for some basic data restructuring, curl to download the data, pycurl to automate some of the process.

Source of the data is awt.cbp.gov from August 19th 2022 to August 18th 2025. Their policy is to make data of the last 3 years available, regardless of how much they have collected. I filtered out August 2025 data, as well as any data from 2022, since they were incomplete.

I often see rhetoric that the US visitor numbers are down this year, both in the news and by redditors who are in the industry (airports, hotels, restaurants and other tourism adjacent workplaces). I would also expect the numbers to be down, but I was curious about the numbers. I then remembered about the AWT website that I often use for travel purposes. I typically use it because I get anxious about immigration wait times, after an especially long wait time at JFK. While it provides a breakdown of average wait time for US citizens and non-US citizens, it also gives the number of flights that landed in the hour, as well as a very rough curve on the wait time (in units of 15 minutes), all in graphical format. It has been very useful to estimate immigration wait times at airports for me. Hopefully access to this information is not removed.

Analyzing the data has revealed a mixed bag; it's not easy to conclude anything from this data but there are certain correlations you can observe. I will note caveats first:

1. This is only airports, so countries like Canada and Mexico, where people can take the land option aren't fully represented.
2. This tool by CBP is used to estimate immigration wait times. Since the US forces everyone to go through immigration even if they are only in transit, the data doesn't fully represent actual visitors to the US. I am unsure of the ratio of visitors to the US vs transit, but I expect the vast majority are actual visitors, and a small, significant percentage are people in transit.
3. The US has a significant, long term immigrant and non-immigrant population that will count towards the non-US citizen section of this data. This includes green card holders (~10-15M), H1-B visas (~1M), F-1 students (another 1M), not to mention the other categories in these visas. While this population contributes to tourism industry, their effect on travel is not immediately obvious, and will require waiting for long term trends to see in the data.
4. Additionally, this is only "3" data points occurring at the tail-end of a world changing event. Obviously, the travel boom of the last few years make everything harder to predict and analyze.

Because of all these issues (that I only thought about after looking at the data), I was discouraged to find inconclusive results. Nevertheless, since I already generated this graph, I wanted to go ahead and share it. Please leave feedback on the visuals, and if you find any anomaly. I have double checked manually if the graph is accurate to the data, but you never know.

So what are the results from what I observe?

1. Overall, compared to last year, non-US citizen visits to the US are mostly down, in between '23 and '24, except for a brief spike in April, and at the start of the year. The drop from Jan to Feb is steeper in '25 (~7.9M) compared to '24 and '23 (<5.1M). It's possible that this is because many rescheduled their flight after many articles came out in these months of people being detained and sent back (in the best case scenario). However, Feb generally sees a decline in travel so it's hard to say conclusively.
2. Individual airports do not always follow this trend. For example, Washington Dulles, and San Francisco are both quite close to their 2024 numbers, before dropping off after May. Seattle-Tacoma is always higher in '25 than '24 and '23; nearly the same with Orlando. Some airports don't really see any change in their numbers comapred to previous years (e.g., Philadelphia, Charlotte/Douglas) --- these airports don't inform the larger trend because their contributions is quite small (peak 40K visitors per month). Fort Lauderdale-Hollywood Airport has much much lower numbers, to the point where I was doubting some data discrepancy (~40% less compared to '23 and '24 in some months).
3. Obviously, the largest airport dominates the overall trend: JFK has nearly identical looking graph to the overall graph. And, the smallest airports have graphs that look nothing like the overall picture, for example, Austin (or St Louis, which looks insane).
4. Of the smaller airports, one I found interesting was Fresno Yosemite International Airport that serves Fresno, Yosemite National Park, and Sequoia & Kings Canyon National Parks. This airport had a high number of non-US visitors in Jan (~10K) and Feb (~8.3K), and basically the opposite trend, but then it falls off after that. I find it unexpected because these national parks are best visited late spring/summer, certainly not in Jan/Feb when the roads will be iced. It's also high compared to the last two years. A quick glance at Wikipedia says they are expanding the airport from spring '23 which is expected to finish in Fall '25.

All airports here: US Airport Visitors

Let me know if you observe any other interesting aspect to the data.

Source: MarketCapWatch - A website that ranks all listed companies worldwide

Tools: Infogram, MS Excel

Notes:

I only included countries with >0.830 HDI >5 Millions population.

Net migration rates are a cumulative average for the last 5-10 years.

This animation shows water levels on the upper Guadalupe River from midnight July 4, 2025, to 6 p.m. July 5 (local time). The flood killed 119 people in Kerr County, including 25 girls and two teenage counselors at Camp Mystic.

Data sources

• Raw stream gauge data from the USGS was downloaded and processed to be consistent 5-minute data; it was then normalized to the average July water level at each station to get "height above normal."
• The basemap was created using data from Natural Earth, the National Hydrography Dataset, and the U.S. Census Bureau's TIGER database

Tools:

• Python for data harvesting, processing, and basemap generation
• Svelte 5, D3, and custom JavaScript for visualization

Interactive version with contextual information: https://www.willkoeppen.com/datavis/guadalupe-floods/

Source: MarketCapWatch, Simply Wall St

Tools: Infogram, MS Excel

My sheets document includes the sources, but the ranking uses 13 different sources. Sadly, not every country is included in every source so you will see blank spaces for countries that are left out in the data. I've also created a correlation index to see how different metrics matched up with each other and you can see the data I used for each ranking.

https://docs.google.com/spreadsheets/d/1YbfVevxEthNgDtK69P48Xm39bXLHi8eqfeFwxTTYEJE/edit?usp=sharing

Hope you like it, lemme know if you have any questions.

In 2011, the popular VC firm Andreessen Horowitz said "Software will eat the world" which is still their tagline.

In a recent email by Cubbie, a company which ranks the top software products, showed breakdown of spend by different software categories.

So, I put together a historical chart showing the rise of software, shown through the lens of how much companies are actually spending on it globally. I factored in the likely spend given the rise of workforce increases next year and the ongoing shift toward AI tools, which are obviously accelerating software adoption.

Tools used: Python / Matplotlib