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]

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]

Diego Valle-Jones maps homicides and trafficking routes in Mexico.

To unclutter the map and following the lead of the paper Trafficking Networks and the Mexican Drug War by Melissa Dell, I decided to only show the optimal highways (according to my own data and Google Directions) to reach the US border ports from the municipalities with the highest drug plant eradication between 1994 and 2003 and the highest 2d density estimate of drug labs based on newspaper reports of seizures. The map is a work in progress and is still missing the cocaine routes, but hopefully I'll be able to add them shortly.

There's lots to look at and interact with here. To start, there are bubbles that cluster homicides by region and major highway routes in black.

Click on any bubble and you get a time series for the corresponding area, going back to 2004. Or if you like, draw your own polygon to see the time series for specific regions. Pointers on the time series highlight significant events. There's also a slider that lets you see numbers on the map for different years. A layer underneath the bubbles lets you see high density areas for marijuana, opium, and drug labs.

Take a look at the full map for yourself. This is nice work by Valle-Jones.

[Diego Valle-Jones | Thanks, Diego]

Eric Fischer has mastered the art of making use of geotagged things from social sites like Twitter and Flickr. In his most recent set, Fischer maps connectedness via geotagged tweet density (using Dijkstra's algorithm). I just got back from Berkeley a few hours ago, so the map of East Bay travels is of most interest to me.

The main implication, as far as I am concerned, being that because of its traditional focus on downtown commuters, BART does not do a very good job of serving the most promising corridor in Berkeley and North Oakland, which would run approximately under San Pablo, University, the UC Berkeley campus, Telegraph, a jog over to College, Broadway, 40th/Linda, Grand, and some sort of route from the Grand-Lake district crossing Park Boulevard to near 14th and Foothill. Some of this, especially at the south end, would be difficult because of topography, but it could probably be approximated. Needless to say, if this were to be constructed, it would have to be pretty much entirely in subway to avoid tearing down the neighborhoods it would intend to serve.

There are also maps for New York and Chicago.

In 2010, NASA released a map that shows world forest heights. Robert Simmon, using data from The Woods Hole Research Center, has produced an even higher resolution map, down to the management scale:

In the end, the research team was able to construct a map with higher resolution and more precise detail than any large-scale map of forest biomass ever made. The map reveals the checkerboard patterns of logging in the old growth of the Pacific Northwest and the highly managed tree farms of the Southeast. In the Midwest, trees outline the rivers and the edges between farms, while forests re-emerge on land that was once cleared for crops. In the Mid-Atlantic and New England, lands that were stripped bare in the early years of the nation are now tree-covered again—though with many urban developments amidst the forest.

[NASA | Thanks, Michael]

Accompanying an article on the variations of the wealthiest one percent, The New York Times provides this interactive map to see what percent you're in. Simply enter your household income and see how you compare in metropolitan areas with over 50,000 households.

Nation-wide, a household income over $383,000 puts you in the top one percent. However, a lower household income of $179,000 puts you in the top one percent in Flint, Michigan. The same wage in San Diego, California puts you only in the top eight percent.

Also: what jobs the top one percent have.

Update: There was also a fine print version.

[New York Times]

Urban planner Neil Freeman maps the world's subway systems to scale in a minimal style resembling the scribbles or renderings of weird sea creatures by a two-year-old. I wish there were nodes to show stops, too, but the contrasts between the compact TRTA in Tokyo and RATP in Paris, and the spread out Seoul Metro and Transport for London is an interesting look.

[Neil Freeman via @kennethfield]

Google Maps is one of Google's best applications, but the time, energy, and thought put into designing it often goes unnoticed because of how easy it is to use, for a variety of purposes. Willem Van Lancker, a user experience and visual designer for Google Maps, describes the process of building a map application — color scheme, icons, typography, and "Googley-ness" — that practically everyone can use, worldwide.

We have worked (and driven) around the world to create a "map" that is a collection of zoom levels, imagery, angles, and on-the-ground panoramas all wrapped into one. Through these varied snapshots of our world, we are attempting to sew together a more seamless picture of the Earth—from its natural beauty to the surprising (and often absurd) details that make it our unique home. As our work progresses, new technologies give us the opportunity to get away from the limitations and complexity of standard cartography to provide a much more approachable and easy-to-understand map, loaded with data and information.

Remember when we had to refresh the page to see more of map?

[Core77 via @awoodruff]

Reddit user Laurel Quade mapifies the wonderful world of Reddit. Each country represents an area of interest, and "cities" are sized by inhabitants. I'm not familiar enough with the communities to know how accurate it is, but judging by the comments, I'd say pretty good.

[Redditland via @adamsinger]