Esquire invited a handful of map-makers to represent the United States outside its borders.

Red state, blue state, big state, small state, north and south and east and west: How we define our similarities and differences with each other often comes down to where we see ourselves on the map of America. But what if we threw out the standard-issue version and started over with something new?

Esquire’s online format doesn’t do the maps justice though. A lot of the images are too small to see the details. In particular, don’t miss Eric Fischer’s high-resolution cartogram and Stamen’s interactive on where the money went. Fischer’s map shows density of geotags, but zoom in, and you can see major cities in detail. Stamen’s map uses IRS data in a twist to the migration map, showing county-level monetary gains and losses.

Nate Silver looks at past players who have scored 20 or more points, had 6 or more assists, and shot better than 50 percent in four or more games in a row. It’s an illustrious list of all-stars, including Jordan, Bird, and Magic, with only a handful who were just so-so.

Like everyone else, I was skeptical. I saw him play with the Warriors, and it was never that impressive. However, watching last night’s game against the Lakers it was hard not to buy in to Linsanity. We’ll see if he can extend the streak tonight against Minnesota, but even if the Knicks do win, should we read that much into it? Remember, there aren’t that many other scoring options on the Knicks right now, two of the past four wins were against horrible teams (New Jersey and Washington) and the other two, the Lakers and the Jazz, were teams just slightly above .500.

[Nate Silver]

In a tongue-in-cheek tribute to the Dancing Plague of 1518, Niege Borges illustrates dances from a number of shows and movies in his project of the same name. All of them available in print, including the Elaine dance from Seinfeld, Little Miss Sunshine, and Singin’ in the Rain. [Niege Borges]

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]