Our contest is over, check out the talent!

Our Best City in the World Contest has come to an end! We’re so excited to start compiling all the submissions and seeing what people came up with. (Backgrounder: in this contest, the Economist Intelligence Unit challenged the world to devise and visualize new ways to rank cities and measure urban liveability.)

You can check out — and in the case of interactive submissions, play with — many of the contest entries which have now been made public on our Best City Contest Topic page (which aggregates all the submissions). 

Some screenshots of the most recent submissions (picked for no specific reason):

Very impressive — great job, everyone!

The Atlantic picks up our Best City Contest

The Atlantic Cities just posted an interview with one of the judges of our Best City in the World contest, EIU Cost of Living editor Jon Copestake:

Another great related article to read on the subject, also in The Atlantic Cities, is “Why Ranking Cities is Such a Tricky Business,” by Toronto-based Globe and Mail writer John Lorinc, who lays out the pitfalls and oversights that can occur with international city ranking press coverage. 

Would love to hear people’s thoughts on the EIU’s new open-market, open-data approach to encouraging new methods for measuring liveability, in light of Lorinc’s piece. With data enthusiasts already dabbling with different techniques on our real-time Best City Contest topic page, things are already getting interesting. 

Want to throw your hat in the ring? Watch our submission guide video, and check out the contest details

-Momoko Price (momoko@buzzdata.com)

How to make a living off data visualization

Last month I presented BuzzData to Hacks/Hackers Ottawa, a gathering of enthusiastic journalists and developers bent on learning the best ways to tell stories digitally. What with BuzzData’s socially-enabled data-publishing features, our product was well-received by the audience. I had a great time. 

While there, I met Amanda Shendruk, a 26-year-old journalist-entrepreneur who did a presentation on information visualization. Shendruk’s insights were thoughtful and well-researched; the work she displayed was startlingly good, considering her academic background was in Spanish and politics, not graphics design or IT, as I expected. 

Data-viz entrepreneur Amanda Shendruk’s info-viz portrayal of Canadian student vote mobs. 

Shendruk recently launched her own data-viz business, Aesthetic Intelligence, which “explores and creates fresh ways of presenting information by combining visual design and data.” While her student peers scramble for footing in the entry-level job market, Shendruk is planning to make a living off her data-viz savvy, and she’s already off to a good start.

Shendruk’s commitment to visual storytelling, as well as her early success at monetizing her skills, is an inspiration to any journalist who has ever been daunted by the steep technical learning curve of wrangling and visualizing data.

Here, Shendruk explains how she jumped into the info-viz game: 

You don’t come from a graphics design or data background, which is a bit surprising.

No I don’t. I have one degree, an undergraduate degree in Spanish. I’m currently working on a second undergraduate degree in politics and communications. 

But my dad is a graphics designer, so I think I just absorbed a lot by being around him. He would always engage me; even as a kid he would show me his work and ask me to tell him what I thought of it, or or he would show me some kind of design from somewhere and ask me if I thought it was good design or bad design. 

Ever since I was young, we just talked about design. I probably learned a lot there. Probably more than I think. 

Do you ever get frustrated with how conventional journalism is done? 

Yeah. Part of what I’m interested in is the most effective way to display, organize and disseminate information. I don’t think that everything should always be turned into a graphic. I don’t think everything needs to be an information graphic, or that everything needs to be visualized. 

I don’t think there’s enough acceptance or maybe understanding right now that there are various mediums through which to present a story. A graphic isn’t always the best medium, but a narrative isn’t always the best medium.

But more than that, I do think there needs to be a better understanding of numbers and statistics in general, and then I think that it will be easier for people to present them, too. Even narratively. 

Shendruk re-thinks Canada’s food pyramid (which perhaps shouldn’t be a pyramid at all)

Where do you look to stay on top of your craft?

 I read academic papers about cognitive science and perception. I’m interested in (things like) “How do we perceive one colour over another?” “How does our memory work?” “How many concepts can we really retain in our short-term memory?”  So (for example, you shouldn’t) make your visualizations overly complicated because we can’t actually hold all that information in our minds, anyway.

I still have a lot of reading and a lot of exploring to do in the area, but I like to base what I do on science, on actual studies. 

