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Data Visualization

7 things you should know about Data Visualization

Daniel Kouvo

on 18 May 2011

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Transcript of Data Visualization

Data visualization What is it? Data visualization is the use of tools to represent data in the form of charts, maps, tag clouds, animations, or any graphical means that make content easier to understand. The past two years has seen a blossoming of visualization applications, as well as of tech nologies and infrastructure to support increasingly sophisticated visual representations of data. The greatest change, however, may be in access to data. Electronic sensors, for example, have made weather information available on a previously unimaginable scale. While geographic information systems (GIS) have for years allowed individuals to gather, transform, and analyze data, new tools have become widely available that easily create unique mashups of disparate sources of data, as evidenced by the increasing number of applications that employ Google Earth and Google Maps. Growing access to information from education, government, astronomy, geology, medicine, and news offers an increasingly widening pool of data that can be combined to create impressive visuals ranging from cartography to cartoons. Who’s doing it? Data visualization tools are popular among those who use social networking sites—on Facebook, for instance, users can create “friend maps” (digital ballandstick representations that show networks of friends), while a Flickr mapping function lets photog raphers easily show where they took photos. Twitter users can post from visual decks like Twittervision, where tweets appear on a world map, or they can use tools such as Stweet, a mashup of Twitter with Google Street View that shows a photo of the street from which the tweet is sent. In academe, some users have turned to VUE (Visual Under standing Environment), a concept-mapping tool designed at Tufts University that facilitates creation of knowledge maps. At Columbia University, Professor Peter Eisenberger encourages students in his interdisciplinary course, The Earth/Human System, to use VUE to create maps linking the complex problems of sustainability to issues in their own fields of biology, physics, and social sciences. More widely known is the IBM project Many Eyes, which provides both a suite of visualization tools and a public forum for people to share the data visualizations they create. Google maps and Google Earth have made their way into classes and other places where association of data with geography is valuable, as when students at the University of British Columbia created a Google Maps visualization to help others locate healthy local food sources in “food security” assessments of neighborhoods in Vancouver. How does it work? Creating a visual reprsentation of statistics once involved compiling data, interpreting it, parsing it, and then determining what kind of visual presentation would best elucidate what the data meant. New data visualization tools provide a shortcut - a straight line from compiling data to illustrating it. The simplest visualization tools function as a kind of black box. The application presents an interface where users can paste data or link to a data source, spreadsheet, or RSS, and the tool then turns the data into a graphic, such as an animated map, an interactive chart, or a word cloud. More highly skilled users can use a wide range of technology tools to create almost any kind of visualization they can conceive, but the importance of newer tools is that they allow nontechnical users to present data from any discipline - or from multiple disciplines - without having to learn how to use complex modeling or multimedia software. Finding a tool
that can turn data
into an appropriate,
informative visualization can be difficult, but a number of people
use the library of applications at IBM’s open­source venue Many
Eyes or the Visualization Lab available from the New York Times. Why is it significant? Graphic representations of data are popular because they open up the way we think about data, reveal hidden patterns, and highlight connections between elements. Traditionally, it was researchers who designed visuals to make trends clear to an academic or lay audience. Now, because current web applications allow anyone with access to data to enter information and easily create a virtualization of it, students, informal learners, and the purely curious can now easily create visualizations that might reveal trends that are not obvious from the numbers alone. Such easytoaccess tools could simplify the interpretation of complex data sets and encourage crossdisciplinary interpretation. Whereas visualizations once
were often too complex for quick
assimilation, tools that create interactive visualizations provide users with some measure of control over how - and how quickly the information is presented, making complex patterns easier to perceive and understand. Wordle and some of the tools at Many Eyes create visual montages of words, sentences, phrases, or paragraphs uploaded
and processed so that the audience,
examining the end result,
sees text in a new light. What are the downsides? Considering the rapid rise in information available and the power and ubiquity of webbased applications, there is a remarkable scarcity of compelling visualization examples that leverage these opportunities. While many tools are easy to use and turn out engaging graphics, there is a risk that a resulting image might not illustrate the most significant implications of the data. Simirarly, these tools might encourage students to substitute gratuitous graphics for more meaningful content in their presentations. The low barrier of entry could make output from these tools vulnerable to over­interpretation or misinterpretation. Because individuals unaccustomed to interpreting statistics can plug data into one of these tools without a clear idea of its significance, the resulting visual information might shift focus to curious anomalies rather than capture a valid trend. As a result, the graphic representation could mislead those inexperienced in the use and interpretation of statistics. Where is it going? Data visualization has become more ubiquitous as familiarity with web tools has increased and as people have come to terms with the sheer volume of webaccessible data. Data overload from on line public records, sensor data from weather stations accessible from any keyboard, and readily accessible educational and scholarly resources represents a growing challenge for academics and researchers, and so it makes sense to expect the data to drive the tools in the next few years. As general audiences become more discerning and comfortable with increased animation and complex interactivity, they will expect visualizations to map effectively to the data they are supposed to represent. Even as wall­size screens at public venues invite presenters to fill them with complex graphics, mobile devices will continue to require visual adaptation to their smaller viewing screens. Meanwhile, graphical representations, in whatever form they take, will be expected to clarify the narrative in an environment that combines increasingly sophisticated multimedia presentation with ever­increasing amounts and types of data. What are
the implications
for teaching
and learning? For scholars, particularly those whose conclusions depend on interpretation of complex statistics, data visualization offers the promise of easier communication and a wider audience for their findings. As visualization tools become easier to implement, educators and students stand to reap considerable benefit. Educators can present their points in an increasingly engaging form, and students making presentations or delivering papers have the opportunity to graphically incorporate statistics to make points that their peers can easily understand. In addition to adding considerable value by making both meaning and data accessible, visualization tools allow end users to personalize or take ownership of data. Studying an engaging visual representation of information can help bring data to life and draw it into the reach of non­experts.
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