Growing up, most of my friends had cable television, but whenever I brought it up, my parents would always say that I watched enough TV already (which was true). So it was always a treat when we went somewhere like a hotel, where there were more than five channels. I didn't know what any of the shows were, but it sure was fun pressing buttons on the remote. Today, I still don't know what most of the shows are, but the novelty is gone.

Nowadays, I have different choices (and priorities). I can entertain myself online, and services like Netflix and Hulu make that easier. When I do turn on the TV, it's often just for background noise as I cook dinner or do something on the computer.

I almost never watch shows when they actually come on, and I only know the schedules of a few of them. And nowadays, the gift of choice feels more like a waste, as I flip through sixty something channels and see nothing that I want to watch.

The other day I thought to myself, "I'm paying forty bucks per month to watch Groundhog Day. Again." But then I looked at the cable bill that I had not looked at in a year, since it's on auto-pay. I'm paying $64.99 for digital cable from Comcast, plus $15.95 for HD and DVR, and then there's about $5 in taxes and fees. The introductory price ran out long ago.

I could buy an obscene number of tacos from Jack in the Box with that cash.

So I looked into cutting the cord completely. I want to save money, but more importantly, I want to get more of what I want for my money. Toss the channels and shows I don't watch.

At $85.91 per month for the most basic HD plan from Comcast, that comes in at just over a grand per year. With Netflix and Hulu, it's $15.98 per month, or just under $200 per year. That's a big gap between Comcast and Hulu+Netflix. $839.16, to be exact, which is quite a buffer.

Of course you need a device if you don't already have one to play Netflix and Hulu on your television. A Roku costs between $50 and $100, and an Apple TV is about $100. Current difference: $739.16.

Also, you don't get all you want with just Hulu and Netflix. Personally, I watch basketball when good games are on. The NBA League Pass lets you watch (more) games over broadband though, on a Roku or Apple TV. That's $109 for the season. But, and it's a big one, in-market and nationally broadcast games aren't available via the League Pass. More on this to follow. Current difference: $630.16.

Then to get local channels, you can still use an antenna. The bestselling antenna on Amazon is $35.99. Current difference: $594.17.

Finally, that leaves a healthy amount to buy and rent shows and movies not available on Hulu or Netflix, which you can get on iTunes and Amazon Instant. For example, the pass for this season's How I Met Your Mother is $26.47. You could buy (and own) 22 full seasons of your favorite shows with the available buffer. I'm pretty patient though and don't mind waiting for stuff to become available on Netflix. I just need to be able to watch sports live. My wife has been really into Downton Abbey, and the season pass is $17.04. It's free on PBS, but she usually can't watch it when it airs. Current difference: $577.13.

After all the additional stuff, that's $577.13. Over 1,000 tacos.

But back to the basketball problem. Since League Pass doesn't get me nationally broadcast games, that means I wouldn't get most of the playoffs on ESPN and TNT. (I suspect the same for hockey, baseball, and football.) That's the most important part of the season, save the finals, which are broadcast on ABC. And my wife really likes HGTV and a handful of reality shows that aren't available on Roku, Apple TV, iTunes, or Amazon. Crud.

By the numbers and tacos, it makes sense to cut the cord. From a perspective of want though, it's harder to let go. It comes down to this: Is a year of a tiny subset of programming on cable and playoff games not available on ABC worth $577.13?

While we're on the topic of things moving on a map of changing camera angles, class project Taxi, by Tom McKeogh, Eliza Montgomery and Juan Saldarriaga, shows the movements of said vehicles in Manhattan, over 24 hours.

Geographic location data for the origin and destination of each ride is combined with waypoint data collected from the Google Maps API in order to generate a geographically accurate representation of the trip. We used data from taxi rides originating or ending in the neighborhoods of Lincoln center or Bryant Park. The visualization recreates a 'breathing' map of Manhattan based on the migration of vehicles across the city over a period of 24 hours, displaying periods of intensity, density and decreased activity.

I hope they do another iteration of this project. I bet they could do a lot more on the temporal side of things.

[Digital Urban via @kennethfield]

Even Westvang uses tax return data to visualize migration patterns of 300,000 Norwegians.

When running at full speed the visualization is clearly lacking in terms of salient features, yet I find it interesting. Then again, I like looking at Pachinko machines and waterfalls — processes comfortably stuck between the random and the ordered. When slowing the animation down and filtering for certain demographies it becomes more useful. At its best laymen, like myself, can visually perceive facets of the natioal Norwegian migratory process that before were only available through the statistical calculations of researchers in demography.

As you might expect, each particle represents a person moving from one ZIP code to another. The more people moving from point A to point B, the faster the particles move.

The most interesting bit, that I wish Westvan did more of, is closer to the end, when he shows a couple of demographic breakdowns. The older demographic tends to move shorter distances, and those with higher salaries shoot out from bigger cities. Hey Jon Bruner, something to keep in mind for your next iteration. Although I'm pretty sure the US doesn't make income data for every citizen publicly available like Norway does. What's that about?

