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.

In collaboration between USGS, NASA and TIME, Google released a quarter century of satellite imagery to see how the world has changed over time.

The images were collected as part of an ongoing joint mission between the USGS and NASA called Landsat. Their satellites have been observing earth from space since the 1970s—with all of the images sent back to Earth and archived on USGS tape drives that look something like this example (courtesy of the USGS).

We started working with the USGS in 2009 to make this historic archive of earth imagery available online. Using Google Earth Engine technology, we sifted through 2,068,467 images—a total of 909 terabytes of data—to find the highest-quality pixels (e.g., those without clouds), for every year since 1984 and for every spot on Earth. We then compiled these into enormous planetary images, 1.78 terapixels each, one for each year.

Be sure to check out the Timelapse feature on Time.

From the video and pictures it's pretty clear that the photos look similar, but Huang took it a step further with a handful of computer vision techniques to quantify the likeness between faces. And again, the analysis shows similarity between the photos, so the gut reaction is that the contestants are nearly identical.

However, you have to assume that the pictures are accurate representations of the contestants, which doesn't seem to pan out at all. It's amazing what some makeup, hair, and photoshop can do.

You gotta consider your data source before you make assumptions about what that data represents.

Deadspin made a straightforward map that shows the highest paid public employee in each state.

Based on data drawn from media reports and state salary databases, the ranks of the highest-paid active public employees include 27 football coaches, 13 basketball coaches, one hockey coach, and 10 dorks who aren't even in charge of a team.

The quality of television shows follow all kinds of patterns. Some shows stink in the beginning and slowly gain steam, whereas others are great at first and then lost momentum towards eventual cancellation. Using data from the Global Episode Opinion Survey, Andrew Clark visualized ratings over time for many popular shows in an interactive.

The graph represents the average ranking for the show over time. The red lines indicate changepoints, estimations of when the properties of the time-series, typically the mean changes. The intensity of the plot varies according to the number of respondents. An episode of a show that is favourably rated tends to get more people ranking as do earlier episodes in long-running show.

For example, the chart above shows ratings for 24. The ratings started in the 8s and finished in the 7s, which isn't a huge difference really when you compare it to ratings for The Simpsons.

There's a self-selection challenge here. To participate in the GEOS survey, you have to create an account, so there's probably going to be some polarity in the ratings as well as limited sampling for many episodes. So take it all with some salt. Nevertheless, it's fun to poke around and see how your favorite shows changed over time. Most of the ratings matched my expectations.

The R code is available on github if you want to have a go at the data.

Watch Arrested Development enough and you start to realize there are a lot of recurring jokes in various episodes and seasons. In an interactive by Beutler Ink and Red Edge, Recurring Developments shows what episodes jokes, such as the awkwardness between George Michael and Maeby, happen. And like the visualization this is based on, you can also go the other way around and look at the recurring themes in each episode.

The interaction is fairly straightforward. Jokes are on the left and a listing of episodes is on the right. Click a joke and orange lines extend to corresponding episodes. Click an episode and lines extend to corresponding jokes.

Excuse me while I go on an Arrested Development binge on Netflix.

On Wikipedia, there are constant edits by people around the world. You can poke your head in on the live recent edits via the IRC feed from Wikimedia. Stephen LaPorte and Mahmoud Hashemi are scraping the anonymous edits, which include IP addresses (which can be easily mapped to location), and naturally, you can see them pop up on a map.

In a follow-up to their map of racist tweets towards Barack Obama, the folks at Floating Sheep took a more rigorous route to get around the challenges of sentiment analysis. Over 150,000 geotagged tweets against races, sexuality, and disabled were manually classified and mapped.

All together, the students determined over 150,000 geotagged tweets with a hateful slur to be negative. Hateful tweets were aggregated to the county level and then normalized by the total number of tweets in each county. This then shows a comparison of places with disproportionately high amounts of a particular hate word relative to all tweeting activity. For example, Orange County, California has the highest absolute number of tweets mentioning many of the slurs, but because of its significant overall Twitter activity, such hateful tweets are less prominent and therefore do not appear as prominently on our map. So when viewing the map at a broad scale, it’s best not to be covered with the blue smog of hate, as even the lower end of the scale includes the presence of hateful tweeting activity.

Hard to believe this stuff is still around. It looks like I might want to stay clear of some parts of Virginia. (The aggregation at the national level seems a bit aggressive. When you zoom in on the map, the polarity between the east and west doesn't seem so strong.)

Update: Be sure to read the FAQ before making snap judgements.