That being said, some of it is just gut: “This is what I think is good design, and it’s what I think is clear, so that’s what I’ll put out there.”

Is there any cognitive “rule of thumb” of the human brain that really surprised you?

A really interesting study that I just recently read, from the University of Saskatchewan, was about chart junk

They had some people look at embellished charts and then just the graph versions, then they compared what people got out of the charts. When they tested the same people weeks later, the memory retention for the people who had looked at the charts with drawings on them, or “chart junk” — I don’t like to call it chart junk because it’s such a negative term — was actually better than those who looked at the static graphs. 

I found this very interesting because there’s a lot of debate online and a lot of big players in the information design industry who say that your charts should cut out anything extraneous. 

Shendruk’s go-to example of excessive chart junk. (Graphic from GOOD Magazine)

Stephen Few certainly comes to mind.

Yeah. I love reading Few’s blog because he just goes off on (info-visual artist) David McCandless, and it was when I started reading (Few) I really noticed the inaccuracies in McCandless’s work. But that being said, I still don’t fall into Few’s camp. 

Yeah, Few tends to boil visualization down to bar graphs a lot of the time, it seems to me. 

There’s two things to say there. First of all, people don’t look at bar graphs and pie charts in the same way that they’d look at a data visualization. People just aren’t drawn to them in the same way. 

The Eyetrack study from the Poynter Institute that I talked about during Hacks Hackers found that people look at them a lot less than they expected, actually. Or their attention was grabbed a lot less than they expected. 

Second, there are different types of information visualizations and graphics for different audiences.

I fully admit that my audience is the general public. I’m clarifying data for mass consumption. I don’t think CEOs and CFOs in Fortune 500 companies should be using my infographics to make business decisions.

I’m trying to convey ideas and concepts; not even exact numbers. Sometimes I’m conveying exact numbers. But a lot of the time, it’s just ratios.

On the flight graphic that I made, it’s not important exactly the difference between how many people fly to New York, or to Chicago, or to San Francisco. The point is, you can clearly see that way more people fly to these three places, whatever they are, and that’s the interesting part.

At a glance, you can get the information that you’ll remember. You won’t remember the exact number of people that fly to Ft. Lauderdale or Boston. But you will remember that San Francisco, Washington and New York are the top three places that people fly to from Ottawa.  

 

Do you see yourself doing this fulltime once you’re done school?

Yeah, I’d like to. I think I could, because just the amount of work that I’m getting right now, and I’ve only been in business for 3-4 months, I see myself being able to sustain myself with this as a business.

I have to finish school first, but I really spend most of my time focusing on this anyway. I go to class but I’m just trying to get it done, so I can put all my effort into this. 

-Momoko Price

 

 

 

A big piece of the data puzzle is now in place

We’ve added some juice to the site and wanted to let you know right away! Without further ado, here are some sweet changes you’ll notice the next time you login to BuzzData:

1. Prettier visualizations & articles

A big part of BuzzData’s vision is making it easier to build the story around your data. As such, we’ve upgraded BuzzData’s Articles and Visualizations tabs to a gallery-style display. Now you can showcase your data projects beautifully and professionally, without having to tinker with your own website. 

image hosted on CloudApp

[Note: At the moment posting visualizations on BuzzData still requires external image hosting. If you don’t have a favourite image-hosting app yet, we highly recommend giving CloudApp a try. We love it, and it’s free.]

2. A more versatile API

Since releasing an early-stage API a few weeks ago, we’ve received lots of excited feedback from developers & entrepreneurs about how they’d like to use it down the road. This in turn has made us pretty excited, too. Because of this, we’ve significantly expanded its functionality already, so we hope you developers out there have some fun with it (and let us know about the cool things you create). 

We’ve already had more than 100 developers request an API key, and that was when it still nascent. Now, in addition to a host of other actions, you can create a new dataset on BuzzData from scratch through the API. To us (and many of you) this means a major piece of the long-term data puzzle has come online: BuzzData can now function as a light-weight data storage target for thousands of apps around the web. Pretty cool.

You can check out the latest changes to the BuzzData API and client library in full on github. If you have any questions (or require an API key), ping us at beta@buzzdata.com we’ll get right back to you. 

