Shot

Photomicrograph from 2008 of Axon Scaffolding Proteins, by Michael Hendricks and Suresh Jesuthasan

Earthquake Visualization

On the 22nd February 2011 just before 1pm a 6.3 magnitude earthquake blasted Christchurch city in New Zealand. We felt it in Wellington about 10 seconds later, on a different island. The faultline had only been discovered as little as six months earlier when a stronger quake struck, but this one was during the day time, was shallower, and hit at the heart of the city during lunch hour:

The effect was devastating, with death and destruction obliterating the central business district. It is New Zealand's worst natural disaster in our short history.

When the magnitude 7.1 quake struck on September 4th 2010 and continued to linger with aftershocks for months Paul Nicholls at the University of Canterbury created a mashup using Geonet's public quake drum data and overlaid it onto Google Maps:

The resulting visualization is a timelapse display of location, strength and depth of every subsequent quake.

Taking this a step further Chris Crowe uses the same data to calculate the energy released from the earthquakes:

 What both visualizations make clear is that the quakes, while distinct, are part of an ongoing sequence of seismic activity. What is striking about these events are the forces experienced in what was previously thought to be a stable part of the country. The following graph maps the magnitude of each quake to the depth, what is revealing is the shallowness of each quake:

For those of us that live in Wellington an earthquake is nothing new, but when a 4.3 quake struck late in the evening last weekend many of us paused to wonder if Christchurch was hit; thankfully a quick check on Twitter revealed everything was fine down there. The quake was rocky and sharp, here is how it appeared on Geonets quake drum:

To put this into perspective here is an image of the Christchurch drum for the same period:

Our thoughts are with everyone in Christchurch as they cope with rebuilding their city and lives in such a fragile and unpredictable environment.

Data Explorer: Influenza

Google have opened access to their Data Explorer tool, which allows you to build graphs of public data.

Data Visualization of Google Flu searches

This graph demonstrates the incidence of flu related search terms used on Google in the United States and compares them with New Zealand over time. Taking into account the seasonal nature of flu queries (people tend to be more interested in winter) you can see the initial spike in America from the 2003 influenza A subtype H5N1 ("Bird Flu") outbreak, then again in 2009 with the influenza H1N1 ("Swine Flu") pandemic.

Graffiti Markup Language

In case you haven't seen this yet; GML. Graffiti Markup Language by Evan Roth. First check it out:

Unfortunately no info on Wikipedia as yet. Graffiti Markup Language is an XML based way to record and store tags by X and Y axis, with time creating the Z axis. This open format is compatible with any other GML interpreter, meaning you can explode your tag into a giant laser or store in a database filled with legendary graf artists. A new door is opening on the underground art world.

Want to check it out for yourself? You can with DustTag, an iPhone app "designed for graffiti writers that visualizes the motion involved in the creation of a tag. Motion data is recorded, analyzed and archived in a free and open database, 000000book.com, where writers can share 3-D animated representations of their hand styles." Dig deeper at http://graffitianalysis.com/

Immediate Information

The New York Times has some fantastic visualizations, including this infographic "Is it better to Buy or Rent?"

This interactive calculator has a clear visual representation of the negative to positive return on investment movement, with the title summarizing the finding 'buying is better than renting after 6 years' within the values of the selected parameters.

More recently NYT have used a treemap to explore the data within the proposed United States 2012 Budget:

An increasingly popular visualization tool for displaying big data volume and movement within compact space, in this instance you are looking at $3.7 trillion USD.

update 18 Feb: There is a lot of opportunity to add value to this type of data set, for example New Zealand's Southgate labs have a tax calculator showing where your hard earned cash ends up. When government can provide real time data there is a lot of scope for new visualizations.

The Social Graph versus the Interest Graph

Digital Surgeons have just released an infographic comparing Facebook with Twitter:

click to enlarge

In one sense you see a lot of similarities, the key differences appear to be that Twitter users update status more frequently, use mobile devices to access the site more, and are more loyal to their brand, or 'interest'. In all other senses the demographics are roughly the same.

Facebook has the market share with the Social Graph, which is a term to describe the networking of relationships between people. With 500 million users connecting to each other Facebook dominates in this space. Facebook knows who you know, and who they know, and so on. If your wondering, it looks a little like this:

Twitter on the other hand has about a fifth the number of users, but the users it does have are more active, and are constantly talking about about their interests. This is where the Twitter model starts to diverge from Facebook. People use Twitter to talk about anything and everything, with the crucial difference that all the talk is in the public Twitter domain. Facebook status updates are restricted to your friends, or your friends of friends.

You can use Twitter to see what is going on with any particular subject. People can search for the phrase or topic they are interested in, see what is going on, and participate in the conversation. This is the Interest Graph, a system where you can participate in whatever your interest is.

The Interest Graph provides Twitter and marketing companies with a powerful tool to understand hot topics, brand impacts on target demographics and trending over time of waxing and waning interest. This is in its infancy but expect a huge growth curve ahead, possibly a valuation for Twitter exceeding Facebook.

Unless they are beaten to the punch by Groupon, whose site lets you bid for a coupon; when enough people purchase a deal is struck with the store the coupon is for, everyone wins. This is a real time Interest Graph that is profitable, in fact highly profitable: Groupon are on track to be the fastest growing company ever, including Google. This is why Groupon will outpace Twitter in this space.

The Interest Graph is a view to the future, the data it contains is the essence of societies interactions, a valuable prize for the company that wins it.

Potraits of the Mind

The New York Times has a fantastic article on Carl Schoonover. Midway through a Ph.D. program in neuroscience at Columbia University, he shares his passion in “Portraits of the Mind: Visualizing the Brain from Antiquity to the 21st Century”. The picture "SpinyNeuron" from 2009 above was taken using Electron Microscopy, to reveal a world previously unseen by light based microscopes.

An electron bean scans the surface, each single electron bounces away, a dectector captures and maps the otherwise invisible contours of the central soma and its branch like dendrites.

A single neuron (in red), with all it's connecting neurons highlighted (yellow). This illuminating shot was created by using a modified rabies virus engineered to contrast with the surrounding brain structure.