Today, Kevin Merritt, founder and CEO of blist, provides some background on putting data in the hands of mainstream users.

blist is not a company of modest ambitions. We want to democratize working with data much as PowerPoint and Visio have empowered mainstream users to create their own presentations and diagrams. Before these breakthroughs in innovation, mainstream users sketched free hand and asked professionals in central resource pools (art departments and engineering departments) to turn drawings into foil transparencies and blueprints.
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Matthew Block and Jonathan Corum from The New York Times use a network graph to map diseases and the genes they have in common. Color indicates the type of disease, circles represent diseases, and gray squares are genes that the diseases have in common. The graphic has a nice magnifying glass zooming feature, so that you too can be a biologist.

A quick announcement: I’m headed back to California for a few days and may or may not be online. While I’m gone, I have a couple of interesting guest posts scheduled, so I’m looking forward to reading what you all think when I get back :).

Also, I have two guest post spots left for when I leave on vacation, so anyone is welcome to email me their ideas.

If I were to skip straight to the part in The Shawshank Redemption when Andy Durfesne climbs out of the pipe of poo (and put it on mute), someone who never saw the movie might see an escaped convict who steals money from a warden and fleas to some random place in Mexico called Zihuatanejo. Out of grief, the warden kills himself and Ellis Boyd “Red” Redding eventually teams up with Andy to commit more crimes.

Those of us who have seen the movie though know this isn’t the case. Why? Because we saw the whole movie and have context.

Context Matters

As Andrew, a FlowingData reader, put it, “For statistics to be useful, it needs to be explained in a context.” When I get my hands on some data, whether I’m analyzing or visualizing, I want to know the context of data first. I want to know who collected the data, how it was collected, when it was collected, and what was done to it before it arrived in my hands. Without that meta-information, I could easily make an incorrect assumption about the data or misrepresent it somehow in a visualization – which is very bad.

Simply put, we use visualization and statistics to tell stories with data. If we don’t have all the information, then we can’t tell a complete story.

Thank you to everyone who responded to last week’s poll: What Field of Expertise Do You Study or Work In? At the time I’m writing this, there were 326 responses. While I knew all of you came from lots of different fields, I was surprised by how diverse this group really is, which made me really happy.

Here are the results. I tried to extract some of the “Other” responses from the comments and placed them into new categories.
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NewsWare was launched yesterday on msnbc.com. It’s a set of apps, games, and widgets to interact with the news. The three main points of interest are the Spectra (pictured above) and two games that resemble a couple of popular arcade games infused with news.
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In a deviation from the usual pie chart and standard tree map, this graphic from The New York Times resembles something of a stained glass window – a really pretty piece of work. Amanda Cox, with Matthew Bloch and Shan Carter, designed the interactive graphic that lets you explore how American consumers spend their money.
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Visualization Criticism – A criticism on the criticism on visualization. Robert Kosara, Fritz Drury, Lars Erik Holmquist, and David Laidlaw argue that we need to critique to further develop viz theory.

Data Visualization Talks Online – Talks for your viewing pleasure from the likes of Ben Fry, Eric Rodenbeck, Jonathan Harris, and others. A couple hours of weekend learning.

Why Things Cost $19.95 – An interesting article from Scientific American on the “psychological rules of bartering.” Any guesses on this somewhat arbitrary pricing?

People from lots of different fields have emailed me during the course of FlowingData, and I continue to be surprised by the wide reader diversity. So naturally, I’m interested to know what fields all of you study or work in. If you select the Other option, please do leave your “other field” in the comments, or if you are some mix of everything, I’d be interested to hear about that too.

Of the 23,160 Facebook applications, I use about 5, but I probably wouldn’t notice if someone randomly removed all of them from my profile in the middle of the night. Kids these days. I used to play BlockStar, but haven’t used it since it changed to Tetris (formerly BlockStar) and haven’t played Scrabulous since my 1,000,000th consecutive loss. What Facebook applications do you use (or not use)?

Speaking of Facebook, have you joined the FlowingData group yet?

Inspired by Jonathan Harris and Sep Kamvar’s We Feel Fine, and using data from summize, twistori shows what people love, hate, think, believe, feel, and wish for on Twitter. Given the conversational feel of Twitter, twistori shows an almost natural flow of emotion and like Twittervision, is sort of mesmerizing.

[via Twitter]

I studied electrical engineering and computer science in undergrad and now as a stat student, I still work with a lot of engineers and scientists. Something that has always confused me as I walk through the engineering (and statistics) halls of conference posters is the use of the rainbow color scale.
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Ben Fry maps every road segment in All Streets, according to the U.S. Census Bureau’s TIGER/Line data. There’s no actual map or drawn borders; instead Ben chooses to let the data do all the work, and the results are very pretty. Sometimes you don’t need a map to map.