3. Better social integration

One of BuzzData’s main goals is to make data more social so we can all innovate more quickly (and enjoyably). In that vein, we’ve now made it possible for you to find friends from your existing social networks (LinkedIn, Facebook and Twitter) on BuzzData.

To find friends on BuzzData, login and under My Profile > Settings, you should see this:

image hosted on CloudApp

Select the social networks you’d like to integrate into BuzzData and the next time you login, your friends will appear on your dashboard.

Want to share with someone who’s not on BuzzData? Not a problem. Go to Admin > Collaborators on your data set tab bar and type in their email address, like so:

image hosted on CloudApp

Next thing you know, all your friends will be on BuzzData!

Well, that’s it for now, but we’ve got even more features coming down the BuzzData pipeline very soon. If you’d like to know more about where we’re headed, or you have some ideas to share, feel free to contact us to chat. The feedback from the community has been amazing, and we’re always looking for more!

The BuzzData Team

Data visualization done right

Intercontinental Ballistic Microfinance from Kiva on Vimeo.

Micro-loan non-profit Kiva just recently released an interactive data visualization campaign, that to me, has everything: it tells a story and paints a picture, it’s appropriately colour-coded, it’s evocative; heck, even the musical score they picked adds a touch of humour to the mix. And the title of it, Intercontinental Ballistic Microfinance, couldn’t be more cute.

Note that this is a publicity campaign, not a viz meant to be used for business intelligence or analysis, so it falls more into the realm of pop art than science. But as data-based art, it’s just great. You rock, Kiva. 

-Momoko Price

BuzzData’s now live!

Well, this private party’s been fun, but it’s time to stop being so coy and show the world what we’re about. The BuzzData beta is officially public, open to data lovers (and the data-curious) everywhere!

In the last two weeks, we’ve gotten some incredibly engaged and knowledgeable feedback from our private-beta users. Some of the more memorable, warm fuzzy-inducing excerpts:

“I’m sure you hear the word ‘slick’ and/or ‘sleek’ all the time and are perhaps sick of it by now. But that’s what it is, darn it!”

— “I tried uploading my massive 1,315,816-row CSV today, and it worked! :-D

— “I kind of wished the sign-up process were more arduous just so I could fill in some more forms.  O_o  That’s some magic fairy dust, that is.”

— “So far I’ve loved what I’ve seen on the site, I’m kicking myself for not getting on there sooner”

And perhaps the most validating one of all:

      Dude, I love using this!”

We hope to get plenty more feedback as we roll out bigger features — every bit helps us build a product that genuinely meets the needs of the expanding data community. Talk to us, we’re listening. 

Curious about our latest iteration? Check it out for yourself. Here’s one fascinating dataset currently on BuzzData: annual food price indices as published by the Globe and Mail:

Below — an overview (cross-indexed by topic and licensed appropriately):

Then of course, the data itself (feel free to clone or download):

Last but not least, the dataset’s followers:

(To date, two beta users have already graphed and mashed up the indices data, unveiling a yet unaccounted-for spike in sugar prices. You can read about the implications of this collaborative investigative effort on open-data advocate David Eaves’s blog today

Intrigued? You should be. And now that we’re live, you can invite your friends and colleagues to check out the site, too; no invite code required. What are you waiting for? 

A few small caveats to consider while we’re in public beta:

— This is still a beta, so there will be bugs here and there (let us know when you come across bugs, we’ll tackle them ASAP.)

— As a beta, we’re still fine-tuning site accommodation in different browsers. BuzzData works by far the best in Chrome, does well in Firefox 5 and Internet Explorer 9, and is functional in Firefox 3.6 and Internet Explorer 8.  

— We’re still sticking to tabular data (csv, tsv, and simple xls) for now. More to come, we promise.

Within the next day or two, we’ll also be rolling out new features that will let you reap the benefits of the platform and get more seamlessly connected to your existing social circle.

No more faceless emailing: BuzzData is giving data users the visibility and voice (and credit) they need (and deserve).  

