Category Archives: Data Visualization

DataViz: Squaring the Pie Chart (Waffle Chart)

Readers:

Robert-Kosara-Tableau-Software-200x200In the past, I would have highly condemned pie charts without giving you much explanation why. However, Dr. Robert Kosara (photo, left), posted a great thought study of pie charts on his wonderful blog, EagerEyes.org, that I want to share with you.

Dr. Kosara is a Visual Analytics Researcher at Tableau Software, with a special interest in the communication of, and storytelling with, data. He has a Ph.D. in Computer Science from Vienna University of Technology.

Also, as part of his blog post, Robert offers an alternative way to create pie charts: using waffle charts or square pie charts.

Dr. Kosara is also one of the great minds behind Tableau’s new storytelling feature. I hope you enjoy his creative thoughts as much as I do.

Best Regards,

Michael

The Pie Chart

Dr. Kosara contends that pie charts are perhaps the most ubiquitous chart type; they can be found in newspapers, business reports, and many other places. But few people actually understand the function of the pie chart and how to use it properly. In addition to issues stemming from using too many categories, the biggest problem is getting the basic premise: that the pie slices sum up to a meaningful whole.

Touchstone Energy Corporation Pie Chart
Robert points out that the circle (the “pie”) represents some kind of whole, which is made up of the slices. What this means is that the pie chart first and foremost represents the size relationship between the parts and the entire thing. If a company has five divisions, and the pie chart shows profits per division, the sum of all the slices/divisions is the total profits of the company.

Five Slices

 

If the parts do not sum up to a meaningful whole, they cannot be represented in a pie chart, period. It makes no sense to show five different occupations in a pie chart, because there are obviously many missing. The total of such a subsample is not meaningful, and neither is the comparison of each individual value to the artificial whole.

Slices have to be mutually exclusive; by definition, they cannot overlap. The data therefore must not only sum up to a meaningful whole, but the values need to be categorized in such a way that they are not counted several times. A good indicator of something being wrong is when the percentages do not sum up to 100%, like in the infamous Fox News pie chart.

The Infamous Fox News Pie Chart

Fox News Pie Chart

In the pie chart above, people were asked which potential candidates they viewed favorably, but they could name more than one. The categories are thus not mutually exclusive, and the chart makes no sense. At the very least, they would need to show the amount of overlap between any two (and also all three) candidates. Though given the size of the numbers and the margin of error in this data, the chart is entirely meaningless.

When to Use Pie Charts

Dr. Kosara points out that there are some simple criteria that you can use to determine whether a pie chart is the right choice for your data.

  • Do the parts make up a meaningful whole? If not, use a different chart. Only use a pie chart if you can define the entire set in a way that makes sense to the viewer.
  • Are the parts mutually exclusive? If there is overlap between the parts, use a different chart.
  • Do you want to compare the parts to each other or the parts to the whole? If the main purpose is to compare between the parts, use a different chart. The main purpose of the pie chart is to show part-whole relationships.
  • How many parts do you have? If there are more than five to seven, use a different chart. Pie charts with lots of slices (or slices of very different size) are hard to read.

In all other cases, do not use a pie chart. The pie chart is the wrong chart type to use as a default; the bar chart is a much better choice for that. Using a pie chart requires a lot more thought, care, and awareness of its limitations than most other charts.

Alternative: Squaring the Pie

A little-known alternative to the round pie chart is the square pie or waffle chart. It consists of a square that is divided into 10×10 cells, making it possible to read values precisely down to a single percent. Depending on how the areas are laid out (as square as possible seems to be the best idea), it is very easy to compare parts to the whole. The example below is from a redesign Dr. Kosara did a while ago about women and girls in IT and computing-related fields.

Kosara Square Pie

Links to Examples of Waffle Charts

I did a little Googling and found a few great examples of Waffle Charts. I have provided links to examples in Tableau, jQuery, R and Excel. I hope in the new month or so to create an example for you using MicroStrategy.

Squaring The Pie

Sources:

WIRED: A Redesigned Parking Sign So Simple That You’ll Never Get Towed

web-snow-day-1

Your car gets towed, and who do you blame? Yourself? God no, you blame that impossibly confusing parking sign. It’s a fair accusation, really. Of all the questionable communication tools our cities use, parking signs are easily among the worst offenders. There are arrows pointing every which way, ambiguous meter instructions and permit requirements. A sign will tell you that you can park until 8 am, then right below it another reading you’ll be towed. It’s easy to imagine that beyond basic tests for legibility, most of these signs have never been vetted by actual drivers.

Like most urban drivers, Nikki Sylianteng was sick of getting tickets. During her time in Los Angeles, the now Brooklyn-based designer paid the city far more than she would’ve liked to. So she began thinking about how she might be able to solve this problem through design. She realized that with just a little more focus on usability, parking signs could actually be useful. “I’m not setting out to change the entire system,” she says. “It’s just something that I thought would help frustrated drivers.” [1]

Sylianteng notes: [2]

I’ve gotten one-too-many parking tickets because I’ve misinterpreted street parking signs. The current design also poses a driving hazard as it requires drivers to slow down while trying to follow the logic of what the sign is really saying. It shouldn’t have to be this complicated.

