The land use of different foods, to scale, published with the European Correspondent.

Data comes from research by Joseph Poore and Thomas Nemecek (2018) that I accessed via Our World in Data.

I made the 3D scene with Blender and brought everything together in Illustrator. The tractor, animals and crops are sized proportionately to help convey the relative size of the different land areas.

Source: https://www.readtrung.com/p/economics-of-taylor-swifts-2b-eras

Diagram made with https://sankeymatic.com

Full post here: https://www.ballardgames.com/tales/hiring-dev-2025/

Sources: pushshift dump dataset containing text of all posts and comments on r/relationship_advice from subreddit creation up until end of 2024, totalling ~88 GB (5 million posts, 52 million comments)

Tools: Golang code for data cleaning & parsing, Python code & matplotlib for data visualization

Data tracked initially on a notebook and then later directly in Apple Numbers using a shortcut. Plotted using Apple Numbers.

Very consitent trend with peaks in ~July and valleys in ~January. For context, I live in the northeast US, so this is likely a combination of factors including variable road conditions, increased use of 4WD, and gas additives. My actual truck usage does not change appreciably over the course of a year.

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UPDATE: Well, this got much more attention than I was expecting! I see the comments on the X-axis making things less visually appealing and harder to read, and I agree. I'll post an updated image with better axes (still really just a direct output of the spreadsheet software) in the comments, but I can't add it to this header.

Numerous people have noted that air temp is probably one of the biggest factors that I did not include in my initial post. Excellent point, and it would be interesting to plot this vs. my local air temp over time if I can dig that up!

Some extra details about this data:

• My truck is a 2018 Chevrolet Colorado 1LT with the V6 engine option and a crew cab
• Total mileage at the last data-point is 133,748 miles. Data represents 387 unique points.
• MPG is calculated the old-fashioned way at each fill-up by dividing the number of miles driven between fill-ups by the gallons added.
  
  • Accuracy using this requires that I actually FILL the tank each time, which I do.
  • The truck also has a built-in mileage tool in the dash using the trip calculator, and for a while I also used that to see if there was a difference. Data agreement was very good (+/- ~.1-.2 MPG), so I stopped doing both and now just do the manual calculation. I also track cost and a few other metrics, so it's easier to just do everything one way.
• The truck gets regular and scheduled maintenance.
• I do not use specific snow tires in the winter. I use all-terrains all year.
• I don't tow much with the truck, but the bed is utilized pretty heavily.
• The truck is used for commuting and transporting various things in the bed throughout the year. There is not a significant difference in utilization b/w seasons.

Several comments requested I determine the best-fit sinusoidal equation and post it. To capture the linear degredation, below is the best sinusoidal+linear fit I've been able to get:

MPG(t) = R * sin( 2*pi()/P * (t-t0) + phi ) + m*(t-t0) + c

where...

• R = 1.3822
• P = 365.5687
• t = date of interest
• t0 = initial date
• phi = 2.1102
• m = -.0005112
• c = 20.8878

There have also been some requests for the full data. Not sure the best way to share that, but will update here with it when I can.

A new chart explained in my Substack. Created with matplotlib in Python.

Data comes from NHS England.

https://blog.waldrn.com/