Noah Iliinsky On Good Visualizations

Here at BuzzData we’re still having stimulating debates about UX, visualizations and everything in between. After batting ideas around with other team members, I lobbed a few questions over to data-viz wizard Noah Iliinsky, technical editor of Beautiful Visualization. His views, transcribed here from a phone interview, were as open-minded as they were acute:


As data visualizations become more popular, it seems the line between data visualizations, infographics and visual art are becoming less clear. Where do you draw the line?

N: If you get to the point where the data is no longer meant to be consumable as data — if you cannot take meaning from it, you can say “that’s a pretty picture” but you can’t see a trend or a pattern or anything in it — I think then you’ve transitioned over to art. Which is fine, that’s acceptable, but I think that’s an important distinction to make.

You can do anything you want with your data, and of course it’s a “data visualization,” but you wouldn’t classify it necessarily with data visualizations that are meant to actually convey knowledge.

[Iliinsky then deconstructed what he believes is the difference between an infographic and a data visualization. Surprisingly, it’s not necessarily about purpose, it’s more about process:]

N: Infographics are the ones that are usually illustrated by a graphic designer; they’re probably done in Illustrator, there’s some data in them, but they’re not necessarily data-rich. They tend to be manually authored, manually constructed — obviously on a computer — but somebody sat down and said, “we’re going to put the big windmill here for ‘more windpower’ and more sunshine for ‘more solar power’ and a smaller oil barrel here” or whatever. That’s an infographic.

A data visualization tends to be generated automatically or algorithmically. Someone sets those (algorithms) up, but from there you can point to the data source and, as the data changes over time, you can regenerate the graphic trivially. It’s not something somebody had to draw.

That’s the fundamental, definitional divide.

[According to Iliinsky, however, even this divide isn’t all that significant if you what you really care about is effective communication: ]

N: I came into visualization academically, through a masters program that focused on the needs of your users and satisfying their informational needs.

So that was the headspace I was in when I started drawing diagrams: “Let’s not just represent the structure or the data; let’s really do this in a way that’s conductive to it being useful, that really addresses context of use.”

That middle ground, that sweet spot of “who is my audience, and what do they need?” — I find that that is lacking in other camps. You have the “make it pretty” camp, or “make it a sound bite” camp (from the infographic side ) and then you have the “put every possible data point in the universe on one page” camp from the big-data end of things. Neither of them are really coming from a user experience (UX) tradition.

That sweet spot of “how do you really make this visualization a solution to somebody else’s informational problem?” — that’s when you get a real success; that’s when you get something actually useful and good and fun.

A while back I witnessed in a passionate discussion of whether or not a PivotViewer visualization was “good” or not. We couldn’t really come to a consensus because we couldn’t agree on whether putting data out there for the user to play around with is in itself valuable. Or, does it fail because you’re just handing over a bunch of data and and not pulling apart the story for them?

N: I’m gonna quote Bob Dole here and say: “It depends.” The answer is context of use. One of the fundamental considerations — and this is my UX training coming in — is: what’s it for? What problem are you trying to solve with this? Once you answer that, then you can talk about whether something is in the right format. But you first have to understand what it’s for.

There’s nothing wrong with data exploration, there are some really great tools for that. Some of the PivotTable tools are fun for exploration because you can slice and dice and sort in different ways, but those typically don’t have a single message. They don’t usually have a single story they’re trying to tell. They say: here’s some data, explore it and enjoy it.

Context of use, baby, it’s everything!

I’ve gone back and forth with others at work about the value of Planetary — some of us consider it remarkable, some of us don’t. Would you call Planetary a data visualization?

N: Sure. I mean, it’s a visualization, right? It’s based on data; it’s generated, they’re not hand-drawing each planet for every track. So sure, it’s a data visualization. It’s a highly stylized data visualization.

(Planetary) is heavily aesthetic which can make it more appealing, and that’s not a bad thing. If you define the purpose as a cool way to browse your music, then there’s nothing wrong with it. Or maybe it’s a cool way to view your music.

Is it successful? Well, it’s pretty, it’s awesome, people talk about it, so it might be successful by that measure. Is it a successful data visualization? It’s probably not the first thing I would use to visualize my data.  

Richard McManus of ReadWriteWeb recently wrote about how Planetary might represent a shift to using data visualizations as UIs. Considering how hard it is to create an effective data-viz on its own, adding a UI purpose on top seems like a challenging ideal.

