Patricio Gonzalez Vivo, an MFA Design & Technology student, scraped depth from Google Streetview and then reconstructed it in openFrameworks. The result is Point Cloud City. See it in action in the video below.

Dreamlike.

Now I’m curious what else can be gleaned from this data, because this essentially means you could get really detailed data about the makeup of places, down to the window of a building. Although I don’t imagine Google will let this stay so accessible for long. [Thanks, @pixelbeat]

Jen Lowe tracks her heart rate with a Basis watch, and she’s showing the last 24 hours of that data in One Human Heartbeat.

Basis doesn’t provide an open API, so I access the data using a variation of this code. The heartrate you see is from 24 hours ago. This is because the data can only be accessed via usb connection. Twice a day I connect the watch and upload my latest heartrates to the database. I’ve been doing this for 33 days now.

It’s March 25, 2014, and statistics say I have about 16452 days left.

On the surface, it’s just a pulsating light on a screen, but somehow it feels like more than that. The countdown aspect makes me uneasy, as if I were watching a ticker on someone’s life, or my own even. I want to keep watching though, because it continues to pulsate. It’s hopeful.

Seth Kadish looked at the road network of several major counties and estimated the directions the streets run. The result is a set of charts that shows which cities use a grid system and those that don’t.

If you’re like me, and you use the Sun to navigate, you probably appreciate cities with gridded street plans that are oriented in the cardinal directions. If you know that your destination is due west, even if you hit a dead end or two, you’ll be able to get there. However, not all urban planners settled on such a simple layout for road networks. For some developers, topography or water may have gotten in the way. Others may not have appreciated the efficiency of the grid. This visualization assesses those road networks by comparing the relative degree to which they are gridded.

Whoa, Charlotte.

Since the original, Kadish has added more counties and a handful of international cities.

Kevin Wu made a straightforward interactive that lets you see IMDB television ratings over time, per episode and by season.
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Stamen visualized Bitcoin activity, noting a variety of traders who knew what they were doing, didn’t know what they were doing, and were apparently automated.

In February 2014 MtGox, one of the oldest Bitcoin exchanges, filed for bankruptcy protection. On March 9th a group posted a data leak, which included the trading history of all MtGox users from April 2011 to November 2013. The graphs below explore the trade behaviors of the 500 highest volume MtGox users from the leaked data set. These are the Bitcoin barons, wealthy speculators, dueling algorithms, greater fools, and many more who took bitcoin to the moon.

With player tracking installed in all of the NBA arenas, the sports analytics folks can essentially replay entire games through data and dissect the many facets of play. Andrew Bergmann looked at the passing averages between starters on each team.

The thickness of the gray lines on the accompanying chart represents the average number of passes per game between two players.

A very clear picture emerges on which teams distribute the ball more evenly between players, such as the Nets, Bulls and Cavaliers. On the flip side, Chris Paul and Blake Griffin dominate passing for the Clippers, and likewise for Kevin Love and Ricky Rubio of the Timberwolves.

These connections are non-directional, so it hides a little bit, but you still get a good sense of who the offense runs through based on the sum-width of connections from an individual. You can also easily see team ball distribution, which is the point of the graphic.

Next step: match ups. I bet that’s where the money’s at. We’ve seen a lot of analyses and graphics that show the activity of a single team, but ultimately, you want to know how your team plays against others in your division and playoff contenders. Ideal gameplay against subpar teams? Not so important.

Pantheon, a project from the Macro Connections group at The MIT Media Lab, explores cultural influences across countries and domains.

To make our efforts tractable, Pantheon will not focus on culture, as it is understood in its broadest sense, but on cultural production. In a broad sense, culture can be understood as all of the information that humans—or animals [1]—generate and transmit through non-genetic means [2]. At Pantheon, however, we do not focus on the entire range of cultural information, but in a subset of this information that we define narrowly as cultural production. That is, we do not focus on cultural information such as passed on family values or societal trust [3], but on cultural production as proxied by the biographies of notable historical characters. Moreover, we focus on the subset of cultural production that we can identify as global culture, meaning the subset of cultural production that has broken the barriers of space, time and language.

Rankings inevitably come into play, such as who the most influential philosopher, physicist, or country is, and the project covers a broad spectrum, so the methodology is the most important here. Using data from Wikipedia, Freebase, and other online sources, the researchers created several indices that essentially give a score to individuals for popularity and production. This naturally results in estimation fuzziness, which means you take the results with salt and all that.

It’s an interesting look though and a good start to something bigger. If anything, you’ll probably learn something new after poking around for a bit.

Looking for a job in data science, visualization, or statistics? There are openings on the board.

Assistant/Associate Professor-Statistics-Mathematical Sciences for the University of Massachusetts Lowell in Lowell, Massachusetts.

Data Visualization Engineer / Reporting Manager for Nike in Portland, Oregon.

Lead data Visualization Design-Developer (freelance) for SingTel in Singapore.

After he noticed gambling odds fluctuate wildly at the end of a football game, Todd Schneider realized a correlation between betting odds and game excitement. The Gambletron 2000 is a fun look into the proxy.

It occurred to me then that variance in gambling market odds is a good way to quantify how exciting a game is. Modern betting exchanges allow gamblers to bet throughout the course of a game. The odds, which can also be expressed as win probabilities, continually readjust as the game progresses. My claim is that the more the odds fluctuate during a game, the more exciting that game is.

