Change in Presidential Vote from 2000 to 2020 by US County

Change in Presidential Vote from 2000 to 2020 by US County

This animation shows how the presidential vote in each county changed from 2000 to 2020. Every step in the animation shows the lift from 2020 with counties that voted more Republican shaded red and Democrat blue.

The blue shift towards Obama and then the Red shift towards Trump make a lot of sense. I find it really interesting how little changes between Trump and Biden.

Note that the colors represent the change in share of the vote and not an absolute measure. A country that went from 70% Republican to 60% Republican would be shaded blue due to the shift towards the Democrat vote. The vote is interpolated linearly between elections and so when you're looking at 2016 to 2020 for instance the animation shifts each county towards the votes that they will cast in 2020. Like the electorate I ignore third parties.

Data is from Harvard Dataverse. The animation and any errors introduced in its fabrication are all me.

(Related: Liquid Democracy and united.vote; Republicans and Democrats: Too big to succeed; Thinking about the UK referendum on AV)

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Space Journey

Space Journey

Animation of a trip through a random collection of stars with a bit of a hyperspace effect in the middle. I didn't mean to write this code but it's what ended up happening anyway. It would make a good mid-90s screensaver. I'll post what I was actually trying to do next.

(Related: 1,000th Post!; The Secret Diary of a Xamarin Android Developer, Aged 48 1/3; Capture DropCam (Nest Cam) frames to Google Drive)

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Stable Diffusion Watches a Security Camera - a short horror movie

Updated on Friday, September 16, 2022

Stable Diffusion Watches a Security Camera - a short horror movie

A horror movie made by forcing img2img to watch a Japanese security camera over 24 hours.

The office was empty, so the main influences are sunrise and sunset and then just subtle shifts in lighting. In terms of settings, strength was 0.75 with scale of 6.6 so the animation doesn't look anything like the actual office in question. The prompt was "photo of something unspeakable lurks in the shadows of the office, high quality, cinematic lighting, subtle horror, horrifying, japanese, found footage, sharp focus, 8k, no text". I upscaled with Gigapixel AI and then added some music.

(Related: A black lab chases a Roomba and then things start to get weird...; Stable Diffusion Animation of Scale Parameter; Time Zone Time Lapse)

(You might also like: Vernal Equinox 2015; Blogger Classic Templates Bugs and XHTML; Response to updated GGNRA Draft Dog Management Plan)

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A black lab chases a Roomba and then things start to get weird...

A black lab chases a Roomba and then things start to get weird...

Another experiment with Stable Diffusion (see my San Francisco skyline video from earlier today). This one uses img2img instead of txt2img. I started with a video of my dog following the Roomba around the house. I dumped all the frames out and then used Stable Diffusion with the strength parameter ramping up from 0.0 (source image preserved) to 1.0 (source image ignored) and a scale of 11.5. The prompt was "illustration of a black labrador being chased by a giant scary roomba trending on deviant art". The frame at the top maybe best captures this concept. I used Gigapixel AI to scale the output back up to 4K resolution and then added the original soundtrack.

(Related: Improving the accuracy of the new Catfood Earth clouds layer; Style Transfer for Time Lapse Photography; Stable Diffusion Animation of Scale Parameter)

(You might also like: Crushing PNGs in .NET; How to get SEO credit for Facebook Comments (the missing manual); Hummingbird)

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4K One Year Global Cloud Timelapse

Updated on Saturday, February 19, 2022

Hurricane Dorian in Catfood Earth

Six 4K images a day at 24 frames per second (so each second is four days) from April 18, 2019 to April 17, 2020:

I made a version of this video a couple of years ago using xplanet clouds. That was lower resolution and only had one frame per day so it's pretty quick. This version uses the new 4K cloud image I developed for Catfood Earth just over a year ago. I've been patiently saving the image six times a day (well, patiently waiting as a script does this for me). It's pretty amazing to see storms developing and careening around the planet. The still frame at the top of the post shows Dorian hitting Florida back in September.

