Big Data means Advanced Data Visualization
The last few years have been particularly exciting for data visualizations. We’ve witnessed a boom in the popularity of infographics and in tools to help create everyday visualizations for practical purposes. With all these exciting developments it’s difficult not to wonder what the future of this field will look like. Industry-renowned data visualization expert, Edward Tufte once said “The world is complex, dynamic, multidimensional; the paper is static, flat. How are we to represent the rich visual world of experience and measurement on mere flatland?”
Texas drought maps and photos
Various plans for dealing with future droughts and growing demand for water in Texas exist, but most comprehensive — and accepted — is the state Water Plan. It offers a frank assessment of the current landscape, saying Texas “does not and will not have enough water to meet the needs of its people, its businesses, and its agricultural enterprises.” It predicts that “if a drought affected the entire state like it did in the 1950s,” Texas could lose around $116 billion, over a million jobs, and the growing state's population could actually shrink by 1.4 million people.
Visualize this: Is it information or is it art?
By John Grimwade An old infographic chestnut. It’s always been a tricky balance between getting the story across, and making a great image. But thanks to some serious computing power, we’ve arrived at a crunch point.
"Visualizing 'Big Data' in the Arts and Humanities
On Wednesday, September 26, 2012 from 3:00pm to 4:30pm in 150A Thompson Library, the Humanities Institute and the Digital Arts and Humanities Working Group will host a panel discussion on "Visualizing 'Big Data' in the Arts and Humanities.” Panelists David Staley (History), Jessie Labov (Slavic & East European Languages & Cultures), and H. Lewis Ulman (English) will explore the place of data visualization as a form of humanities scholarship, with visualization as the hermeneutic act that allows humanists to read “big data.”
Editorial: Visualisation Tools for Understanding Big Data
I recently co-wrote an editorial (download the full version here) with Mike Batty (UCL CASA) in which we explored some of the current issues surrounding the visualisation of large urban datasets. We were inspired to write it following the CASA Smart Cities conference and we included a couple of visualisations I have blogged here. Much of the day was devoted to demonstrating the potential of data visualisation to help us better understand our cities. Such visualisations would not have been possible a few years ago using desktop computers their production has ballooned as a result of recent technological (and in the case of OpenData, political) advances. In the editorial we argue that the many new visualisations, such as the map of London bus trips above, share much in common with the work of early geographers and explorers whose interests were in the description of often-unknown processes.
Fast Thinking and Slow Thinking Visualisation
Last week I attended the Association of American Geographers Annual Conference and heard a talk by Robert Groves, Director of the US Census Bureau. Aside the impressiveness of the bureau’s work I was struck by how Groves conceived of visualisations as requiring either fast thinking or slow thinking. Fast thinking data visualisations offer a clear message without the need for the viewer to spend more than a few seconds exploring them. These tend to be much simpler in appearance, such as my map of the distance that London Underground trains travel during rush hour. The explicit message of this map is that surprisingly large distances are covered across the network and that the Central Line rolling stock travels furthest. It is up to the reader to work out why this may be the case.
Why Is Data Visualization So Hot?
Noah Iliinsky is the co-author of Designing Data Visualizations and technical editor of, and a contributor to, Beautiful Visualization, published By O’Reilly Media. He will lead a Designing Data Visualizations Workshop at O’Reilly’s Strata conference on Tuesday, Feb. 28. Data visualization is hot. All of a sudden there are dozens of companies and products that want to help you visually analyze your data, build your own visualizations, and visually display interesting data sets of all kinds. So, why is visualization interesting? Why is it desirable?
Designing Data Visualizations Workshop: Strata 2012 - O'Reilly Conferences, February 28 - March 01, 2012
Attendees: All attendees should bring paper an pen for quick sketching. Attendees should bring their own data to work with. Alternately, they can download interesting data sets from sites such as infochimps.com, buzzdata.com, and data.gov.
Taxonomy for interactive visual analysis
Interactive visualization continues to grow more useful and prominent in every day analysis. Jeffrey Heer and Ben Shneiderman offer a taxonomy for the budding field . Visualization provides a powerful means of making sense of data. By mapping data attributes to visual properties such as position, size, shape, and color, visualization designers leverage perceptual skills to help users discern and interpret patterns within data.
3 Trends That Will Define The Future Of Infographics
Now that everyone loves them, early adopters and forward thinkers want to know what is next for the infographic. Is this just the beginning of a visual revolution, or have they already jumped the shark? This is an important question, especially for those who are making large investments in the medium, such as publishers and marketers. Is the Infographic Dead? My cofounder, Jason Lankow, says it well when people ask about the fate of infographics in the face of increasing web saturation.
Interactive Dynamics for Visual Analysis
Graphics Jeffrey Heer, Stanford University Ben Shneiderman, University of Maryland, College Park The increasing scale and availability of digital data provides an extraordinary resource for informing public policy, scientific discovery, business strategy, and even our personal lives. To get the most out of such data, however, users must be able to make sense of it: to pursue questions, uncover patterns of interest, and identify (and potentially correct) errors. In concert with data-management systems and statistical algorithms, analysis requires contextualized human judgments regarding the domain-specific significance of the clusters, trends, and outliers discovered in data.
