This is my last week at OECD, where I have worked for over eight years. A lot has happened to me during that period, and I can’t even start to list everything. Shortly after I started here I began working on graphs and providing advice on best practices. Back then, I was just a guy who knew Excel well. But soon after I got introduced to Jacques Bertin and realized that there was a whole science in the production on good visuals. About that time, OECD like other similar large government institutions developed an interest in dynamic graphics, so I became more and more curious about data visualization. Eventually, I picked up processing, attended my first visWeek in 2008, and became a full-fledged data visualization practitioner.
In my last year of school, one professor (@speelunk) told us that we should strive to do what we love, rather than get the most prestigious or difficult or highest-paid occupations. I am very fortunate to have found my calling with data visualization, to be able to do what I love, to love what I do and to be good at it. So I am very grateful to OECD to have made that possible.
So, I couldn’t leave without visualizing my experience of OECD.
Visit the Ocean Health Index website for incredible visualizations an interactive map of ocean health!
The Ocean Health Index IS a valuable tool for the ongoing assessment of ocean health. By providing a means to advance comprehensive ocean policy and compare future progress, the Index can inform decisions about how to use or protect marine ecosystems.
The Index is a collaborative effort, made possible through contributions from more than 65 scientists/ocean experts and partnerships between organizations including the National Center for Ecological Analysis and Synthesis, Sea Around Us, Conservation International, National Geographic, and the New England Aquarium. (via News | Ocean Health Index)
How do you make education interesting and, more importantly, beautiful? When it comes to the work of NASA, attracting enthusiasts isn’t difficult with the usual visuals of bright stars and colorful planets on hand. Look no further than the recent awe over Mars rover Curiosity’s high-res pictures to see proof of humanity’s fascination with space.
But not all of NASA’s data is packaged into a neat little photos. In fact, some of the organization’s most important findings about space come back in the form of numbers, beamed in by one of the many satellites orbiting our planet. And this information is brought to life by the Scientific Visualization Studio (SVS) — a team of scientists and animators that turns numerical data into a dynamic graphic or video.
The SVS is not only an active and creative tool for NASA outreach — it has even gone viral. Earlier this year, the SVS team received information from a project team called Estimating the Circulation and Climate of the Ocean, or ECCO, which uses mathematical tools to better understand how the ocean’s circulation patterns change over time. The result was Perpetual Ocean, a detailed and moving video interpreting a year’s worth of the ocean’s current patterns in minutes.
“I think scientists have an amazing internal world — they think about these things and how they work,” says Dr. Horace Mitchell, director of SVS. “But, they don’t do the kind of visuals that can be found in a feature film. That’s why we’ve found a niche that works.”
Continue reading this article here
The famous geographer Immanuel Kant maintained that geography was the study of knowledge in a location, while history was the study of knowledge in time. Since a map is a stationary object that’s meant to represent a physical location, it’s tempting to think that it wouldn’t allow you to display changes over time the way an animation or a graph would. So, if you have to compare information in a given place and over a period of time at the same time, how can you do it? (continue reading at: Time and GIS: Ways of Representing Time on a Map)
Incredible resource for data visualization!
Location-enabled data sets are pouring into organizations. Geospatial visualization allows companies to see what’s really going on among the rows and columns.
Geospatial visualization marries the broad insights available through visualization with specific types of analysis that can be performed on location-enabled data. Its power comes from an ability to zero in on key spatial relationships within large structured and unstructured data sets. Visualizing these relationships provides a useful way of organizing large volumes of data. More important, it can reveal fresh insights that would remain hidden without the interpretive combination of analytical integration and the human brain’s amazing ability to discern visual patterns.
Seeing is Believing
In the past, only a handful of industries—oil and gas, governmental agencies, and transportation and logistics—invested in using location as an organizing principle for advanced analysis. New tools and access to more geographically referenced data are now allowing the power of location to be unleashed across many more business areas and to a much broader base of users. New sources feeding the torrent of geospatial information include new structured data from mobile devices (e.g., phones, tablets, other GPS-assisted assets) and new streams of location-aware unstructured data (e.g., from Twitter, Facebook, Foursquare, and Flickr).
Geospatial visualization can enable the human mind to process and detect patterns hidden among huge volumes of information. Spatial analysis provides quantitative evaluation of complex relationships. Time-based animation and other forms of interactive visualization reveal long-term trends and subtle events. Real-time visualization can help drive better decisions on a daily or even hourly basis.
Perhaps most importantly, geospatial visualization’s familiarity and intuitiveness make it one of the most accessible manifestations of analytics. It provides both a compelling and widely usable form of insight derived from information automation and big data.
A case in point: A hardware company used geospatial visualization to understand why customer satisfaction levels had declined. It mapped customer sentiment against service center locations, traffic patterns, and competitor presence. The issue quickly became apparent. To save costs, the company had moved its customer service facilities away from downtown areas, which significantly increased the travel time for customers seeking support at the facilities. These customers were the source of negative feedback. To address their concerns, the company launched an education program on how to use its virtual support channels, initiated on-location services for a handful of large accounts, and simply acknowledged the issue with customers. This allowed the company to win back the good faith of many of its disgruntled customers.
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Hans Rosling’s 200 Countries, 200 Years, 4 Minutes - The Joy of Stats - BBC Four (by BBC)
Graphing Every* Idea In History
Notice the asterisk? There are important caveats to this work! I was originally put onto network visualization by Simon Raper by his fantastic post graphing the history of philosophy. I’ve learned a lot in the last week and decided to be ambitious. I wanted to see what the entire network would look like – with everyone on Wikipedia. Well, everyone with an infobox containing ‘influences’ and/or ‘influenced by’. For those unfamiliar to this work please see his post first – even if it is just for the pictures!
For those new to this type of thing: the node size represents the number of connections. In short, I used a database version of Wikipedia to extract all people with known influences and made this map. The bigger the node, the bigger influence that person had on the rest of the network. Nietzsche, Kant, Hegel, Hemingway, Shakespeare, Plato, Aristotle, Kafka, and Lovecraft all, as one would expect, appear as the largest nodes. Around these nodes, cluster other personalities who are affiliated (depends on distance). Highlighting communities by color reveals sub-networks within the total structure. You’ll notice common themes amongst similarly colored authors.
(Continued at Graphing Every* Idea In History « Griff’s Graphs)
London Urban Form 3D Map
The structure of large cities such as London is complex and endlessly fascinating. Effective visualisation can reveal the many patterns in urban structures for research and planning tasks, and the visualisation challenge is to manage the multi-dimensional and dynamic nature of urban complexity. Here we explore the geography of land-use and density across Greater London using 3D cartography at a 500 metre grid scale (HD version here):