The only questions on everyone’s minds are:
1. “Can I park here now?”
2. “Until what time?”

My strategy was to visualize the blocks of time when parking is allowed and not allowed. I kept everything else the same – the colors and the form factor – as my intention with this redesign is to show how big a difference a thoughtful, though conservative and low budget, approach can make in terms of time and stress saved for the driver. I tried to stay mindful of the constraints that a large organization like the Department of Transportation must face for a seemingly small change such as this.

01 two-step

The sign has undergone multiple iterations, but the most recent features a parking schedule that shows a whole 24 hours for every day of the week. The times you can park are marked by blocks of green, the times you can’t are blocked in a candy-striped red and white. It’s totally stripped down, almost to the point of being confusing itself. But Sylianteng says there’s really no need for the extraneous detailed information we’ve become accustomed to. “Parking signs are trying to communicate very accurately what the rules actually are,” she says. “I’ve never looked at a sign and felt like there was any value in knowing why I couldn’t park. These designs don’t say why, but the ‘what’ is very clear.”

Sylianteng’s design still has a way to go. First, there’s the issue of color blindness, a factor she’s keenly aware of. The red and green are part of the legacy design from current signs, but she says it’s likely she’d ultimately change the colors to something more universal like blue. Then there’s the fact that urban parking is a far more complex affair than most of us care to know. There’s an entire manual on parking regulations; and Sylianteng’s design does gloss over rules concerning different types of vehicles and space parameters indicating where people can park. She’s working on ways to incorporate all of that without reverting back to the information overload she was trying to avoid in the first place. [1]

redesigned-parking-inline2

Sylianteng also posted on her blog an illustration of the problem in terms of biocost, as part of her Cybernetics class with Paul Pangaro. [2]

Biocost_ParkingSign

Sylianteng has been going around Manhattan and Brooklyn hanging up rogue revamped parking signs. “A friend of mine called it functional graffiti,” she says. She’ll stick a laminated version right below the city-approved version and ask drivers to leave comments. In that way, Sylianteng’s design is still a ways away from being a reality, but so far, she’s gotten pretty good feedback. “One person wrote: ‘The is awesome. The mayor should hire you.’” [1]

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Sources:

[1] Liz Stinson, A Redesigned Parking Sign So Simple That You’ll Never Get Towed, Wired, July 15, 2014, http://www.wired.com/2014/07/a-redesigned-parking-sign-so-simple-youll-never-get-towed-again.

[2] Nikki Sylianteng, blog, http://nikkisylianteng.com/project/parking-sign-redesign/.

12 JavaScript Libraries for Data Visualization

Readers:

This is from a blog post by Thomas Greco.

Thomas GrecoThomas is a web developer / graphic designer living in New York City. When Thomas isn’t striving towards front­end perfection, he enjoys hanging with friends, going to concerts, and exploring through the wilderness!

Thomas has provided twelve JavaScript frameworks that are extremely useful for data visualization. Thomas feels that a more heavy focus is being placed on JavaScript as a data visualization tool.

I tried the demos for these JavaScript frameworks and they are very impressive. I hope you enjoyed this information as much as I did.

Best regards,

Michael

Dygraphs.js

The Dygraphs.js library allows developers to create interactive charts using the X and Y axis to display powerful diagrams. The more data being parsed, the higher the functionality of the graph. That being said, Dygraphs was built for these visualizations to contain a multitude of views. For example, Dygraphs.js makes it capable to analyze separate portions of a data-set, such as specific months, in addition to the timeframe in its entirety. Also, the Dygraphs.js library is compatible across all major web browsers, and can responds to touch sensitivity, making it a thoroughougly solid choice as a data visualization framework.

D3.js

Eventually becoming the successor to Protovis.js, D3 is capable of creating stunning graphics via dynamically updating the DOM. An acronym for Data-Driven Document, D3.js makes use of chained methods when scripting visualizations, subsequently creating dynamic code that is also reusable. Due to its reliance on the DOM, D3 has been created in accordance with W3C web standards so that the library may render correctly across web browsers. Lastly, D3′s path generator function, defined as d3.svg.line(), gives developers the capability to produce a handful of SVGs by defining different paths, and their properties.

InfoVis

Commonly referred to as InfoVis, the JavaScript InfoVis Toolkit (JIT) also earned its stripes as a JavaScript library for data visualization. Equipped with WebGL support, InfoVis has been trusted by names like Mozilla and AlJazeera, showing its solidarity as a visualization tool. Along with the D3 framework, InfoVis also makes use of chained methods to manipulate the DOM, making it a reliable library for developers of any skill set.

The Google Visualization API

Hailing from the Google Developers Console (GDC), Google’s Visualization API can be called with barely any code. In addition to easy DOM modification, this Google API makes it easy for its user to easily define custom modifier functions that can then be placed into custom groups. Furthermore, this interface’s usability, matched with its support from the GDC’s open source network, place it among the top of the list of data visualization tools.

