Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

JuxtaposeJS Frame comparisons. Easy to make. Seamless to publish. (Northwestern University Knight Lab, Alex Duner.)

From the webpage:

JuxtaposeJS helps storytellers compare two pieces of similar media, including photos, and GIFs. It’s ideal for highlighting then/now stories that explain slow changes over time (growth of a city skyline, regrowth of a forest, etc.) or before/after stories that show the impact of single dramatic events (natural disasters, protests, wars, etc.).

It is free, easy to use, and works on all devices. All you need to get started are links to the images you’d like to compare.

Perhaps an unexpected use, but if you are stumped on a “find all the differences” pair of photos, split them and create a slider!

This isn’t a hard one but for example use these two images:

http://www.durusau.net/publications/ocean-beach-san-diego-alley-shopping-cart-left.png

http://www.durusau.net/publications/ocean-beach-san-diego-alley-shopping-cart-right.png

As the slider moves over a change between the two images, your eye will be drawn towards the motion. (Visit Cranium Crunches Blog for more puzzles and images like this one.)

On a more serious note, imagine the use of this app for comparison of aerial imagery (satellite, plane, drone) and using the human eye to spot changes in images. Could be more timely than streaming video for automated analysis.

Or put differently, it isn’t the person with the most intell, eventually, that wins, but the person with the best intell, in time.

Before you read The map showing which countries want the UK to stay in the EU or my comment below, a question for you:

Do countries shaded in lighter colors support the UK remaining in the EU?

Simple enough question.

Unfortunately you are looking at one of the worst representations of sentiment I have seen in a long time.

From the post:

The indy100 have created the following graphic based on the data. In the map, the darker the shade of blue, the more support there is in that country for the UK to remain in the EU. The scores are calculated by subtracting the percentage of people who want Britain to leave, from those who want Britain to remain.

That last line:

“The scores are calculated by subtracting the percentage of people who want Britain to leave, from those who want Britain to remain.”

is what results in the odd visualization.

A chart later in the post reports that support for UK leaving the EU is only 18% in France, which would be hard to guess from the “32” shown on the map.

The map shows the gap between two positions, one for the UK to stay and the other for it to leave, and the shading represents the distance between staying and supporting positions.

That is if public opinion were 50% to stay in the EU and 50% to leave the EU, that county would be colored clear with a score of 0.

Reporting support and/or opposition percentages with coloration based on those percentages would be far clearer.

Nebula Bliss

Visually impressive 3-D modeling of six different nebula.

I did not tag this with astroinformatics as it is a highly imaginative but non-scientific visualization.

Enjoy!

The image is a screen capture from the Butterfly Nebula visualization.

What I Use to Visualize Data by Nathan Yau.

From the post:

“What tool should I learn? What’s the best?” I hesitate to answer, because I use what works best for me, which isn’t necessarily the best for someone else or the “best” overall.

If you’re familiar with a software set already, it might be better to work off of what you know, because if you can draw shapes based on numbers, you can visualize data. After all, this guy uses Excel to paint scenery.

It’s much more important to just get started already. Work with as much data as you can.

Nevertheless, this is the set of tools I use in 2016, which converged to a handful of things over the years. It looks different from 2009, and will probably look different in 2020. I break it down by place in my workflow.
…

As Nathan says up front, these may not be the best tools for you but it is a great starting place. Add and subtract from this set as you develop your own workflow and habits.

Enjoy!

PS: Nathan Yau tweeted a few hours later: “Forgot to include this:”

10 Best Data Visualization Projects of 2015 by Nathan Yau.

From the post:

Fine visualization work was alive and well in 2015, and I’m sure we’re in for good stuff next year too. Projects sprouted up across many topics and applications, but if I had to choose one theme for the year, it’d have to be teaching, whether it be through explaining, simulations, or depth. At times it felt like visualization creators dared readers to understand data and statistics beyond what they were used to. I liked it.

These are my picks for the best of 2015. As usual, they could easily appear in a different order on a different day, and there are projects not on the list that were also excellent (that you can easily find in the archive).

Here we go.

As great selection but I would call your attention to Nathan’s Lessons in statistical significance, uncertainty, and their role in science.

It is a review of work on p-hacking, that is the manipulation of variables to get a low enough p-value to merit publication in a journal.

A fine counter to the notion that “truth” lies in data.

Nothing of the sort is the case. Data reports results based on the analysis applied to it. Nothing more or less.

What questions we ask of data, what data we choose as containing answers to those questions, what analysis we apply, how we interpret the results of our analysis, are all wide avenues for the introduction of unmeasured bias.

A Day in the Life of Americans – This is how America runs by Nathan Yau.

You are accustomed to seeing complex graphs which are proclaimed to hold startling insights:

Nathan’s post starts off that way but you are quickly draw into one a visual presentation of daily activities of Americans as the clock runs from 4:00 AM.

Nathan has produced a number of stunning visualizations over the years but well, here’s his introduction:

From two angles so far, we’ve seen how Americans spend their days, but the views are wideout and limited in what you can see.

I can tell you that about 40 percent of people age 25 to 34 are working on an average day at three in the afternoon. I can tell you similar numbers for housework, leisure, travel, and other things. It’s an overview.

What I really want to see is closer to the individual and a more granular sense of how each person contributes to the patterns. I want to see how a person’s entire day plays out. (As someone who works from home, I’m always interested in what’s on the other side.)

So again I looked at microdata from the American Time Use Survey from 2014, which asked thousands of people what they did during a 24-hour period. I used the data to simulate a single day for 1,000 Americans representative of the population — to the minute.

More specifically, I tabulated transition probabilities for one activity to the other, such as from work to traveling, for every minute of the day. That provided 1,440 transition matrices, which let me model a day as a time-varying Markov chain. The simulations below come from this model, and it’s kind of mesmerizing.

Not only is it “mesmerizing,” its informative as well. To a degree.

Did you know that 74% of 1,000 average Americans are asleep when Jimmy Fallon comes on at 11:30 EST? 😉

What you find here and elsewhere on Nathan’s site is the result of a very talented person who practices data visualization ever day.

For me, the phrase, “a day in the life,” will always be associated with:

How does your average day compare to the average day? Or the average day in your office to the average day?

A Cartoon Guide to Flux by Lin Clark.

From the webpage:

Flux is both one of the most popular and one of the least understood topics in current web development. This guide is an attempt to explain it in a way everyone can understand.
…

Lin uses cartoons to explain Flux (and in a separate posting Redux).

For more formal documentation, Flux and Redux.

BTW, in our semantically uncertain times, searching for Redux Facebook will not give you useful results for Redux as it is used in this post.

Successful use of cartoons as explanation is harder than more technical and precise explanations. In part because you have to abandon the shortcuts that technical jargon make available to the writer. Technical jargon that imposes a burden on the reader.

What technology would you want to explain using cartoons?

Pixar Online Library

The five most recent titles:

• Vector Field Processing on Triangle Meshes
• Convolutional Wasserstein Distances: Efficient Optimal Transportation on Geometric Domains
• Approximate Reflectance Profiles for Efficient Subsurface Scattering
• Subspace Condensation: Full Space Adaptivity for Subspace Deformations
• A Data-Driven Light Scattering Model for Hair

Even with help from PIXAR, your app isn’t going to be compelling enough to make users forego breaks, etc.

But, on the other hand, you won’t know until you try. 😉

I was surprised that a list of Pixar films didn’t have an edgy one in the bunch.

The techniques valid for G-rated fare can be amped up for your app.

What graphics or sounds would you program for bank apps?

I first saw this in a tweet by Ozge Ozcakir.