Michelle Minkoff » tufte

Piece de resistance: Data viz wrapup

Michelle Minkoff — Mon, 08 Mar 2010 06:27:00 +0000

And, scene. That’s a term borrowed from theater, it’s used as an act or scene closes. My giant Chicago art gallery persistence project is completed. We’ve got a trend article that uses CAR techniques, and a Flash visualization and a searchable database. The main thrust to the story is that of the galleries that existed in Chicago in 1990, about half have survived. What’s enabled them to survive, though, has varied. And while I can give you the overall picture, maybe you, as someone interested in the topic, care more about a certain medium or gallery or part of the city. You must be able to find the part of the story that matters to you. This is the non-linear storytelling first mentioned in the beginning weeks of Medill. But for me, it is no longer just an idea, it is a reality.

Visualizing through graphics

The Flash component allows you to search by art medium accepted, or by neighborhood. The neighborhoods are determined by groups of zip codes, and the ten art medium categories are made of various combinations of the more than 60 categories itemized in the Chicago Artists’ Coalition’s list of galleries. One important thing I’ve learned about data reporting is that you have to use journalistic discretion to pare down the options. 60-something categories of media can be extremely overwhelming. If you fail to use discretion when creating a project, you’ve failed the user. I used Tuftian principles for the graph itself, keeping it as simple as possible. I liked using the idea of duplicates to make up the units of a bar graph. I went with houses to symbolize the gallery buildings. My first instinct was to use paintbrushes, or paint palletes. But that doesn’t work when you are sorting for the decline of these galleries that sold wearable art. The Monopoly-style house was the best generic choice. I went with the blue accent color since it was calm and unobtrusive, but more visually interesting than a solid black and white graph. I’ve been doing a lot of reading about the power of an accent color, and sticking mostly with the neutral to pack a punch. Other tweaks I made as I went along included adjusting the size of the ticks on the y axis. I found myself thinking more about interface design than usual, when you have a certain filter on, the background color on that button should be different to indicate what variables you are looking at.

And I learned something about myself. When I set out, this type of piece is what I saw as the definition of a visualization. Now, I see data-driven applications, especially those allowing for you to dig deep using the power of frameworks, as a form of visualization. One isn’t necessarily better than the other, in fact, I think they play off of each other. But I’m not a visual person. It’s fun to play with, but I know designers who could have packed more of a visual punch with this piece. But by keeping it simple, I’m hoping it works.

Searchable databases are visualizations, too

You can read most of my thoughts on how this came out here, and I’ve made a few small tweaks since then. Most notable is dealing with the list of media accepted on the detail page. I was trying to form a sentence with the media that were accepted, but was having trouble figuring out how to display “and” before the second-to-last template tag that was actually displayed. I solved the problem by turning the accepted media list into a list of bullet points. I think it reads better this way, too. I like the idea of being able to get an overview of sorting the data through the Flash visualization, and then digging as deep as you want with the searchable database.

Not providing current gallery information

Going back to the Flash visualization, throughout the quarter, my independent study adviser Rich Gordon and I debated the merit of putting the number of current galleries into the visualization. I eventually opted not to. That’s because this whole project is about persistence of art galleries — what happened to the 96 that existed in 1990? Introducing the number of current galleries is a completely different data set. I tried making it another node on the graph, but it made everything else seem tiny. Even listing the number of current galleries just seemed out of place. I believe the lesson here is to stick to your data set. Comparing the galleries that persisted to total number of galleries is a valid project, but a different project — that was my ultimate conclusion.

Flash as a viz tool

Some other challenges I encountered included proper alignment of the various house icons (thanks to Flash’s grid feature for help on that). Knowing when to use the right tool was an interesting battle. At first, I was trying to get Flash to create the detailed views that Django is ultimately better suited for. By combining Flash and Django, I think I came up with a significantly better combination. Even if someone doesn’t explicitly go into design, I would heartily recommend they read Tufte and try experiments in Processing, Flash and Django. It’ll all help in the long run.