N: It’s a tricky thing — but it can be done well, there certainly are visual interfaces that make life much better and easier. For example, go search for a flight on Hipmunk (like Chipmunk without the C):


http://www.hipmunk.com/#!Toronto_Berlin,Jun27_Jul02

Isn’t that gorgeous? It’s amazing, right? See the “agony” filter? “Agony” is a combination of price, time of day, number of stopovers. That’s the one you want! That’s really smart.

And it’s got the time bar across the top will have both time zones listed — just spectacular. You’re not doing a lot of manipulating of this data, but if you mouse over a flight, it gives you specific times, and if you click on one, and there’s your details. That is sweet. That is really excellent use of visualization, not just for representing what’s there, but giving you really clean access to what you need.

[I also asked Iliinsky if he could cite an example of a poorly conceived data visualization and explain its shortcomings. He offered the Periodic Table of Controllers, which he blogged about back in May:]


http://complexdiagrams.com/tag/periodic-table/

N: This just takes technology and pours it into a periodic table-shaped box. This is a rant, okay? [laughs] So there’s the word “periodic,” right? And the periodic table of the elements is periodic because the elements have properties that are periodic, so when you put them in the table, the periods line up and you get a periodic table.

Unless your data is periodic, don’t put your data in a damn periodic table! It’s that simple! 

There’s the periodic table of dog breeds and desserts and Google APIs! But the one that makes me cry, the one that strikes me to my very core, is the Periodic Table of Visualization Methods. What people want mostly from these tables is a categorization, but there are great categorical groupings for visualization methods and for Google APIs — do it categorically, do a chronology. Timelines are great — it’s a really powerful axis, that time axis, because you can see where there are clumps and trends. Pour it into a box like [the periodic table] and you get none of that.

[Iliinsky then offered an alternative visualization of the same data that uses chronology to much richer effect:]


http://popchartlab.com/collections/prints/products/the-evolution-of-video-game-controllers

This is a family tree; it shows the interesting influence of the different controllers. Which is awesome and fun, and totally valid and useful — like, “Oh, that’s information that makes my life easier” — that’s the one you want. Because it actually puts them in a meaningful context, not just a chronology.

And that changes everything.

-Momoko Price

What makes a visualization good? Part I

The London Underground map is a classic example of a great data visualization.

“Most often, designs that delight us do so not because they were designed to be novel, but because they were designed to be effective; their novelty is a byproduct of effectively revealing some new insight about the world.”

-Noah Iliinsky, Beautiful Visualization

The term ‘data visualization’ definitely isn’t fringe geek-speak anymore — once CNN reports on a trend, it’s pretty much mainstream.

CNN’s feature dates back to 2009; today,  “data visualizations” pop up everywhere — The New York Times, The Guardian, the Los Angeles Times, Twitter, Facebook, Google Maps. An increasing number of enthusiasts, armed with an ever-proliferating arsenal of visual software, are making data come to life.

As data-viz gains momentum (and practitioners), visualizations have taken on fresh, dazzling forms. Colourless scatterplots have been replaced by history flows, heat maps, human figures and hues of every kind.

As an art form, data visualizations will always be in a state of perpetual reinvention. After all, one of the key attributes of a good visualization is making the viewer see data in a new way. But as designers continue to push the boundaries of data perception, the criteria of what makes a great visualization shouldn’t be cast aside.

A few visualization instruments have triggered heated discussions about data-viz criteria in the last few weeks within BuzzData’s local sphere: the first was Microsoft’s Silverlight PivotViewer.

Pivot with purpose

Earlier this month, Microsoft Canada open-source strategy lead Nik Garkusha (and team member of Open Halton) introduced a visualization to Open Hamilton’s discussion group that put Vancouver’s council expense data through PivotViewer: “I wanted to learn how to use PivotViewer and to build dynamic CXML collections, which was a fun learning experience,” he said, asking for feedback:

(See Garkusha’s visualization live here.)

