• Target for charting excellence

    November 19, 2010  |  Design


    The scales for what qualifies as pretty and useful change depending on the application and purpose, but you always aim for the same quadrant. The best data graphics come from those who are able to find the right balance between aesthetics and utility.

  • Learn the rules of data graphics so you can bend them

    November 15, 2010  |  Design

    If you take away anything from The Visual Display of Quantitative Information, make it the epilogue. This is the most important part:

    Design is choice. The theory of the visual display of quantitative information consists of principles that generate design options and that guide choices among options. The principles should not be applied rigidly or in a peevish spirit; they are not logically or mathematically certain; and it is better to violate any principle than to place graceless or inelegant marks on paper. Most principles of design should be greeted with some skepticism, for word authority can dominate our vision, and we may come to see only through the lenses of word authority rather than with our own eyes.

    When we first start out with data graphics, it is easy to read a list of rules about ratios, flourishes, and sizes, and then trick ourselves into believing that is all there is to it. But like cooking, writing, programming, painting, speaking, designing, sporting and numerous other things, you learn the basics first. The principles. And then you figure out what rules can bend and how far.

  • Graph Design Rule #2: Explain your encodings

    August 26, 2010  |  Design

    Rule #2 - Explain your encodings

    This is part two in a seven-part series on basic rules for graph design. Rule #1 was to check your data. Today we cover rule #2: explain your encodings.

    The design of every graph follows a familiar flow. You get the data, you encode the data with circles, bars, or colors, and then you let others read it. The readers have to decode your encodings at this point. What do those circles, bars, or colors represent?

    William S. Cleveland and Robert McGill have written about encodings in detail. Some encodings work better than others. But it won't matter what you choose if readers don't know what the encodings represent. If they can't decode, the time you spent designing your graphic goes to waste.
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  • Graph Design Rule #1: Check the data

    August 17, 2010  |  Design

    Check the data

    Now that we've covered the 7 basic rules to graph design, it's time to go deeper, starting with the first: check the data.

    I have to admit. Data checking is definitely my least favorite part of graph-making. I mean, when someone, a group, or a service provides you with a bunch of data, it should be up to them to make sure all of their data is legit, goshdarnit. But this is what good graph-makers do. After all, reliable builders don't use shoddy cement for a house's foundation. You don't use shoddy data to build your data graphic.

    Data-checking and verification is one of the most important—if not the most important—part of graph design.
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  • Redesign of the Federal IT Dashboard

    August 2, 2010  |  Design, Statistical Visualization

    About a year after the launch of the Federal IT Dashboard, business intelligence consultancy Juice Analytics focuses on five areas — message, flow, charts, context, and design fundamentals — where the dashboard could use some improvement.

    The first tip on message:

    The information designer is responsible for presenting the data in a way that the message is delivered in a clear and understandable way. If the data is left to speak for itself, users can be left confused or frustrated. And in all likelihood they won't to [sp] see the full value of the data. That's particularly tough for this Federal IT Dashboard where a huge amount of effort has been put into gathering consistent data across agencies.

    Totally agree with this, but to avoid confusion, let's clarify. You should always let the data speak for itself. It's just that what the data says often seems like a foreign language to non-professionals. It's up to you, the information designer, to translate. The better you can translate, the better the information designer you are.

    See the rest of the redesign on Juice Analytics (who is hiring, by the way).

  • Process: Mapping War Logs for the Guardian

    July 28, 2010  |  Design

    This is a guest post by Alastair Dant, interactive lead at the Guardian. He describes the efforts that went into designing the recently published war logs map of incidents revealed by Wikileaks.

    Our site editor approached me with a serious challenge: could I visualize six years worth of military reports? Up in their makeshift war room, our team introduced me to Julian Assange. While reporters from the New York Times and Der Speigel took photos and video, the director of Wikileaks booted his encrypted netbook and showed me a page from the war logs. I may have looked a little distressed. The gravity of this material was stark and, having never dealt with such documents before, I was uncertain if I wanted to start.