Looking toward the future

There was a time, not that many months ago, when I prided myself on the fact that I knew enough about programming to communicate with coders. Now, I just do the programming myself — or at least, as much as I can. I’ve determined that visual design isn’t my forte, but I understand enough about it that I’ll be able to integrate smoothly with an art director or Web designer. I even understand enough about Flash that I’ll be able to collaborate closely with front-end interactive designers. But I wouldn’t know any of this if I hadn’t tried a lot of new things. I’ve applied my journalistic curiosity, that I typically use for subject matter, to new ways to tell stories, and tools with which I can bring those stories to life. It is for that adventure that I embarked on this journey. As I said after my Washington quarter, I’ve learned a lot, and I’ve also learned just how little I know. It will be an ongoing quest. But how fun and educational it’s been to ramp up my skills in these past ten weeks.

The power of community

As I’ve been pursuing the independent study, one of my other goals has been to blog regularly. By that, I mean more often than once a month, which has been my typical pattern. And through the excitement I’ve gotten from my exploration of programming journalism and visualizations, I’ve been able to do that. I’ve promised myself it won’t end at this point, there’s so much more to explore.

But if you’re reading this, I want to thank you. Your comments on the site, via email, or on Twitter have enhanced my learning process. You may not be receiving tuition money, but whether you helped me troubleshoot my Django app, discussed the intracies of Flash, pointed me to a great GitHub tutorial or just chatted with me about where I might fit in the grand scheme of things, I couldn’t have done it without you. The greatest asset of the CAR community, as I see it, is the community itself. I look forward to keeping in touch for years to come.

It’s for that reason that I couldn’t be more excited about NICAR later this week. (Less than 72 hours till I leave!) I can’t wait to meet you all in person, to hear even more inspiring ideas that I can bring to my first “real job” (not sure where that is yet, but I’ll keep you posted). Heck, I’m even giving a five-minute lightning talk on some cool tricks I’ve been playing with in Javascript. It’s a beautiful thing when an almost-graduated student’s knowledge is valued that much. But from what I’ve seen, I shouldn’t be surprised. It’s not about a judgment of age or experience, it’s about sharing something with each other. That’s what makes NICAR, this quarter and journalism in general so special. As it says on my high school class ring, knowledge is power. I’m ecstatic that we all will continue that journey together for years to come.

Visual confections are more than mere presentation

Michelle Minkoff — Mon, 15 Feb 2010 18:35:39 +0000

Data visualization. It’s one of those terms that can mean so many things. I say I’m doing an independent study on data visualization this quarter. That’s true. But a better description would be “data visualizations for journalism.” I’ve talked about this with people before, a lot of pieces are gorgeous, and they do convey information, but they don’t tell a story that informs the viewer in a useful way. Sure, the colors used in Flickr photos is data, and if you represent it visually, that is a visualization. But that doesn’t make it appropriate for a news Web site. It’s the same reason simple transcription doesn’t make up all of our news — and if it ever does, then we really need to save journalism. Whether through text or words, we use journalism to tell a story.

It’s something I’ve been wrestling with all quarter. It’s the reason I have such a problem with uncontextualized data centers as the primary data initiative in news organizations. Which is why I found myself saying, “Yes, yes, yes!” to “Visual Confections,” the final chapter I read this week in Edward Tufte’s Visual Explanations.

I love the word confection, and the images it draws to mind. One definition is of a food rich in sugar. It’s sweet, pleasurable to the senses. I always think of confections as more carefully crafted sweets, something I would get in the family-owned fudge shop by my middle school as opposed to a Hershey bar I might see in the checkout line. And our journalistic creations should be confections in this sense, they must delight as well as inform. But a second definition of confection, according to Princeton’s WordNet, is concotion, “the act of creating something by compounding or mixing a variety of components.”

And that’s where it gets interesting. Journalists have the capability to aggregate, analyze and redisplay information. Even if it already exists on the Web, and the user can access it, we provide the context. This speaks to me as various professors and editors have referred to some of my more adventurous news projects as “Web concoctions.”

It’s the only chapter in the book where I can recall an example from journalism being used. The idea of visual confections is that while many visualizations are just representing a data set, you can use a visualization to tell a story, combining different sources and using bits of text — or “chatter” to inform the viewer.

Graphs may approach this when placed next to a story, or if ample text is included with the graph, or if further explanation appears on rollover or click. And perhaps in exceptional circumstances, not one word of text will be needed, but I think that’s extremely difficult. It should be reasonably quick for viewers to understand the point of the image or interactive experience, and in most cases, some words will be needed to help facilitate that.