Garkusha said he was originally motivated by Gary Flake’s TED talk demo of PivotViewer from 2010:

Flake’s TED talk certainly gives the impression that PivotViewer’s animation and extreme-zoom features are capable of making almost any data set pop. However, the group’s feedback was a good reminder that effective data visualization is less about sophisticated software and more about carefully considering the data you have, and what knowledge you want people to glean from it.

“It’s certainly pretty, but it’s not clear to me why this is a good visualization,” open-data hacker James McKinney said. “How do I answer simple questions like: Which councillor spent the most money, or who spends the most on parking?

“What do I care how frequently a councillor files expenses?” McKinney asked, referring to PivotViewer’s predominant use of the COUNT function over than the SUM function. “‘Who spent more money?’ is a far more common and important question than ‘Who spent the most frequently?’ “

Before long other group members, including Garkusha, began debating the issue, deconstructing the merits and faults of putting expense data in PivotViewer format.

Garkusha countered that while how much a councillor spends is certainly a common question, a visualization’s value is subject to what each viewer is looking for, in which context: “I do believe that like art, visualizations are interpreted and understood differently by different people, and whether it’s ‘good’ or ‘bad’ depends on the questions they are looking to answer & insights they are looking to derive.

“If the objective is to answer ‘which councillor spent the most money,’ what I put together as a visualization is ‘bad.’

“If the objective is to answer ‘which councillor submitted most expenses,’ what I put together may not be so bad.

“I do think it’s a bit shortsighted to imply that a certain type of question is more relevant or important than others,” he added. “Who decides what’s relevant?”

Personally speaking, I think both McKinney and Garkusha were homing in on one central point about creating data visualizations — the most telling criterion of a “good” or “bad” viz is whether or not it’s apparent the creator considered what is relevant (or worth learning) in their data, for whom, and of course, which design or tool is the right way to communicate it.

In the PivotViewer video, for example, Flake shows spectacular visualizations out of screenshots of browsing history — but browsing history is very visual and frequency-oriented, i.e.: “how often do you check which pages?” Naturally, PivotViewer’s COUNT function and deep-zoom would be perfect for this kind of data.

City council expense data, on the other hand, might need nothing more than a few clean tables, as McKinney pointed out at one point in the discussion. “You
don’t need to make data fun to make it interesting,” he added.

-Momoko Price

BuzzData to present at Hacks/Hackers!

Mark your calendars, y’all — BuzzData will hitting up Radiolaria (1166 Dundas St. W., Toronto) Monday evening (May 30, 7pm) to present our vision and a demonstration of the BuzzData app at Toronto’s Hacks/Hackers meet up. We’ll also talk about how we hope BuzzData will change the landscape of data journalism and open data for the better.

In the last few months we’ve gotten invaluable support and advice by knowledgeable members of the U.K.-based Open Knowledge Foundation, the Creative Commons, and other policy and data experts, both Canadian and international. Because of this, we’re really excited to finally share what we’ve been working on and explain how we’re different than other data startups, and why different is good.

One of the best things about BuzzData is how inclusive and community-focused the app is — BuzzData has, from the start, been guided by a vision that data can be  thought-provoking and accessible to — as well as consumed and created by — people of all professional fields and technical skill levels. If you can upload and update on Facebook, you can use BuzzData.

Not only is BuzzData a great tool with which data professionals and hacker enthusiasts can showcase their work and discuss nitty-gritty data processing details with their peers, it’s a place where curious newbies can sign in, look up a general interest like say, climate change or Canadian politics, and begin exploring and discussing how the world of public data applies to that topic.

BuzzData was built with the belief that, as Vodafone U.K. CEO Guy Lawrence once said, “data, on its own, is impotent.” Data ensconced in community and context, however, can be transformative.

You can check out all the details of the May 30 meetup here, and if you’d like to learn more about Hacks/Hackers in general, check out the home page for the global community here.

See you soon!

-Momoko Price

Public Data: Unplugged

Urban water needs: Can we keep up? from hal watts on Vimeo.

In the previous post we talked about how public data can still be compelling outside of Web 2.0 applications.

The video above is a really cool illustration of this. Hal Watts merges the conventional fields of art, design and technology to create physical visualizations like “Can We Keep Up?” which shows countries’ freshwater needs with soaked sponges.

How cool is that?

You can see the full visualization and read up on Watts here. Mesmerizing.