Springy.js

Springy.js is a JavaScript library that relies on an algorithm to create force-directed graphs, resulting in nodes reacting in a spring-like manner on the web page. Although Springy.js comes configured with a predefined algorithm, options such as spring stiffness and damping can easily be passed as parameters. Springy.js was developed by Dennis Hotson as a library for developers to build off of – a fact that he makes clear.

Polymaps.js

Polymaps.js makes use of SVGs to generate interactive web maps with cross browser compatibility in mind. At the heart of Polymaps lies vector tiles, which help ensure both optimal load speeds and optimal zoom functionality. Although it may come configured with components, Polymaps.js is easily customized, and is able to read data in the form of vector geometry, GeoJSON Files, and more. Check out the graph below of the U.S. created by the U.S. Census borough.

Dimple

This past January, the Dimple API was developed so that analysts at Align-Alytics could develop strong data visualizations without having to possess much development knowledge. That being said, Dimple makes it easy for anyone, analyst or not, to develop stunning, three dimensional graphics without any real JavaScript training. Moreover, dimplejs.org displays several demonstrations, which can be easily manipulated by one’s personal data to render a graph with the same configuration, but different values. So, if you, or anyone you know is trying to segway into the depths of JavaScript, then these examples are perfect for beginners to vist and poke around.

Sigma.js

For people looking to build highly advanced line graphs, Sigma.js provides an unbelievable amount of interactive settings inside its library, and also within its plug-ins. Hailing a motto that states “Dedicated to Graph Drawing”, those developing using Sigma.js cannot help but feel like they have chosen a reliable library to work with. Moreover, Sigma’s developers encourage people to re-configure this library and create plug-ins, which has resulted in a large open-source network. Having said all that, I was extremely pleased with various aspects of Sigma, and it is among my favorite libraries for creating graphical representations in JavaScript.

Raphael.js

The Raphael.js library was created with an emphasis on browser compatibility. The framework follows the SVG W3C Recommendation, which is a set of standards that ensure images are completely scalable and without pixelation. In addition to the use of SVGs, Raphael.js even reverts to the Vector Model Language (VML) if rendered in Internet Explorer browsers prior to IE9. Although VML is very rarely used today, the support for it does a great job of showing the attention to detail that the Raphael.js team placed on this project when developing the library.

gRaphaël

Although Raphael.js is a library used to for the creation of SVGs, it was not built with a total focus on the representation of large datasets. In turn, the gRaphaël JavaScript library was created. Weighing in at a mere 10KB, gRaphaël.js has proven to be a worthy extension to Raphael.js. Although it may have not been developed behind things like a force-driven algorithm, nor does it come pre-configured with any physics properties, gRaphaël is still a well respected library for reasons ranging from its cross-compatible SVG structure, to its ease of use. As long as it coincides with the task at hand, I believe that gRaphaël.js should always be looked at as a viable resource to complete a project.

Leaflet

Whether developing for a smartphone, tablet, or desktop, the Leaflet JavaScript library has ranked atop the list of interactive mapping libraries for several reasons. Lead by the founder of MapBox, Vladimir Agafonkin, the Leaflets team of developers worked to create a library “designed with simplicity, performance, and usability in mind.” Along with Polymaps, Leaflet shares the ability to render SVG pattens via vector tiles, however only Leaflet has been developed to support Retina display. Furthermore, Leaflet can interpret various forms of data such as GeoJSON, making it perfect for a number of tasks.

Ember Charts

For those who already use the juggernaut that is Ember.js, the developers at Addepar Open Source have created a few add-on libraries to extend the Ember experience: Ember Table, Ember Widgets, and Ember Charts. A child of Ember.js and D3.js, Ember Charts utilizes the properties of flat-design. Although limited, the library does have a handful of options that deal with properties such as color and size, making it fairly simple to create impressive visualizations. Nonetheless, Ember’s presence in the front end could really help Ember Chart’s popularity in the future.

Stephen Few: Why Do We Visualize Quantitative Data?

Readers:

Stephen_FewIt has been a while since I have discussed some of the latest creative thoughts on data visualization from Stephen Few. I have read all of Steve’s books, attended several classes from him, and religiously follow his blog and newsletter on his website, Perceptual Edge.

For those of you who don’t know, Stephen Few is the Founder & Principal of Perceptual Edge. Perceptual Edge, founded in 2003, is a consultancy that was established to help organizations learn to design simple information displays for effective analysis and communication.

Steve has stated that his company will probably always be a company of one or two people, which is the perfect size for him. With 25 years of experience as an innovator, consultant, and educator in the fields of business intelligence and information design, he is now considered the leading expert in data visualization for data sense-making and communication.

Steve writes a quarterly Visual Business Intelligence Newsletter, speaks and teaches internationally, and provides design consulting. In 2004, he wrote the first comprehensive and practical guide to business graphics entitled Show Me the Numbers, now in its second edition. In 2006, he wrote the first and only guide to the visual design of dashboards, entitled Information Dashboard Design, also now in its second edition. In 2009, he wrote the first introduction for non-statisticians to visual data analysis, entitled Now You See It.

