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]

Data science has been covered at length during the past couple of years, and we tend to think of it as a field of study just a couple of years older than that. Jeff Hammerbacher and DJ Patil have played roles in further propagating the term as an actual profession in roughly the same timespan. So I was surprised to come across this rarely-cited 2001 paper by statistician William Cleveland, Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics [pdf].

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.
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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]