The MIT SENSEable City Lab, in partnership with BBVA, visualizes spending in Spain during Easter of 2011. The animation shows the activity of 1.4 million people and 374,220 businesses, over 4 million transactions.

The map is less interesting to me since I’m a non-Spaniard (population density?), but the categorizations and spending volume over time is fun to see. Groceries are shown in blue, gas stations in yellow, fashion in pink, and red in bars and restaurants. During the day, you see people filling up the tank, and then as evening comes, the city centers and coast lights up red.

[via @pkedrosky]

About three years ago, I shared 37 data-ish blogs you should know about, but a lot has changed since then. Some blogs are no longer in commission, and lots of new blogs have sprung up (and died).

Today, I went through my feed reader again, and here’s what came up. Coincidentally, 37 blogs came up again. (Update: added two I forgot, so 39 now.) I’m subscribed to a lot more than this since I don’t unsubscribe to dried up feeds. But this list is restricted to blogs that have updated in the past two months and are at least four months old.
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The Eatery app by Massive Health lets people snap pictures of their food and rate the healthiness. The premise is that you don’t have to carefully count calories to lose weight. You just need to be more aware of what you eat. Using 7.68 million ratings over a five-month span, Massive Health maps eating healthiness over an aggregated 24-hour time window.

Mouse back and forth over the map slowly to see the changes. It’s interesting that as night falls, desserts and midnight snacks make themselves known and then the green comes back in the morning.

[Thanks, Thomas]

It feels oh so wrong posting about bad charts in a report about happiness around the world, but here you go. I do it for you. The first World Happiness Report was released by the United Nations earlier this month. It’s filled with gems like the 3-d bar chart above. Notice the axis that starts at 0.66. (You shouldn’t do that because length is the visual cue here, and it makes the differences look greater than they actually are.)
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I listen to a lot of podcasts. They make my workouts much more enjoyable. For the most part though, I only listen to ones about sports and more general podcasts about design, technology, and working from home. However, a couple of months ago, Enrico Bertini and Moritz Stefaner started Data Stories, a podcast on visualization. Enrico is a researcher in the area and Moritz is more of a practitioner, so it’s a good contrast between the two.

Neither had experience producing podcasts before this, so it was rough around the edges at first. But each episode has been getting better. I highly recommend it.

In the most recent episode, with Andy Kirk, they discuss the most common question from people new to the field: how to get started. Go ahead and listen. It’s a good one if you’re itching to get your feet wet.

One thing I’d add (that maybe I missed as cars drove past me) is that it’s important to establish what you want to learn visualization for. The purpose will change what methods to use and what software to learn. Monitoring server load for a web service is going to be different than say, designing an atlas.

Dominic Basulto parallels the urban metrosexual to those who collect personal data.

The same cultural zeitgeist that gave us the metrosexual – the urban male obsessive about grooming and personal appearance – is also creating its digital equivalent: the datasexual. The datasexual looks a lot like you and me, but what’s different is their preoccupation with personal data. They are relentlessly digital, they obsessively record everything about their personal lives, and they think that data is sexy. In fact, the bigger the data, the sexier it becomes. Their lives — from a data perspective, at least — are perfectly groomed.

The difference is that metrosexuals spend their time accentuating their best features and hiding their flaws, whereas personal data collectors spend their time at Quantified Self meetups telling others the weird and interesting things they found.

Robert Kosara contrasts my version of the pay gap graphic with the NYT original and notes how small changes make a big difference in how a graphic reads.

But what Nathan’s version is missing is the story. The additional data mostly adds confusion: move your mouse over the year in the lower right, and what do you see? Lots of points are moving around, but there doesn’t appear to be a clear trend. The additional categories are interesting, but what do they add?

Not much. When I was putting together the graphic, I was hoping for a clear trend — something so obvious that didn’t have to be explained. Instead I got fuzzy results. And that’s where I stopped. On the other hand, the NYT version explains those fuzzy results, namely the outliers, such as women CEOs who work for non-profits or the greater percentage of men in medical specialties like surgery.

In analysis, assuming the users are experts of their data, annotation is less important. It’s about allowing them to stay nimble and ask/answer a lot of questions. Graphics that tell stories with data, however, already have something interesting to say.

A couple weeks ago, I looked at gender pay gap data to see how the differences have changed over the past nine years. This was after seeing Narrow the Gapp by Gina Trapani and then a Time Magazine cover story on how more women are becoming the main earners of households. A little after that, Mike Bostock posted his D3 port of GapMinder’s well-known Wealth & Health of Nations, and the New York Times interactive by Hannah Fairfield and Graham Roberts from 2010 came to mind. My idea was to combine the two as a recreation of the latter, with a couple of my own interactions. I went to work on a bunch of horrible government PDFs and then pulled it all together.
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