Category "Visualization"


Here’s a tutorial of how to make an animation of the moon’s shadow with GOES imagery during The Great American Eclipse of 2017:

Here is one of the coolest examples that I have created using IDL in a while. For this blog post, I’m going to walk through how I created an animation of the Moon’s shadow during the Great American Total Solar Eclipse using several different technologies for accessing, downloading, and visualizing the data.

The video is on Harris Geospatial Solutions’ Facebook page.

The dataviz.tools site is an annotated and categorized catalog of good visualization tools.

This site features a curated selection of data visualization tools meant to bridge the gap between programmers/statisticians and the general public by only highlighting free/freemium, responsive and relatively simple-to-learn technologies for displaying both basic and complex, multivariate datasets.

via FlowingData

Some great tips for spotting misleading visualizations:

By using dual axes, the magnitude can shrink or expand for each metric. This is typically done to imply correlation and causation. “Because of this, this other thing happened. See, it’s clear.”

There are some great links as examples of these problems, like the spurious correlations project by Tyler Vigen to automatically find correlations.

The Mathematics Genealogy Project is an amazing effort to record basic information about every mathematician in the world. We can create a family tree for any mathematician. Here is my tree:

For a description of how to create the graph of another mathematician’s genealogy, see Dana C. Ernst’s article.

I have been doing some reading about machine learning recently, using Python as an implementation language. I lot of the routines used are fairly easy to implement in IDL, so I have started filling out my library with IDL versions.

I have written a scatter plot matrix routine that takes a collection of vectors and makes all the scatter plots between pairs of them. For example, here’s a scatter plot matrix produced by the routine for the classic iris dataset:

If you want to use the routine, it’s probably easiest to clone my entire library.

FlowingData rounds up his list of best visualization projects of 2016:

Visualization continues its merging into the everyday — less standalone and more of a medium that blends with words. I think this is partially because of a concentration on mobile. There’s simply less visual space on a phone than there is a giant computer screen, so the visualization is stripped or split up into smaller pieces that are more easily digested while scrolling.

For example, the Rhythm of Food shows the popular food by time of year:

And if that is not enough for you, here’s a roundup of 2016 visualization roundups.

National Geographic collects the best maps of 2016:

It’s been a good year for map lovers. Whether you’re into old maps, new maps, or new ways of interacting with old maps, there was much to cheer about in 2016.

via kottke.org

The NSF has opened up voting for the People’s Choice for visualizations in the Photo, Illustration, Poster/Graphic, Interactive, and Video categories.

Voting closes Sunday December 4 at 11:59 p.m. PST.

D3 in Depth:

D3 in Depth aims to bridge the gap between introductory tutorials/books and the official documentation.

I have found D3 extremely useful for creating dynamic plots on dashboard style websites for monitoring data pipelines. This looks an excellent resource for learning it.

via FlowingData

Colorgorical is an alternative to ColorBrewer with a few different options for creating color tables. For example, you can add a couple specific colors that should be in the color table and let Colorgorical figure out the others which maximizes the perceptual difference between the colors. Colorgorical seems particularly well suited to generating qualitative color tables, e.g., to find sufficiently different colors for each line in a plot.

via FlowingData.

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