    After several days feeling like I'd walked into the Bourne trilogy, David Leigh and Rob Evans put my mind at rest. We wouldn't be publishing any material that might put anyone at risk and my work could focus on charting the rise in explosive devices from 2004 – 2009.
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  • 7 Basic Rules for Making Charts and Graphs

    July 22, 2010  |  Design

    Basic rules for making graphs and charts

    Charts and graphs have found their way into news, presentations, and comics, with users from art to design to statistics. The design principles for these data graphics will vary depending on what you're using it for. Making something for a presentation? You'll want to keep it extremely simple and avoid using a lot of text. Designing a graphic for a newspaper? You'll have to deal with size constraints and try to explain the important parts of your graphic.

    However, whatever you're making your charts and graphs for, whether it be for a report, an infographic online, or a piece of data art, there are a few basic rules that you should follow.

    There's wiggle room with all of them, and you should think of what follows as more of a framework than a hard set of rules, but this is a good place to start for those just getting into data graphics.
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  • Why context is as important as the data itself

    May 21, 2010  |  Design, Statistics

    John Allen Paulos, a math professor at Temple University, explains, in the New York Times, the importance of the before and after of when you get that data blobby thing in your hands.

    The problem isn’t with statistical tests themselves but with what we do before and after we run them. First, we count if we can, but counting depends a great deal on previous assumptions about categorization. Consider, for example, the number of homeless people in Philadelphia, or the number of battered women in Atlanta, or the number of suicides in Denver. Is someone homeless if he’s unemployed and living with his brother’s family temporarily? Do we require that a women self-identify as battered to count her as such? If a person starts drinking day in and day out after a cancer diagnosis and dies from acute cirrhosis, did he kill himself?

    In a nutshell, statistics is a game of estimation. More often than not, the numbers in front of you aren't an exact count. They could easily change if you shift the criteria of what was counted. As a result, there's always some amount of uncertainty attached to your data, and it's the statistician, analyst, and data scientist's job to minimize that uncertainty.

    So the next time you see a list of rankings like "fattest city" or "dumbest town," don't take it for absolute truth. Instead, think of it as an educated guess. Similarly, when you analyze and visualize, remember the context of your data.

    Catch Paulos' full article here.

  • The path to successful infographics

    May 11, 2010  |  Design, Infographics

    Most people don't know what actually goes into a good infographic. There's a lot more to it than just the design. There's research, analysis, and fact-checking that you have to do long before you open Illustrator. Sarah Slobin, from the Wall Street Journal, explains how to create successful infographics. Have an idea, get the data, choose your tools, edit wisely, and above all else, pay close attention to detail.

  • Major wood pallet fires?

    May 4, 2010  |  Design, Mapping


    I put this up only because I had no idea wood pallet risks were such a hot topic. No pun intended.

    Of course, if you compare number of pallet fires to number of residential fires, the above almost seems like nothing. There were 20 major pallet fires between 2008 and 2010. There were 403,000 residential structure fires, causing an estimated $8.6 billion in damage - in 2008 alone.

    While I'm sure the pallet fires caused plenty of problems, it's always good to put things in perspective.

    Update: As Douglas points out, the site reeks of plastic pallet propaganda. Another case of forcing an issue by exaggerating the numbers. Tsk.

    [Thanks, John]

  • Visualizing data: ask a question first

    April 29, 2010  |  Design, Quotes

    There is no way to think up an original and extraordinary design—it can only come as a result of pursuing a given task. In the same way running down a list of words is different from making a narrative.

    — Artemy Lebedev, Designer’s block, February 16, 2010

    This applies to visualization too. When you don't have a question to answer or a simple wonderment about something, you end up staring at a bunch of numbers with no clue what to do with them. Want to test this out? Go to data.gov and make something useful.

    [via @Coudal]

  • perception

    Graphical perception – learn the fundamentals first

    Before you dive into the advanced stuff - like just about everything in your life - you have to learn the fundamentals before you know when you can break the rules.
  • Think like a statistician – without the math

    March 4, 2010  |  Design, Statistics

    Think like a Statistican

    I call myself a statistician, because, well, I'm a statistics graduate student. However, ask me specific questions about hypothesis tests or required sampling size, and my answer probably won't be very good.