Here is his latest thoughts from his newsletter.

Best regards,

Michael

 

Why Do We Visualize Quantitative Data?

Per Stephen Few, we visualize quantitative data to perform three fundamental tasks in an effort to achieve three essential goals:

Web

These three tasks are so fundamental to data visualization, Steve used them to define the term, as follows:

Data visualization is the use of visual representations to explore, make sense of, and communicate data.

Steve poses the question of why is it that we must sometimes use graphical displays to perform these tasks rather than other forms of representation? Why not always express values as numbers in tables? Why express them visually rather than audibly?

Essentially, there is only one good reason to express quantitative data visually: some features of quantitative data can be best perceived and understood, and some quantitative tasks can be best performed, when values are displayed graphically. This is so because of the ways our brains work. Vision is by far our dominant sense. We have evolved to perform many data sensing and processing tasks visually. This has been so since the days of our earliest ancestors who survived and learned to thrive on the African savannah. What visual perception evolved to do especially well, it can do faster and better than the conscious thinking parts of our brains. Data exploration, sensemaking, and communication should always involve an intimate collaboration between seeing and thinking (i.e., visual thinking).

Despite this essential reason for visualizing data, people often do it for reasons that are misguided. Steve dispels a few common myths about data visualization.

Myth #1: We visualize data because some people are visual learners.

While it is true that some people have greater visual thinking abilities than others and that some people have a greater interest in images than others, all people with normal perceptual abilities are predominantly visual. Everyone benefits from data visualization, whether they consider themselves visual learners or not, including those who prefer numbers.

Myth #2: We visualize data for people who have difficulty understanding numbers.

While it is true that some people are more comfortable with quantitative concepts and mathematics than others, even the brightest mathematicians benefit from seeing quantitative information displayed visually. Data visualization is not a dumbed-down expression of quantitative concepts.

Myth #3: We visualize data to grab people’s attention with eye-catching but inevitably less informative displays.

Visualizations don’t need to be dumbed down to be engaging. It isn’t necessary to sacrifice content in lieu of appearance. Data can always be displayed in ways that are optimally informative, pleasing to the eye, and engaging. To engage with a data display without being well-informed of something useful is a waste.

Myth #4: The best data visualizers are those who have been trained in graphic arts.

While training in graphic arts can be useful, it is much more important to understand the data and be trained in visual thinking and communication. Graphic arts training that focuses on marketing (i.e., persuading people to buy or do something through manipulation) and artistry rather than communication can actually get in the way of effective data visualization.

Myth #5: Graphics provide the best means of telling stories contained in data.

While it is true that graphics are often useful and sometimes even essential for data-based storytelling, it isn’t storytelling itself that demands graphics. Much of storytelling is best expressed in words and numbers rather than images. Graphics are useful for storytelling because some features of data are best understood by our brains when they’re presented visually.

We visualize data because the human brain can perceive particular quantitative features and perform particular quantitative tasks most effectively when the data is expressed graphically. Visual data processing provides optimal support for the following:

1. Seeing the big picture

Graphs reveal the big picture: an overview of a data set. An overview summarizes the data’s essential characteristics, from which we can discern what’s routine vs. exceptional.

The series of three bar graphs below provides an overview of the opinions that 15 countries had about America in 2004, not long after the events of 9/11 and the military campaigns that followed.

graph-of-country-opinions

Steve first discovered this information in the following form on the website of PBS:

table-of-country-opinions

Based on this table of numbers, he had to read each value one at a time and, because working memory is limited to three or four simultaneous chunks of information at a time, he couldn’t use this display to construct and hold an overview of these countries’ opinions in his head. To solve this problem, he redisplayed this information as the three bar graphs shown above, which provided the overview that he wanted. Steve was able to use it to quickly get a sense of these countries’ opinions overall and in comparison to one another.

Bonus: Here is a link to where Steve discusses the example above on his website.

2. Easily and rapidly comparing values

Try to quickly compare the magnitudes of values using a table of numbers, such as the one shown above. You can’t, because numbers must be read one at a time and only two numbers can be compared at a time. Graphs, however, such as the bar graphs above, make it possible to see all of the values at once and to easily and rapidly compare them.

3. Seeing patterns among values

Many quantitative messages are revealed in patterns formed by sets of values. These patterns describe the nature of change through time, how values are distributed, and correlations, to name a few.

Try to construct the pattern of monthly change in either domestic or international sales for the entire year using the table below.

table-of-sales-data

Difficult, isn’t it? The line graph below, however, presents the patterns of change in a way that can be perceived immediately, without conscious effort.

graph-of-sales-data

You can thank processes that take place in your visual cortex for this. The visual cortex perceives patterns and then the conscious thinking parts of our brains make sense of them.