I admired the example the book pulled that accompanied a June 1985 article in the Washington Post, warning viewers about the dangers of the fast-moving Potomac river. What could have been presented as a boring list of safety tips becomes almost cartoonish in its illustration. It doesn’t seem silly, but rather, makes the illustration more personable. The various illustrations within the larger piece range from showing the structure of the waterways, to how you should arrange your body to try to survive the current. Little blocks of illustration and chatter use Tufte’s principle of several small multiples being more clear than an illustration trying to do everything at once.

Treating a visualization as a confection and creative opportunity does more for grabbing a viewer’s attention, as well. And it demands more creativity from the creator, which makes it more fun to design, I think. That enjoyment is passed on to the user, the information sticks in the user’s head. With that principle in mind, I can now see that a Django-based application, or any data-driven app, is a data visualization by some other name.

Visualizations are much more than how to make that Excel graph easier to understand and less boring — they tell a story that grabs the reader and helps him or her comprehend a diverse set of information. It’s an attitude that we can bring to journalism of all sorts to take the profession away from stenography and into analysis.

Journalists are a lot like college professors, we must spend the time doing research to discover new things. But we must also make sure we can convey that diverse set of information to people of all skills and interest levels, just as professors must communicate with everyone from freshmen in their intro class with vague interests to advanced graduate students working on a dissertation who demand very specific information.

Repetition, repetition: The power of multiples

Michelle Minkoff — Tue, 09 Feb 2010 17:11:08 +0000

Teachers are true heroes in today’s society, I owe so much to almost every one that I’ve ever had the privilege of working with. As the daughter of an elementary school teacher, and from teaching some classes on my own, I know that repetition is a key part of passing knowledge from your brain to my brain. And if you think about it, so much of what we do as journalists is a form of teaching, as we inform the public. That’s why repetition can be a very valuable technique in data visualization, and Tufte devoted a chapter to it in this week’s reading, titled “Multiples in Space and Time.”

We see it used often in graphics, by juxtaposing the same object against other versions of the same object, we’re better able to compare and focus on the distinctions. If we’re trying to make a point with a graphic or an article, repetition helps to make sure it’s something the user is thinking about.

It’s a strategy used in this graph featured in Junk Charts, looking at the participation of people on Twitter in terms of percent. By keeping the unit used in a visualization consistent, you are keeping that variable constant. But make that icon interesting enough, and people will care a little bit more than just looking at the height of a bar graph. Tufte says don’t frame the different instances, but let the objects speak for themselves. Keep it simple.

It’s all about simplicity, and the balance between aesthetically pleasing the user and ensuring an accurate display of information. And if only one is possible, the latter must take priority.

I’m definitely considering using this as a strategy in the visualization I’m doing for my final project. It’s still in the beginning stages, since I’ve been creating my own database from scratch.

And on a side note, I just took this career test where you pick colors and it tells you what you are best suited for. It says I am an “organizer” and I am best suited — wait for it — to work with “data systems.” Weird.

Parallelism: Packing information into visualization

Michelle Minkoff — Sat, 30 Jan 2010 19:00:17 +0000

Information is fascinating at many different levels. Show me a simple graph of the components that make up a whole, that tells me something. I’ve found almost anything is more interesting when looked at across time, since it adds another dimension. This also helps with analysis, because outliers or rapid changes are often related to historical events. Compare a few data sets across time, and you have more perspective, and even more information.

The ebb and flow of the use of trains in border crossings is interesting on its own, but by comparing it to the ebb and flow of personal vehicles used in border crossings, you learn more about both data sets.

Which brings me to the value of parallelism, Tufte’s topic in this week’s reading, “Parallelism: Repetition and Change, Comparison and Surprise” from “Visual Explanations.” He explains how valuable it is to look at data sets at once, in one graph, than have several different graphs across time that each stand on their own. Of course, it is possible to clutter up a visualization with too much information, so one must be careful.

Tufte also focuses on the capabilities of computers in allowing a user to navigate through complex data to the part they want to focus on, permitting them to repeatedly listen to and analyze a certain part of a musical composition, for example. This relates to the power of interactive tools in data viz, and generally journalistic content. Break it down into digestable chunks, let people step in or out of the content as much or little as they want. The details are there, but you don’t have to absorb all of it to get the point. Or go ahead and absorb as much as you want. Have fun exploring one chunk very deeply, because you want to get its nuance. It’s up to the user to make his or her own personalized experience.