    The other day I was trying to think of the last time I did an actual hypothesis test or formal analysis. I couldn't remember. I actually had to dig up old course listings to figure out when it was. It was four years ago during my first year of graduate school. I did well in those courses, and I'm confident I could do that stuff with a quick refresher, but it's a no go off the cuff. It's just not something I do regularly.

    Instead, the most important things I've learned are less formal, but have proven extremely useful when working/playing with data. Here they are in no particular order.
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  • 11 Ways to Visualize Changes Over Time – A Guide

    January 7, 2010  |  Design

    Deal with data? No doubt you've come across the time-based variety. The visualization you use to explore and display that data changes depending on what you're after and data types. Maybe you're looking for increases and decreases, or maybe seasonal patterns.

    This is a guide to help you figure out what type of visualization to use to see that stuff.
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  • 9 Ways to Visualize Proportions – A Guide

    November 25, 2009  |  Design

    With all the visualization options out there, it can be hard to figure out what graph or chart suits your data best. This is a guide to make your decision easier for one particular type of data: proportions.

    Maybe you want to show poll results or the types of crime over time, or maybe you're interested in a single percentage. Here's how you can show it.
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  • Chart Junk vs. Eye Candy: What’s the Difference?

    September 25, 2009  |  Design

    There's this one phrase that really bothers me when it comes to data graphics. No doubt you've heard it or read it, and maybe it even popped into your head once or twice.

    The phrase I'm talking about is: "Edward Tufte is crying."

    People like to say this when they see a graphic that doesn't fit the ET law of high data/ink ratio. Then after the commenter has declared that ET is in fact a very emotional man, the graphic is classified "chart junk."

    First off, I'm pretty sure ET isn't that melodramatic. He doesn't cry over a bad graph nor does he die a little inside or roll over in his grave if he were dead. I don't think an angel get its wings every time he rings a bell either. Although I could be wrong about the latter.

    Second, not everything that fails to fit the mold of a traditional graph, visualization, or whatever you want to call it, is chart junk. One person's chart junk is another person's eye candy. What you see just depends on what angle you're looking at it from.
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  • What Visualization Tool/Software Should You Use? – Getting Started

    September 3, 2009  |  Design

    toolAre you looking to get into data visualization, but don't quite know where to begin?

    With all of the available tools to help you visualize data, it can be confusing where to start. The good news is, well, that there are a lot of (free) available tools out there to help you get started. It's just a matter of deciding which one suits you best. This is a guide to help you figure that out.
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  • 6 Easy Steps to Make Your Graph (Really) Ugly

    June 15, 2009  |  Design

    We spend so much time trying to make our graphs accurate, simple, understandable, etc that we forget the lost art of making graphs that are inaccurate, unreadable, make absolutely no sense, and make your eyes want to vomit. I'm so tired of understanding data. I want to experience it, and I know you want to also.

    So this one's for you, crappy graph.
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  • Rise of the Data Scientist

    June 4, 2009  |  Design, Statistics

    Photo by majamarko

    As we've all read by now, Google's chief economist Hal Varian commented in January that the next sexy job in the next 10 years would be statisticians. Obviously, I whole-heartedly agree. Heck, I'd go a step further and say they're sexy now - mentally and physically.

    However, if you went on to read the rest of Varian's interview, you'd know that by statisticians, he actually meant it as a general title for someone who is able to extract information from large datasets and then present something of use to non-data experts.
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  • Data Visualization is Only Part of the Answer to Big Data

    March 20, 2009  |  Design, Exploratory Data Analysis

    How can we now cope with a large amount of data and still do a thorough job of analysis so that we don't miss the Nobel Prize?

    — Bill Cleveland, Getting Past the Pie Chart, SEED Magazine, 2.18.2009

    For the past year, I've been slowly drifting off my statistical roots - more interested in design and aesthetics than in whether or not a particular graphic works or the more numeric tools at my disposal. I've always had more fun experimenting on a bunch different things rather than really knuckling down on a particular problem. This works for a lot of things - like online musings - but you miss a lot of the important technical points in the process, so I've been (slowly) working my way back to the analytical side of the river.
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