4. Comparing patterns

Visual representations of patterns are easy to compare. Not only can the independent patterns of domestic and international sales be easily perceived by viewing the graph above, but they can also be compared to one another to determine how they are similar and different.

In Summary

These four quantitative features and activities require visual displays. This is why we visualize quantitative data.

Forbes: Data Visualization Is The Future – Here’s Why

Readers:

Dorie ClarkI read this blog post from Dorie Clark back in March. I keep notes on interesting blogs and articles I come across and wanted to share this one with you regards the importance of data visualization.

Dorie Clark is a marketing strategist and professional speaker who teaches at Duke University’s Fuqua School of Business. Learn more about her new book Reinventing You: Define Your Brand, Imagine Your Future (Harvard Business Review Press) and follow her on Twitter.

I hope you find this helpful in your data visualization endeavors.

Best regards,

Michael

Data Visualization Is The Future – Here’s Why

We’ve all heard that Big Data is the future. But according to Phil Simon’s new book The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions, that may not be quite right. Big Data is a powerful discovery tool for companies seeking to glean new insights. But without the right framework for understanding it, much of that knowledge may go unrecognized. Oftentimes, it’s data visualization that allows Big Data to unleash its true impact.

The Visual Organization is fundamentally about how progressive organizations today are using a wide array of data visualization (dataviz) tools to ask better questions of their data – and make better business decisions,” says Simon, citing the example of companies such as Amazon, Apple , Facebook, Google, Twitter, and Netflix, among others.

Phil Simon
Data visualization allows Big Data to unleash its true impact, as author Phil Simon explains.

Two recent factors have conspired to make this the moment for data visualization. First, says Simon, is the rise of Big Data and the growing public awareness of its power. “Today more than ever, professionals are being asked to argue their cases and make their decisions based on data,” he says. “A new, data-oriented mind-set is permeating the business world.”

But that push outside IT circles means that many non-technical professionals must now produce and comprehend insights from Big Data. Visualization can help, and a raft of new tools makes that possible. “IBM, Cognos, SAS, and other enterprise BI (business intelligence) stalwarts are still around, but they are no longer the only game in town,” he says. “Today, an organization need not spend hundreds of thousands or millions of dollars to get going with dataviz. These new tools have become progressively more powerful and democratic over the last decade. Long gone are the days in which IT needed to generate reports for non-technical employees. They have made it easier than ever to for employees to quickly discover new things in increasingly large datasets. Examples include Visual.ly, Tableau, Vizify, D3.js, R, and myriad others.”

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Source: Dorie Clark, Data Visualization Is The Future – Here’s Why, Forbes, March 10, 2014, http://www.forbes.com/sites/dorieclark/2014/03/10/data-visualization-is-the-future-heres-why/.

Charles Apple: Two Recent Infographic Fails You Ought to Know About

Readers:

Charles AppleI have been a big fan of Charles Apple’s work for a long time. I have blogged about him and his work in the past (see “Charles Apple” in my Categories on the right or do a search for “Charles Apple” on my blog).

Charles Apple (photo, right) is a longtime news artist, graphics reporter, designer, editor and blogger. The former graphics director of the Virginian-Pilot and the Des Moines Register, he spent five years as an international consultant and instructor. Currently, he’s Focus page editor of the Orange County Register.

I always like to reshare articles and blogs about what NOT to do in regards to data visualization and infographics. This morning, Mr. Apple posted a blog entry titled “Two recent infographic fails you ought to know about.” Charles has always shown a keen eye for detail and accuracy. He is also very reflective of his own work as today’s blog entry shows.

I hope you enjoy Mr. Apple’s thoughts as much as I do.

Have a great Good Friday and Happy Easter.

Best Regards,

Michael

Source: Charles Apple, Two recent infographic fails you ought to know about, http://www.charlesapple.com, April 18, 2014, http://www.charlesapple.com/2014/04/two-recent-infographic-fails-you-ought-to-know-about/.

Two recent infographic fails you ought to know about

A couple of charting debacles popped up this week of which you might want to take note.

POSITIVE VS. NEGATIVE SPACE

First, Reuters moved this fever chart showing the number of gun deaths in Florida going up after the state enacted its “stand your ground” law in 2005.

Just one little problem: The artist — for some unknown reason — elected to build the chart upside down from the usual way a fever chart is drawn.1404GunDeaths01Meaning the chart appears to show the number of gun deaths going down… if you focus on the white territory and consider the red to be the background of the chart.

After a lively discussion on a number of forums — most notably at Business Insider — a reader volunteered to flip the chart right-side around for clarity’s sake.1404GunDeaths02Is that better? Most folks seem to think it is.1404GunDeaths03Three important rules about infographics that I’m making up right here:

Rule 1: A graphic must be clear. If it’s not clear, then it’s not doing its job and should probably be put out of its misery.

Rule 2: It’s OK for a graphic to offer the reader a longer, more complicated view that requires more time spent observing a piece. But that’s not typically the job of a freakin’ one-column graphic.

