Using data from the London Fire Brigade, James Cheshire mapped 144,000 incidents in London.

This map shows the geography of fire engine callouts across London between January and September 2011. Each of the 144,000 or so lines represents a fire engine (pump) attending an incident (rounded to the nearest 100m) and they have been coloured according to the broad type of incident attended. These incident types have been further broken down in the bar chart on the bottom right. False alarms (in blue), for example, can be malicious (fortunately these are fairly rare), genuine or triggered by an automatic fire alarm (AFA). As the map shows, false alarms – thanks I guess to AFAs in office buildings – seem most common in central London.

It looks a lot like a sky of fireworks in this view. I bet a map for each category might help flesh out different patterns.

Microsoft researcher Kate Crawford describes several myths of big data. Myth #4: It makes cities smarter.

“It’s only as good as the people using it,” Ms. Crawford said. Many of the sensors that track people as they manage their urban lives come from high-end smartphones, or cars with the latest GPS systems. “Devices are becoming the proxies for public needs,” she said, “but there won’t be a moment where everyone has access to the same technology.” In addition, moving cities toward digital initiatives like predictive policing, or creating systems where people are seen, whether they like it or not, can promote lots of tension between individuals and their governments.

Yep. I hear those people things can introduce a lot of challenges.

It seems like ages since we ran one of these.

It’s hard to believe Data Points hit the shelves two months ago. (Thank you to everyone who got a copy!) It still feels brand new in my head. I kind of thought that time would slow down after I finished the book (and dissertation), but it seems to be moving even faster now.

Anyways, if you’d like a chance to win a copy of Data Points blemished by my signature, leave a comment below by Wednesday, June 5, 2013 11:59pm PST. Tell us what your favorite number is and why. One entry per person please. I’ll pick a winner at random via sample() in R. Good luck.

And of course, if you can’t wait, have never been lucky at cards, or want a blemish-free version of the book, you can get it at online and physical bookstores everywhere.

The central limit theorem:

In probability theory, the central limit theorem (CLT) states that, given certain conditions, the mean of a sufficiently large number of independent random variables, each with a well-defined mean and well-defined variance, will be approximately normally distributed.

Victor Powell animated said random variables falling into a normal distribution (which should look familiar to those who have seen that ping pong ball exhibit in exploratoriums and science museums). Play around with the number of bins and delay time and watch it go.

Twitter mapped all the geotagged tweets since 2009. There’s billions of them, so as you might expect, roads, city centers, and pathways emerge. And it only took 20 lines of R code to make the maps.

In celebration of Arrested Development’s return via Netflix, NPR combed through the jokes — obvious and obscure — and set them in a handy interactive guide.

Arrested Development is back! Because we’re obsessed we care about your watching enjoyment, we wrote down all the recurring gags in every episode — including the new season 4 episodes — with special attention to jokes hidden in the background (like Cloudmir vodka) or being foreshadowed (like when Buster lost his hand).

The three categories of joke are color-coded, where each row represents a joke and a tick represents an occurrence of that joke over four seasons.

I’ve only watched a handful of episodes, but I’m tempted to turn on Netflix with this guide in front of me. [Thanks, @onyxfish]

In distributed denial-of-service attack a bunch of machines make a bunch of requests to a server to make it buckle under the pressure. There was recently an attack on VideoLAN’s download infrastructure. Here’s what it looked like.

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Profound.

The Centers for Medicare and Medicaid Services released billing data for more than 3,000 U.S. hospitals, showing high variance in cost of health scare across the country and even between nearby hospitals.

As part of the Obama administration’s work to make our health care system more affordable and accountable, data are being released that show significant variation across the country and within communities in what hospitals charge for common inpatient services.

The data provided here include hospital-specific charges for the more than 3,000 U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for the top 100 most frequently billed discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2011. These DRGs represent almost 7 million discharges or 60 percent of total Medicare IPPS discharges.

The data is downloadable as CSV or Excel files and is surprisingly usable and worth a look.

The New York Times has a useful per-hospital browser and The Washington Post provides quick comparisons by state.

NYU ITP graduate student Federico Zannier collected data about himself — online browsing, location, and keystrokes — for his thesis. As he dug into personal data more and looked closer at company privacy policies, he wondered what it might be like if individuals profited from their own data. That is, companies make money using the data we passively generate while we browse and use applications and visit sites. What if individuals owned that data and were able to sell it?

Enter Zannier’s Kickstarter campaign to sell his own data for $2 per day of activity.

I started looking at the terms of service for the websites I often use. In their privacy policies, I have found sentences like this: “You grant a worldwide, non-exclusive, royalty-free license to use, copy, reproduce, process, adapt, modify, publish, transmit, display and distribute such content in any and all media or distribution methods (now known or later developed).” I’ve basically agreed to give away a lifelong, international, sub-licensable right to use my personal data.

Somebody told me that we live in the data age, that the silicon age is already over. “In this new economy,” they said, “data is the oil.”

Well, this is me trying to do something about it.

Clearly this is more of a statement and conversation starter, but what if?

There’s about a week left in the campaign, and it’s well past the goal.

PBS Off Book’s recent episode is on “the art of data visualization.” It feels like a TED talk — kind of fluffy and warm — with several names and visualization examples that you’ll recognize. No clue who the first guy is though.

We’ve seen plenty of augmented reality where you put on some digitally-enabled glasses or point your camera phone on something and visuals are overlaid on reality. The augmentation is typically a layer on top.

Eidos is a student project that tries taking this in a different direction. One piece applies an effect similar to long-exposure photography, and the other sends audio to your inner ear to focus on a subject and drown out ambient noise. See the devices in action in the video below.

[via FastCo]

About 35,000 meteorites have been recorded since 2500 BC, and a little over 1,000 of them were seen while they fell, based on data from the Nomenclature Committee of the Meteoritical Society. Carlo Zapponi, a data visualization designer, visualized the latter in Bolides.

We saw a mapped version of this data a while back, but Bolides takes a time-based approach. A bar chart shows the number and volume of meteorites that have been seen over time, and on the initial load, you get to watch the meteorites fall, one bright orange fireball at a time.