Because of these benefits of parallelism, the concept has been used to help people understand information in a literary sense for years — I still remember learning about this in high school, maybe middle school. And a graph showing multiple sets at once is extremely popular, and one of my favorite types of visualizations. Color one line different from the others, and you’re making a point of analysis. Keep it objective, and you’re opening analysis up to the user. Versatile, and ripe for as much depth, or lack of depth, as a user wants.

The first time I was introduced to parallelism was with the Name Voyager, looking at trends in baby name popularity. The concept has showed up in some of the New York Times’ visualizations, such as the 2008 box office receipts graph. And even as I write this, I ran across this tweet from @MacDivaONA (Chrys Wu) pointing me to a graph using parallelism to relate the years of time travel in various movies. I spent 20 minutes looking at it, and I know I still haven’t grasped half the nuances. That’s the point. It is, as Wu writes, a “datavizgasm.”

Now, that last piece may not be telling a news story, per se, but I would argue that journalism means conveying information, and if it captivates someone’s interest, it does tell a story. What’s the earliest year that has been time traveled to in a movie? The latest? Do paths of time traveling characters ever intersect? (When it comes to asking if something’s journalism, I would come down with data guru extraordinaire Adrian Holovaty. When asked the same question re: publishing databases online, such as his Everyblock, Holovaty said, “Who cares?”)

For this last graph, the makers have cataloged their creation process (thanks!) at Information is Beautiful. It’s informative to see the iterations the graph goes through. Parallelism as a graph form does pack a punch. But to do it right, it can’t be too confusing, and there must be a strong Data-Ink ratio, to use a Tufte term. All the lines drawn on the page, or as many as possible, should have to do with data. Creating content concisely is important whether you’re writing, editing, designing or coding. And that sentence, right there, is a parallel structure.

Personal reflection: Tufte’s messing with my head

Michelle Minkoff — Tue, 26 Jan 2010 03:32:46 +0000

My eyes have been opened by the readings I’ve been doing from Tufte. While I only assigned myself “Visual Explanations,” I’m beginning to feel as if I want to soak up as much of his work as possible.

So, I went to my advisor’s office (Rich Gordon), where the walls are lined with what feels like an infinite number of CAR/data/viz/new media books, and procured Tufte’s other three works. I’ve been perusing them — read “The Visual Display of Quantitative Information” from cover to cover, and have only skimmed the other two so far.

As I’m working through the books, I’ve been thinking about the similarity between that material and principles of visual art I’ve been reading up on for my beat in Arts Journalism class. The comments about colors, spacing, conveying the most information in a little space, not including lines that distract from the main point, it applies to the message of visual art as well.

I have a hard time summarizing all that Tufte covers, I truly believe you have to read it (and see the examples) to get it, but as always, the more I read, the more I realize I don’t understand.

But I do see myself looking at ads on the El, signs on the front of buildings, and hearing examples from Tufte in my head.

Example #52681 of how I’m slowly going insane…or becoming more aware of the field, and thus more aware of my world, take your pick.

Keep it subtle, stupid: Differentiating data values in visualizations

Michelle Minkoff — Mon, 25 Jan 2010 18:44:07 +0000

I’m not anything resembling a visual genius, but I like to believe I have enough of an eye that I can tell what works and what doesn’t. The reason Tufte’s books are so helpful is that they tell me why something works, and give me rules so I know what I can and can’t do. That’s why I have great appreciation for the Tufte chapter of the week: “The Smallest Effective Difference” in my ongoing exploration of “Visual Explanations.” (I’m also reading his other books on the side, and more on that in the coming week.)

The chapter at hand was surprisingly short — six pages — but for the subject matter, it worked. The “smallest effective distance” refers to how you differentiate between different levels of data in intensity-based charts, or different variables. The point is, either way, if you make things too distinct, it can be jarring to the eye, so it’s important to keep colors within the same general theme. At the same time, pick colors that are too close together, and you risk losing the person looking at your information — they should get it at a glance.

It’s true – the most jarringly rainbow visualizations I can think of are the USA Today weather map and this other visualization. They both use rainbow color schemes, but the intensity of the colors are all equally bright, and the values of similar color are all in the same area of the visualization. Tufte demonstrates that the problem is when a patch of bright red is surrounded by bright yellow, bright pink, and bright green — that’s just jarring.