[Even Westvan via @mariuswatz]

Web-based Analysis and Visualization Environment, or Weave for short, is open source software intended for flexible visualization.

Weave (BETA 1.0) is a new web-based visualization platform designed to enable visualization of any available data by anyone for any purpose. Weave is an application development platform supporting multiple levels of user proficiency — novice to advanced — as well as the ability to integrate, disseminate and visualize data at "nested" levels of geography.

It looks like everything is done through a click interface, and you can piece together modules and link them, etc. There is some setup involved, but there are a number of video tutorials and documents to get everything installed.

Source code also available on GitHub.

[Weave]

In a blend of data and storytelling, Jeremy Mendes and Leanne Allison dig into surveillance logs generated by a monitored grizzly bear between 2001 and 2009. The final work is a moving interactive documentary, Bear 71.

She lived her life under near-constant surveillance and was continually stressed by interactions with the human world. She was tracked and logged as data, reflecting the way we have come to see the world around us through Tron and Matrix-like filters, qualifying and quantifying everything, rather than experiencing and interacting.

Leanne Allison sifted through thousands of photos from motion-triggered trail cameras for this project. The grainy images gathered over the past 10 years by various scientists reveal the hidden life of the forest, played out by the animals and humans — including Bear 71 — captured covertly on film.

It begins with the capture of a grizzly, its tagging, and then release, as a first-person narrative tells a story through the eyes of the bear. You, the observer, are allowed to follow the bear and explore its environment on an abstract map, and somewhere along the way digital and the physical world melt together.

[Bear 71 via @wiederkehr]

Like something from of a video game, this graphic from The New York Times shows the most mentioned NFL players and coaches this season. Players are scaled approximately by the number of mentions between August 1, 2011 to February 1, 2012 on ESPN's SportCenter and Sunday NFL Countdown. The giant on the left is Tim Tebow, with 1,450 mentions. Bar graphs on the bottom highlight mentions over time for players of interest.

[New York Times]

This document describes a plan to enlarge the major areas of technical work of the field of statistics. Because the plan is ambitious and implies substantial change, the altered field will be called "data science."

For those unfamiliar, Cleveland's work on graphical perception might ring a bell.

The first time I heard "data science" was in 2007 while reading a proposal that my adviser had passed along, outlining an academic program similar to what we think of as data science. Now that I think of it, the proposal probably had a lot of similarities to the program outlined by Cleveland (which I would have signed up for in a heartbeat).

Cleveland outlines six areas and the percentage of focus for each.

• Multidisciplinary Investigation (25%) — collaboration with subject areas
• Models and Methods for Data (20%) — more traditional applied statistics
• Computing with Data (15%) — hardware, software, and algorithms
• Pedagogy (15%) — how to teach the subject
• Tool Evaluation (5%) — keeping track of new tech
• Theory (20%) — the math behind the data

That sounds like what we associate data science with, but current practitioners focus more on tools and less on pedagogy and theory. Although that's not to say that data scientists today couldn't benefit for more traditional statistical knowledge under their belt. And the same goes for statisticians learning more about how to use the tools available (and how to make the tools themselves).

Cleveland's overall theme is a melting of various fields that obviously fit well together.

Computer scientists, waking up to the value of the information stored, processed, and transmitted by today's computing environments, have attempted to fill the void. One current of work is data mining. But the benefit to the data analyst has been limited, because the knowledge among computer scientists about how to think of and approach the analysis of data is limited, just as the knowledge of computing environments by statisticians is limited. A merger of the knowledge bases would produce a powerful force for innovation.

Sounds familiar. Of course, John Tukey seemed to have the right idea in the 1970s. In any case, it was refreshing to find this from a statistician over the canned stat scoff that data science is statistics.

[William Cleveland via @drewconway]

Presidential candidates have raised $186 million up to now, according to the Federal Election Commission. The New York Times lets you compare the amounts raised by each candidate, over time and space. Simply select a candidate on the left, and another on the right to see how they match up. Fundraising by candidates from previous elections, at the same time of year, are also included for context.

While not the focus of the interactive, the distributions for donation size at the bottom seem to be especially telling.

[New York Times via infosthetics]

Birds move. eBird shows us how.

Understanding patterns of bird occurrence at continental scales has long been one of eBird's fundamental challenges. Only now, with 42 million records and ever more thorough coverage nationwide, is this becoming possible. Ongoing research at the Cornell Lab is currently producing cutting-edge graphics that we are pleased to share here. Day-by-day predictions of species occurrence allows these models to shine a spotlight on the most awe-inspiring of natural spectacles: the ebb and flow of bird migration.

Cutting edge? No. They are thorough though, with maps (in the form of animated gifs) for a large number of species.

[eBird | Thanks, Ed]