Games and odds update automatically up to the minute, with a highlight on the “hotness” of games, or the amount of variation over time. A blowout game shows a line that heads towards 100 percent probability that a team will win, whereas a comeback game shows a dip towards 100 percent for one team and then a trend back towards 100 percent for the opposition.

I had the odds for the Golden State-Portland game open for part of the time tonight, and it was kind of a fun accompaniment.

Mobile alert app for sports, anyone? Current offerings are abysmal.

The Star Tribune has a fun interactive that recommends Minnesota brews, based on five key beer characteristics. Use sliders to enter your preference of bitterness, aroma, etc and the results come in radar graph form.

Whether you’re a creature of habit or always up for something new, this tool will help you get to know what’s brewing in Minnesota. We’ve catalogued more than 100 beers from 36 Minnesota breweries and sorted them by five characteristics.

I fully expect someone to expand this to the rest of the world.

The Atlantic interviewed Dr. Demetrios Matsakis, Chief Scientist for Time Services at the US Naval Observatory about where time comes from, the precision required and how they obtain it, and why we need such precision. Five seconds into it, my wife commented, “That sounds nerdy.” That’s how you know it’s gonna be good.

Tony Haile discusses how we read and share online, based on actual data. It’s not as click- and pageview-based as you might think.

A widespread assumption is that the more content is liked or shared, the more engaging it must be, the more willing people are to devote their attention to it. However, the data doesn’t back that up. We looked at 10,000 socially-shared articles and found that there is no relationship whatsoever between the amount a piece of content is shared and the amount of attention an average reader will give that content.

When we combined attention and traffic to find the story that had the largest volume of total engaged time, we found that it had fewer than 100 likes and fewer than 50 tweets. Conversely, the story with the largest number of tweets got about 20% of the total engaged time that the most engaging story received.

There’s plenty of software to muck around with data, but to gain the skills to really get something out of it, that takes time and experience. Mikio Braun, a post doc in machine learning, explains.

For a number of reasons, I don’t think that you cannot “toolify” data analysis that easily. I wished it would be, but from my hard-won experience with my own work and teaching people this stuff, I’d say it takes a lot of experience to be done properly and you need to know what you’re doing. Otherwise you will do stuff which breaks horribly once put into action on real data.

And I don’t write this because I don’t like the projects which exists, but because I think it is important to understand that you can’t just give a few coders new tools and they will produce something which works. And depending on how you want to use data analysis in your company, this might break or make your company.

Braun breaks it down into four bullet points worth a read, but the tl;dr version is that analysis isn’t simple, and no tool is going to do everything for you. It’s simple with simple data, but you can almost always go deeper with more data, and it takes experience to ask the right questions. So try not to be too content with that software output.

John McDuling for Quartz writes about the FiveThirtyEight replacement.

David Leonhardt, the Times’ former Washington bureau chief, who is in charge of The Upshot, told Quartz that the new venture will have a dedicated staff of 15, including three full-time graphic journalists, and is on track for a launch this spring. “The idea behind the name is, we are trying to help readers get to the essence of issues and understand them in a contextual and conversational way,” Leonhardt says. “Obviously, we will be using data a lot to do that, not because data is some secret code, but because it’s a particularly effective way, when used in moderate doses, of explaining reality to people.”

With the new FiveThirtyEight coming soon, The Upshot, and plenty of smaller bits sprouting up in other areas, this data-driven news thing might be more than a fad. Hey, statisticians, you want to get in on this? Seriously, there’s plenty of data to go around.

How much caffeine can you consume during the day and still fall asleep at night? For some, it’s one cup and they’re up all night, whereas others don’t feel a thing. UP Coffee, an app from Jawbone Labs, helps you understand your own consumption and caffeine tolerance.

Data entry is straightforward since it’s only for caffeine-related beverages, such as coffee and soda. Enter your beverage, and the app tabulates caffeine amounts for you.

The key though is that it doesn’t just stop at milligrams. What’s 100 milligrams of caffeine mean anyways? Instead, with a focus on sleep, it tells you how much caffeine you’ve consumed and how many hours you’re expected to feel the effects.

Pair it with your Jawbone UP band and account for an even wider out picture. Although you don’t have to. I’ve been using the app with neither, and it’s still fun the play with. And it kind of makes me want a band.

Forget bell curves, jellybeans, and coin flips to explain statistical concepts. Dancing Statistics is a video series that demonstrates variance, correlation, and sampling through coreographed movements. The dance below explains variance.

Watch the full playlist here. [via infosthetics]

Justin Blinder used New York’s city planning dataset and Google Streetview for a before and after view of vacant lots.

Vacated mines and combines different datasets on vacant lots to present a sort of physical facade of gentrification, one that immediately prompts questions by virtue of its incompleteness: “Vacated by whom? Why? How long had they been there? And who’s replacing them?” Are all these changes instances of gentrification, or just some? While we usually think of gentrification in terms of what is new or has been displaced, Vacated highlights the momentary absence of such buildings, either because they’ve been demolished or have not yet been built. All images depicted in the project are both temporal and ephemeral, since they draw upon image caches that will eventually be replaced.

Based on reviews from BeerAdvocate, Beer Viz, a visualization class project, asks you to choose a general style of beer and a beer that you like. Then it shows you beers that are similar, based on appearance, taste, aroma, and overall score. It’s like a visual version of the beer recommendation system we saw last year.