The latter part I understood before, but hadn’t really thought about the former. It’s a valuable point, one we use in writing as well. Don’t jump all over the place, stay on topic, keep a focus to your piece. This is just a question of keeping a visual focus, and making distinctions easy for the eye to grasp. It’s a simple but powerful lesson.

Committing fact errors in visualizations

Michelle Minkoff — Tue, 19 Jan 2010 05:04:27 +0000

At Medill, there’s a wonderful tradition called the “Medill F.” Make a factual error of any sort, and you fail the assignment. The sadistic part of me likes it — a journalist’s job is to tell the truth. If you miss the mark, you’ve failed the public, and failed at your job for the day. Harsh but true. My only Medill F was writing down the wrong day for an exhibit closing. I’ll never forget that one.

Why do you care, you ask? Because it’s an example of just how simple it is to make a factual error. That’s something I’ve been thinking about a lot recently.

Sure, it’s bad, immoral even, to make up sources or quotes, say you saw something you didn’t. The sin of omission, leaving out certain parts of a story, is a facutal error in its own right. I knew that. But in one chapter of Edward Tufte’s “Visual Explanation,” titled “Explaining Magic: Pictorial Instructions and Disinformation Design,” we learn about the factual error of miscommunicating information.

I think the magic analogy is a strong one, because images and graphics are so powerful, it’s all too easy to use a sleight of hand and manipulate the information. On the one hand, if you include everything, it becomes cluttered and it’s not easy for the user to discern what’s important. Also, if you’re just dumping data into a visualization, you’re not really helping anyone, and I would argue that’s not journalism. Dumping data into a visualization is different than the wonderful work being done with news apps like Politifact and Everyblock — even as they use lots of data, they are making sure to contextualize it. That’s what makes the idea of data-driven apps so appealing to me — I believe they are a very important part of where the future of journalism lies.

But from a journalistic standpoint, if you focus on certain pieces of information, you are making a sort of editorial judgment. So, the lesson I take from this chapter is that it’s something I need to be careful of. Don’t crowd out the important material to diminish its impact (Tufte’s example of this is a thick border around Surgeon General warnings on cigarette packages.) Just as it’s an editorial judgment when you decide what story to report on, and where that story gets placed in your new product, similar judgments come into play with data viz. And those are certainly not decisions to be taken lightly.

Importance of combining data analysis with context (reflections on readings from week two)

Michelle Minkoff — Tue, 12 Jan 2010 04:03:40 +0000

“Visual and Statistical Thinking: Displays of Evidence for Making Decisions,” Edward Tufte, Visual Explanations

This chapter gave practical examples of something I’ve been saying from almost the first day of my data analysis journey — that it’s absolutely fundamental that the decisions behind the analysis are shown to the reader/user. Data’s never perfect, and if you think it is, that’s probably the most likely clue that something’s wrong.

That means it’s essential to tell your audience what you’re concerned about, what you were unable to verify for certain, and how you got the answer you got. Tufte gives examples of this being necessary in visualizations form nearly a century ago, but I would argue that it’s more important than ever now.

As more news organizations are posting databases online, so users can dig deeper themselves (a move supporting openness and transparency that I am absolutely behind, by the way), we must give readers all the information so they can approach the data with a similar methodology as we did. Providing context is a major point of Tufte’s, and especially important for journalists. One of our strengths is knowledge of the beat, and being able to take a dataset and show how it’s relevant to the ins and outs of that particular industry: education, finance, etc. If we solely provide data, or solely write stories based on observations, either way, we’re only fighting half the battle. Put beat reporting and data analysis together though, and present it in an easily-understandable way, and then we can really pack a punch.

“Getting Started with Processing” and “Mapping,” Ben Fry, Visualizing Data

In this portion of the book, we learn the basic structure of Processing. I was surprised at how similar it was to Actionscript, in terms of breaking down a program to its component parts. There are certain types of commands, or functions, that you perform once as part of setup, and others that you want to happen repeatedly.

It was very exciting to create this map, where I was able to put the structure into practice. The big difference between using a programming language like this to create a map, and depending on a service like Google, is the flexibility that it allows you to have. The issue with Google Maps, at least the way I’ve been doing them, is that they don’t really look like my own. Sure, you can customize the placemarks, and the info contained when you click on each object, but it’s always going to be a Google Map.