Rule 3: Occasionally, it’s OK to flip a graphic upside down. But you’d better have a damned good reason for doing it. Other than, y’know, “I thought it’d look cool.”

This graphic fails all three: It’s not immediately clear — at least to many readers — and it’s a small graphic. So it has no business getting fancy. If the artist had a reason for turning it upside down, that reason eludes me.

Read more about the debate over this piece at…

UPSIDE DOWN YOU’RE TURNING ME

Full disclosure: I feel a little guilty criticizing this piece because I myself did something funky last week: I turned a map upside down:Unnamed_CCI_EPS

That ran in the middle of a page about John Steinbeck‘s the Grapes of Wrath. The intent was to show the route the fictional Joad family took in the book from the dust bowl of Oklahoma to what they hoped would be a better life here in Southern California.But vI really wanted to get those two pictures in there, which needed to read from left to right. I wanted those to sit atop my map showing the journey. I tried mapping it the usual way, but it was difficult to get the reader to stop — and then read this one segment of my page from right to left — and then resume reading the rest of the page from left to right.This would take quite a bit more vertical space and some very careful use of labels. And I was plum out of vertical space.So I elected to flop the map upside down. My logic: This time, it was more important to follow the narrative — to feel the twists and turns in the Joads’ journey — than to take in the geographical details of the trip. If the upside-down map was vetoed, Plan B would have been to kill the map and run the list of cities in a timeline-like format. There was just one problem with that: I already had a timeline on the page, just above the map:

Unnamed_CCI_EPS

We debated this and decided I was right to flip the map — This time. I can’t imagine too many times we’d ever want to run a map with the north arrow pointing down.And, y’know, perhaps we did the wrong thing. Another editor might have made a different choice.But the point is: We made a conscious decision here to let the map support the narrative. I don’t know what point Reuters was making with its upside-down fever chart. Whatever it was, it’s not apparent to me.It’s OK to make unusual choices. Just make sure your data is clear, your story is clear and readers don’t walk way from your piece puzzled as hell.

WHEN IS A MAP NOT A MAP?

This seems like a good time to present the other infographics debacle this week: This one is by NBC News.1404DemographicsOh, dear. I was just talking about using a map when the map wasn’t the most important element.What we have here is another fever chart, but this one has been pasted inside a map of the U.S. This has a number of effects that harm the greater good we do by presenting the data in the first place:Fever charts (and pie charts and bar charts and most other charts, for that matter) are all about showing proportions. If the proportions get screwed up — by, say, varying the widths of your bars or by covering up part of the chart — then the reader can’t make the visual comparisons you’re asking her to make.And that’s the case here: We see territory marked as “Asian” in the upper left of the chart and also at the upper right. But where is that set of data in 2010? I’m guessing it’s there, but it’s hidden outside the area of the map.

Rule 4: If you’re going to hide important parts of your chart, then your chart is no good. And, yes, it should be put out of its misery.

The data is displayed over a map. What is the artist trying to tell us? Where white people live in the U.S.? That Hispanics only live near Canada and Asians in Washington State and New England?No, the map is merely a decorative element. It has nothing at all to do with the data.

Rule 5: If you don’t need an element to tell your story, then eliminate it. Or I will.

Rule 6: If your decorative element gets in the way of your story, then not only do I demand you eliminate it, I also insist you come over here so I can smack you upside your head.

Rule 7: Don’t use a map if you’re not telling a story that includes some type of data that needs geographical context.

Oh, and don’t forget this last one:

Rule 8: Don’t tilt a map or turn it upside down. Not unless you have a good reason.

Go here to read more about the perils of rotating maps.

A Must Have Tool: The Data Visualisation Catalogue

Data Visualisation Catalogue

Readers:

This is something I find to be very worthwhile and a great tool to have available when you have data, but can’t decide on which visualization is best to use.

The Data Visualisation Catalogue is currently an ongoing project developed by Severino Ribecca.

Originally, Severino started this project as a way to develop his own knowledge of data visualisation and to create a reference tool for him to use in the future for his own work. Fortunately for us, Severino thought it would also be useful tool to not only other designers, but also anyone in a field that requires the use of data visualisation regularly (economists, scientists, statisticians etc).

Severino website is very comprehensive, detailed and can help you decide the right method for your needs.

He plans on adding in new visualisation methods, bit-by-bit, as he continues to research each method to find the best way to explain how it works and what it is best suited for.

The project itself is in the developmental stages at the moment.

All news and website updates can be found on Twitter.

I also encourage you to donate to this cause. I just donated today.

Best regards,

Michael

Below is an example of how Severino has catalogued the Bar Chart

dataviz catalogue 1 dataviz catalogue 2 dataviz catalogue 3

Blurred Lines: A Tale of Two Dashboard (Contests)

Readers:

This is an important read for anyone who works in the data visualization profession. I ask you to be reflective as you read this. I use myself as an example of what not to do.

