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Stephen Few doesn't like radar graphs , and he's not the only one who has written against them . In a recent discussion on Twitter, Jon Peltier said that they are "worse than pies" —ouch! Even Andy Kirk, who is usually as polite as a British gentleman can be, doesn't have nice words about this kind of display. Most of the arguments against radar graphs can be summarized in a couple of sentences from this post by Graham Odds: "Even with a common scale between axes, comparing values across them remains cumbersome and error-prone. This is because rather than the simple straight-line comparison our visual perception is hard-wired to perform that is found in “conventional” chart types, comparison in radar charts requires conscious thought to mentally project a sort of arc of rotation to map a value from one axis onto another, something we are not particularly adept at."
We’ve made the point time and time again that charts and graphs, though they feel official and true, can lie. Rarely do you get to see that at work, but the good folks at Hyperakt have sent us a prime case study in infographic deception. The subject, of course, is politics--and in particular, the raging debate over whether the rich should be made to pay more taxes. "Using the same data, very different stories can be told depending on different agendas," says Deroy Peraza, one of the founders of Hyperakt. A story from the Wall Street Journal 's far-right op-ed page gets us started , with a chart showing how much taxable income is made by Americans ranging from the rich to poor: Looking at that, the conclusion seems glaringly obvious: The rich don’t make so much money!
Information can be useful--and even beautiful--but only when it’s presented well. In an age of information overload, any guidance through the clutter comes as a welcome relief. That’s one reason for the recent popularity of information graphics. Infographics are visual designs that help to explain complicated data in a simple way (mental-health emergencies at Burning Man, anyone?).
<img class="aligncenter size-medium wp-image-3558" title="eastcoast-90dpi_export" src="http://vis4.net/blog/wp-content/uploads/2012/04/eastcoast-90dpi_export-e1333457462704.png" alt="" width="519" height="248" /> This is going to be a quick run-through the creation of the latest Kartograph showcase which is a high res vector map . Select your map projection I really like the idea of starting the map creation process with choosing a map projection. As mentioned in my last post , the projection can be seen as a very crucial point of every map. It allows you to define the perspective on the geography.
We know that The New York Times graphics department produces some of the best visualizations in the world. We have seen its director, Steve Duenes, give a talk about some of their best works and provide some answers on their internal approach. Who I had not seen talk before, however, is Amanda Cox, whose name is often featured on quite some innovative works, such as the voronoi treemap showing the Consumer Price Index, the interactive timeline revealing Michael Jackson's career statistics, the streamgraphs that map how people spend their day, or the dense yet intuitive line graphs that unravel the potential socio-demographic drivers of the unemployment in the US. In the small collection of (4 different) videos you can watch below , Amanda shares some of the lessons the group has learned along the way, particularly on how to integrate real interactivity with storytelling, and how to strike a balance between clarity and creating a sense of wonder.
We’ve all heard it: according to Hal Varian, statistics is the next sexy job . Five years ago, in What is Web 2.0 , Tim O’Reilly said that “data is the next Intel Inside.” But what does that statement mean?
Important Tools for Visualising and Communicating Data
The Bo Sundgren Award of the International Marketing and Output Database Conference IMAODBC 2011 in Obidos Portugal goes to Xavier Badosa from the Statistical Institute of Catalonia Idescat . In his contribution Xavier presented a change of focus or even of paradigm in disseminating official statistics. This gives an interesting insight in developments some offices (like i.e. idescat) have already made or are about to discuss:
Owni’s data team – collectively known as Paule d’Atha – are pleased to present a selection of the best of the best of their weekly round-up of data on the web, from a year that’s seen so many wonderful, innovative, inventive, colorful, moving and funny projects. The future is on the cards The World of Seven Billion is a project powered by National Geographic to celebrate the arrival of the seven billionth person on Earth. The contrast between the black background and the bright colors used to represent different income levels, coupled with population density, across all five continents gives the map a beautiful clarity.
Data values are not 'certain', and there is (almost always) some degree of uncertainty. For example, survey values are usually provided with Confidence Intervals showing the likely range of the data values. Visualisations can show uncertainty alongside data values, for example presenting the Confidence Intervals to indicate the range within which a survey result might lie. This visualisation of uncertainty can help with both analysing the data, and communicating results. <p style="text-align:right;color:#A8A8A8"></p>
Binning is a general term for grouping a dataset of N values into less than N discrete groups. These groups/bins may be spatial, temporal, or otherwise attribute-based.
Cartographer Zachary Forest Johnson has presented a novel way of representing a large number of geo-located points on a map. He took inspiration from the original 'hexbin' visualization method, short for hexagonal binning (PDF!)
Designers, in many ways, are quintessential first adopters and ideal test customers: They’re technically savvy and demanding, with an extreme attention to detail and polish.
In official statistics we’re used to dealing with highly aggregated data. To visualise those, bar-, line- and pie charts are standard tools. But there is a whole other side to visualisation where it is used to recognize patterns, outliers or errors in individual data.