(Related: Animation of a year of Global Cloud Cover; Time Zone Time Lapse; Crystal World)

(You might also like: Converting Blogger ATOM export to BlogML; Happy Storm; Carr is Wrong: Costolo is Wrong: Wikipedia’s SOPA Blackout is a Great Idea)

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Game of Death

Updated on Saturday, February 19, 2022

Generation 390

Conway's Game of Life, for 1,830 generations, starting from a random pattern. Instead of showing the live cells this animation focuses on death - each dead cell gets a little bit greener with each generation. You can just about make out a few static patterns in the darkness and the lines cruising through are left behind by gliders. Mostly though you're watching the horrible loss of life caused by cellular social isolation.

(Related: The Secret Diary of a Xamarin Android Developer, Aged 48 1/3; Life, Non-locality and the Simulation Hypothesis; 15 minutes of terror, or how the UK has changed in four years)

(You might also like: Liquid Democracy and united.vote; Vernal Equinox 2017; San Bruno Mountain 360 4K)

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Global Temperature Anomalies Animation, 1850 to 2018

Updated on Saturday, February 19, 2022

Global temperature maximum anomaly from Jan 2010 to Nov 2018

I made this animation to visualize climate change based on the HadCRUT 4 data (specifically the ensemble median gridded data) from the Met Office Hadley Centre.

HadCRUT 4 provides temperature anomalies in a five degree grid by month and year from January 1850 to November 2018 (as of this post). Anomaly here means deviation from the 1961-1990 average.

In the animation I wanted to capture the full timespan of the data but also show long term trends. Each frame is a month of data and each five degree grid of longitude and latitude is colored based on the maximum cumulative anomaly (positive or negative) for each decade. The range for color is 0 to +/- 20.85 degrees, red for warmer and blue for cooler. This means there is a reset at the start of each decade, the first few years are mostly random noise but by the end of each decade you're seeing the range of extremes.

Spoiler alert - you can spot something happening in the last three decades.

As well as the change in temperature it's interesting to watch the increase in global coverage over time. It's surprising that even the most recent years have no readings for Antarctica. Here's a paper (PDF) discussing the impact of the missing data. The HadCRUT 4 FAQ has more detail on how the temperature anomalies were assembled.

(Previously)

(Related: Animation of a year of Global Cloud Cover; Coronavirus Visualization Update; Improving the accuracy of the new Catfood Earth clouds layer)

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(More Timelapses)

Animation of a year of Global Cloud Cover

Updated on Saturday, February 19, 2022

Update April 19, 2020: I made a longer, higher resolution version which you can find here.

Animation of a year of Global Cloud Cover

Here's an animation showing a year of global cloud cover (from July 2017 to July 2018) :

The clouds are sourced from the free daily download at xplanet. I run a Google apps script that saves a copy of the image to Google Drive every day (basically the same as this script to save Nest cam images). The hard part was waiting a year to get enough frames. Xplanet combines GEOS, METEOSAT and GMS satellite imagery with some reflection near the poles. Although I didn't need to for this project note that you can subscribe to higher quality / more frequent downloads.

As well as the clouds you can also see the terminator between day and night change shape over the course of the year. This video starts and ends with the Summer equinox when days are longest in the Northern hemisphere.

Where it's nighttime the image is based on NASA's Black Marble. The daytime is based on Blue Marble, but blended with a different older image which has better ocean colors and interpolated daily between twelve monthly Blue Marble satellite images. The result of this is that you can see snow and ice coverage changing over the course of the year. If you look closely you'll also notice vegetation growing and dying back with the seasons.

Rendered in a slightly modified build of Catfood Earth (the main release doesn't know how to access my private cache of xplanet cloud images). As well as combining day, night and cloud images Catfood Earth can also show you earthquakes, volcanoes, US weather radar, political borders, places and time zones. It has been enlivening Windows desktop wallpaper for fifteen years now (as shareware back when that was a thing, these days it's a free download for Windows and Android).

More Google Apps Script Projects

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