For the past two years (2013 and 2014), I have submitted an entry into the MicroStrategy World Dashboard Contest. In both years, I was named one of the winners of this competition. I have written about my work and about the competition on this blog. But I did not tell the whole story. I did not mention that my entries were reproductions of the original ideas and designs of other people. I took liberties that, at the time, seemed innocent. As an academic pursuit, I attempted to recreate these originals using a different tool set (in these two cases, MicroStrategy Report Services and the Visualization SDK) to see if it could be done. I spent time trying to develop methods to allow me to recreate the original visualizations almost exactly as their authors had idealize and developed them. I meant no harm, but what I did was wrong.

There are lines and sometimes we cross them. There are lines and sometimes we don’t see them. There are lines that are bold and there are lines that are blurry. The line that I crossed appears bold in retrospect but was blurred at the time. I had spent a considerable amount of time developing these visualizations. It is quite possible that I spent more time trying to recreate the original than the author spent developing the original.

I know now that this does not matter.

I took the ideas and content and submitted it as my own. I am sorry for this and I have learned a great deal as a result. I now want to use this discussion as an example for others.

2013 MicroStrategy Dashboard Contest

My entry in the 2013 Dashboard Contest was a Student Performance Dashboard, which was based on portions of the top three entries in Stephen Few’s Dashboard Design Contest that was held in late 2012. The majority of my dashboard was based on the original design of the first place winner, Jason Lockwood, who had developed his dashboard in Photoshop.

At work, several of us were talking about Jason’s winning entry and how you could probably develop it fairly easily in Tableau, but probably not so easily in MicroStrategy. Being a strong proponent of MicroStrategy, I argued that I could develop that exact dashboard using MicroStrategy’s Report Services and their Visualization SDK. My co-workers challenged me to try it and I began my mission. Unexpectedly, MicroStrategy soon announced their 2013 Dashboard Contest and I thought this would provide me additional motivation by developing the dashboard for their contest.

Back to Stephen’s contest. Back in August of 2012, Stephen Few, data visualization evangelist and author of the seminal book, Information Dashboard Design, announced a contest to design a dashboard following best practices and principles. The contest required participants to design the dashboard using student performance and assessment data that Stephen provided. Any graphic design tool (e.g., Photoshop, InDesign and Excel) or BI tool could be used to create the dashboard.

The winners were announced in October of that year. There were 91 entries. The contest focused more on innovative dashboard design principles rather than the use of BI tools. The winners and the tool they used are:

1st Place:           Jason Lockwood     Photoshop 2nd Place:          Shamik Sharma      Excel 2010 3rd Place:          Joey Cherdarchuk   Excel 2010

To the best of my knowledge (and Stephen’s), none of the 91 participants in the contest used MicroStrategy to create their dashboard. A few of the participants did use Tableau and SAS. This fact alone made me want to create an innovative dashboard to demonstrate the capabilities of MicroStrategy.

Below are examples of the first, second and third place winners entries.

Jason’s entry (first place)

Jason - First Place

Shamik’s entry (second place)

Shamik - Second Place

Joey’s entry (third place)

Joey - Third Place

Below is a screenshot of my entry developed using MicroStrategy and Stephen’s sample data.

Michael - MicroStrategy Version 2013

As you can see by comparing my dashboard to Jason’s. I tried to follow Jason’s entry very closely since my goal was to reproduce his entry as close as possible using MicroStrategy.

I have emphasized the word “reproduce” because in my goal to prove the capabilities and functionalities of MicroStrategy, I now realize, in retrospect, that I crossed a line in using Jason’s original idea, design and work to create my dashboard. Now, if I was doing this in my basement for my own edification and learning, that probably would have been o.k. since it was not being viewed by a public audience. However, when I entered the dashboard in MicroStrategy’s contest, albeit developed using my own skills in MicroStrategy, I was presenting someone else’s original ideas and design work without their permission. This, I now understand, was wrong.

I have had several e-mail conversations with Professor Alberto Cairo about this. Alberto is considered by many (including me) to be one of the industry’s leading experts on infographics and a person I respect and view as a mentor. I was seeing grey areas in what I had done where Alberto was correctly seeing things more in black and white.

Below are some of Alberto’s thoughts on what I did and some analogies he made. I have included his comments completely in quotes to indicate these are his thoughts and have not been modified by me at all.

“There are not really clear-cut rules about plagiarism in visualization in infographics, which is a shame. It’s an area in which a lot of thinking and writing needs to be done.

But when doing ethical reasoning you can always use analogies. When in doubt, imagine that your graphic is a news article or a research paper. Would it be appropriate if anyone took what you wrote and then just make it interactive without getting permission from the author (you) first? Would it be enough to mention you in a description of what was done? It wouldn’t. Quoting a few lines from someone (in between quotation marks) is fine. Copying and pasting paragraph after paragraph is not, if it’s not without proper permission.

In visualization, things get really tricky sometimes. For instance, if someone creates a simple bar graph based on ten data points, do I need to get permission to create a similar graph? Probably not if a) the graphic form is so common, b) I can have access to the underlying data. But when you copy an entire layout, or an unusual graphic form, then things become problematic. Again, going back to my analogy before, it’d be equal to copying an article, a newspaper story, or a blog post. Even if you mention the source, it’s not something you can do without asking for permission. It would be a clear case of plagiarism, and it could even get you into legal trouble.”