What I loved about working through the map exercise in this book is that I could explore different types of visualization. I saw that expressing a value by interpolating a color between red and blue doesn’t really work, because the eye doesn’t necessarily register purple as being on a certain scale in between red and blue.

The size of dots in the center of a state seems to work well, although that’s a better methodology for marking a specific point, not the polygon of an entire state. When looking at unemployment rates across a state, it would have been better to color the whole state, making it light blue for smaller values, and deep blue for larger values. But it was easiest for me to think about these issues when playing with them in code myself, and I have a newfound respect for the work designers do.

Pick the wrong type of visualization, and you’ve lost communication with the user. And if you’ve lost communication with the user, it doesn’t matter how great your story is. No one will know it exists if it isn’t shared effectively with the world. It’s essential that the user understand each data point in context with all the others in the set, because without that element, it’s just a set of jumbled numbers that isn’t conveying real information.

Reflections on Visualization Theory (Data viz readings, week 1)

Michelle Minkoff — Mon, 04 Jan 2010 23:32:33 +0000

In this first set of reading, I learn that the principles of simplicity, accuracy and more are as true in data visualization as they are in a text story.

Edward Tufte, Visual Explanations, “Images and Quantities”

In Tufte’s first paragraph of Visual Explanations, he discusses the importance of readability — a concept I see as having a strong parallel to the usability so often discussed in a more modern era. He breaks down it down into three types of depicting quantities: direct labels, encodings (scales of color) and self-representing scales. I like his breakdown, as I’ve seen examples of all of theses, whether on news sites, or t0 illustrate points in scientific journals when I was doing medical reporting. I attempted to do an encoding on my recent border crossing graph, but was unhappy that using color intensity to express data made everything so light that it was difficult to distinguish between colors. I would add the caution that encodings are best used when the entire graph is just representing various intensities of that one variable.

Tufte also writes that maps are just another type of graph. Statistical graphics are those that don’t just give the data, but spatially arrange the data on a straight one-dimensional line. No comment here, except to say that so far everything makes logical sense. And the main takeaway seems to be a maxim true in written journalism as well, “Keep it simple, stupid.”

At the same time, we should make sure to include all the facts and necessary details, which means including labels when they are not self-explanatory. Tufte critiques a computer visualization for looking pretty, but not putting its data into the proper context. Another maxim: Content is king. Without interesting information, the coolest icons, colors and animations do nothing. It’s nice to hear that it’s a professional point, but before I would have just clicked off of something I didn’t understand. And in the news business, or any business, that’s not something we want.

Ben Fry, Visualizing Data, “The Seven Stages of Visualizing Data”

Here’s Fry’s list:

Acquire
Parse
Filter
Mine
Represent
Refine
Interact

All of these could probably be applied to writing as well. I think it’s interesting that the first six of these elements are equivalent to reporting, and figuring out which quotes to use. It strikes me that in both text and graphic work the actual creation is the end-step, but if you’ve done the previous work well, the piece should just write (or draw, or code) itself. Fascinating.

One part that’s different about interactive data visualizations is the user’s ability to manipulate. This is a major focus of Fry’s introductory chapter. Instead of guiding them through a story by taking them where you want them to go (they read your words in your order), here it’s your job to set up so they can make their own discoveries. Creating visualizations for the Internet is all about putting the power back in the hands of the user. Sometimes with text, I feel it’s a bit too easy to become a bit power-happy, after all, you’re telling the reader what they need to know. Part of what I love about interactivity is that it’s democracy at its finest. User, here’s the data so you can understand it, but there’s a nearly infinite number of ways you can explore it. Have it, and revel in the true sense of democracy. That’s not withstanding the issue of whether the Internet is really democratic because not everyone has access, but that’s a topic for another time. Letting users drive our stories to a greater extent brings us back to why journalism was started, and its relevance goes beyond interactive visualizations, and provides a lesson I take to heart as a journalist, no matter what platform I’m using.

Michelle Minkoff » tufte

Piece de resistance: Data viz wrapup

Visualizing through graphics

Searchable databases are visualizations, too

Not providing current gallery information

Flash as a viz tool

Looking toward the future

The power of community

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