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Flow fields are something we’ve circled for a while now. Back in February I wrote about strange attractors, which are a sort of flow field, and last month we explored noise functions, which is the perfect groundwork for today’s topic. A flow field is simply a grid of vectors, which is to say, it’s a grid of numbers. Flow fields can be generated by attractor functions, images, magnetic fields, wind, or what we will focus on today, noise functions.


Turn up the noise Very few algorithms are award-winning, and even fewer have won an Academy Award. Today’s topic however, can claim this rare honor. In 1982, Ken Perlin developed the Perlin Noise algorithm to generate random procedural textures for Disney’s sci-fi classic Tron. In 1997, Ken won the Academy Award for technical achievement, in large part thanks to his eponymous noise algorithm. In this post, I’ll explore several types of noise, and the modifications we can apply to them.


Since I started learning D3.js as part of the 100 Days of Code project the number one question I’ve gotten is: how are you learning D3? I’ve had lots of people that want to know what learning resources I’m using, what my process is, and what I suggest for how to start on D3. I don’t think I’m any sort of expert, and I certainly don’t claim to have the ultimate learning process, but now that I’m halfway through the project I decided to share what I’ve learned.


I love R and I love ggplot, but there’s always been one thing that’s really irked me: the real lack of support for complex fills, filters, or other graphics effects goodness. In R, there’s basically only support for perfectly rendered shapes and solid fills. If you want something like a gradient fill, blur, or texture, you’re left to your lonesome. I really felt the pain when I discovered the magic of SVG filters and then sadly realized I didn’t have all this awesomeness in R.


How it all started Take me to the project! I started using Mapbox earlier this year and I was hooked from the start. I’ve dabbled in geospatial analysis and visualization before, but I was always put off by the huge barrier of entry. Most spatial analysis requires massive amounts of domain expertise, knowledge of specialized data sources, and a huge time investment to gather the data, clean it, and harmonize it to all work well together.



My CV is available in HTML form or as a PDF.

Recent Publications

. CAZypedia: Carbohydrate Binding Module Family 63. CAZypedia, 2018.

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