Now, I take full responsibility for what I did and apologize to Jason, Shamik, and Joey. I do need to say, my primary purpose was to create recreate cool dashboards or infographics I had seen, in my tool of choice which is MicroStrategy. The key thing I was trying to do was show clients and business partners that I could create the same thing they see in Tableau and Qlikview using the MicroStrategy platform.

 To continue with Alberto’s thoughts on this, I again include an exact quote of what he said.

“I understand it, but copying the layout, the structure, the content, and even the headline and intro copy (on top of everything else) is not the only issue, but also submitting the results to contests with no permission from the original authors, and without mentioning them.

Again, analogy: Imagine that I take one of the wonderful posts you have written about historical visualizations –some of them are indeed great,– and I reproduce it with no permission from you, but I casually attribute it to you once: “Hey, I’ve just found this great post in Michael’s website; I’m building on top of it, adding some pictures, and making it interactive.” You’d certainly feel uncomfortable if I didn’t contact you first. And you’ll probably get really upset if, besides that, I get a writing award thanks to that post (without mentioning you,) to which I just added a few visual elements, and interaction.”

Alberto is correct. I would be upset too.

2014 MicroStrategy Dashboard Contest

My entry in the 2014 Dashboard Contest was An Exploration of Tax Data. It was based on an original idea, text and design by Jim Uden, one of my classmates in Professor Cairo’s MOOC course on Data Visualization and Infographics.

I really liked the An Exploration of Tax Data visualization created by Jim. I liked it so much in fact, that I wanted to make a working example for our development team at work using MicroStrategy. I create a lot of dashboard “templates” for our development team in MicroStrategy, which is our enterprise standard BI tool.

So, using Jim’s data, text and format exactly, I created a dashboard in MicroStrategy with some tweaks to it.

Below is a screenshot of Jim’s original work.

Jim - Taxation

Below is my version created using MicroStrategy Report Services and their Visualization SDK.

Michael - MicroStrategy Version 2014

I used horizontal stacked bar charts instead so that the viewer can visually see how social security and income tax rate add up to the total and explains visually why the countries are ordered the way they are on the dashboard. I also separated out $100K and $300K percentages into separate visuals.

In addition, I added the flags of the countries.

Now, you don’t see any numbers on the data points in this dashboard. The reason you don’t see them is because they appear when you mouse over a bar where you then see the country, category and the percent value as a tooltip.

However, by using Jim’s data, text and design exactly from his original, and without getting his permission first, I again crossed the line. I have emphasized the word “exactly” because in my goal to prove the capabilities and functionalities of MicroStrategy, I now realize again in retrospect, that I crossed a line in using Jim’s original idea, data, text, design and work to create my dashboard.

I also discussed this with Alberto and his comments were,

“If this were just a class project for the MOOC, you should have asked for permission from Jim Uden, but I don’t consider it a huge ethical problem. After all, when you submitted it to the forums, you mentioned that it was an interactive version of Jim’s project, and you thanked him publicly in your message. You didn’t let him know about this directly, by contacting him (which is, again, the appropriate thing to do,) but you were transparent when you credited “Jim Uden” for the original idea. The true ethical problem arises when you didn’t do the same in your post about the exercise in your blog, and when you submitted it to a contest.”

I again take full responsibility for what I did and apologize to Jim.

I approached Professor Cairo again with another question: What are the ground rules for the use of another person’s materials. For instance, a lot of blogs (including mine) will post an infographic they have seen in a magazine or on a site like visual.ly and discuss it. Are we plagiarizing if we cite the author, magazine, etc.?

Professor Cairo responded,

“As for (this) point, there’s something called “fair use” in US copyright legislation. It’s quite fuzzy and controversial, but it basically says that if you reproduce a piece of art just to comment on it or to review it (not to build on it or to change it, not to get profit from it, etc.), you are fine. Academics and bloggers do this all the time. However, some media organizations are known for having asked bloggers to withdraw images of graphics in the past. They have the right to do so, although I think that it’s a bit silly.”

In Summary

I feel this was an important topic for me to discuss and clear my conscience. I would not be honest if I did not say this was very difficult and embarrassing to write. Professor Cairo reminded me it takes courage to do this. Maybe so, but I don’t feel very courageous at the moment.

Next year, if MicroStrategy has another Dashboard contest, I plan to create the entire thing from scratch. Data, text, design, colors, fonts, etc. It will be from my vision only. However, over the next year, I think this is an important topic to discuss in our data visualization community with social media like Facebook, Twitter, Reddit, Instagram, Tumblr, etc. growing in use every day. At what point have we crossed the line? Or are they blurred lines?

I would love to hear your thoughts on this and opinions. I may not like what I will hear, but I will hear and reflect on what you have to say.

Thank you for reading this very long post. I hope you see the value in it as much as I do.

Best Regards,

Michael

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