I was somewhat surprised to see California’s low road density compared to the eastern half of the country, but I guess that’s because of all the freeways. What’s more surprising though is that line down the middle. Roads all of a sudden go dense somewhere around North Dakota. Is that really what it’s like? Does farming suddenly stop and urban life begins in these areas?

Poor Alaska and Hawaii, with too few roads, were left out.

Patrick Ball, a human rights statistician, finds truth in numbers while analyzing and consulting to find patterns and uncover scale in crimes against humanity.

The tension started in the witness room. “You could feel the stress rolling off the walls in there,” Patrick Ball remembers. “I can remember realizing that this is why lawyers wear sport coats – you can’t see all the sweat on their arms and back.” He was, you could say, a little nervous to be cross-examined by Slobodan Milosevic.

Mr. Ball was the first expert witness called in the case against the former Serbian president, who was representing himself against mass atrocity charges at the International Criminal Tribunal for Yugoslavia. Ball had spent 10 months crunching numbers about migration patterns in the former Yugoslav province of Kosovo; his findings suggested that hundreds of thousands of refugees who fled to Albania were spurred by the violence of Mr. Milosevic’s army. By the time Ball entered the tribunal chamber, in March 2002, the ousted leader had a reputation for grand orations rather than direct questions; when Milosevic veered off track, the judge would interrupt. “Milosevic would say, ‘Dobro,’ and go on….” Ball remembers. “It means, ‘OK, very well,’ but it was clearly a, ‘Very well, we’ll have you shot later.’ I hear [that] in my dreams periodically.”

Ball is a statistician – not exactly a profession usually associated with human rights defense. But the Human Rights Data Analysis Group that he heads at Benetech, a technology company with a social justice focus, is bringing the power of quantitative analysis to a field otherwise full of anecdote.

That’s right. Statistics is awesome. I dare you to disagree.

[via Statistical Modeling]

Dan Beech represents worldwide poverty in this video, which is actually a 3-dimensional bar chart with some flare:

Welcome to Povertyville, Slumtown, and Low Income city. I’m not sure what to think. Should I laugh? Should I cry? I don’t know. What do you think?

In this genre of over-produced graphs, Povertyville reminds me of the real estate roller coaster, a dramatic 3-D time series plot:

Early next month, I’m going to be traveling a bit. I’m headed back to California for about a week for some work-related stuff. Soon after, my wife and I will be celebrating our one-year anniversary on some tropical island where I will be basking in the glory of all-inclusive. The following week, I’ll be at the International Summit for Community Wireless Networks.

I’m going to write posts in advance, but I’d also like to feature some high quality posts from FlowingData readers (like yourself) while I’m gone.

What I’m Looking For

I’m pretty open as long as it’s within the scope of FlowingData, but here are some ideas I’m interested in finding:

• Anecdotes on how you use data, statistics, or visualization to discover new things.
• The design process (from data-culling to final product) from those who are working on or who have worked on data visualization projects.
• Tips and tutorials on how to tackle certain types of data.

I’m not looking for heavy promotion of a product (although I don’t mind if you mention it). I want to keep the focus on learning and not so much on buying. Also, I’m looking for original content only. I say this just because I want to stay legit with search engines, so please, no duplicate content.

Email Me Your Post

To submit a post, send it to me via email. Put “FlowingData Guest Post” in the subject line, and put your post in the actual email or a plain text file. No Microsoft Word documents, and if your post is already with HTML markup, all the better.

I’m not really sure how many posts to expect, but I’ll use as many submissions as possible, if not all of them. My hope is that I’ll be able to highlight some more flowing data and as well as help us all learn a thing or two. Looking forward to what you all have in store.

Wilson Miner and Paul Smith, two co-founders of Everyblock, post tutorials and a little bit of their own experiences rolling out their own maps and creating graphs with web standards.

Why Not Go With Google Maps?

Paul gets into the mechanics of how you can use your own maps discussing the map stack – browser UI, tile cache, map server, and finally, the data. My favorite part though was his reasons for going with their own maps:

Ask yourself this question: why would you, as a website developer who controls all aspects of your site, from typography to layout, to color palette to photography, to UI functionality, allow a big, alien blob to be plopped down in the middle of your otherwise meticulously designed application? Think about it. You accept whatever colors, fonts, and map layers Google chooses for their map tiles. Sure, you try to rein it back in with custom markers and overlays, but at the root, the core component—the map itself—is out of your hands.

Because it’s so easy to put in Google Maps instead of make your own (although it is getting a little easier), everything starts to look and feel the same and we get stuck in this Google Maps-confined interaction funk. Don’t get me wrong. Google Maps does have its uses and it is a great application. I look up directions with it all the time, but we should also keep in mind that there’s more to mapping than bubble markers all in the color of the Google flag.

Remember: a little bit of design goes a long way.

Data Visualization with Web Standards

Wilson provides a tutorial for horizontal bar charts and sparklines with nothing but HTML and CSS. Why would you want to do this when you could use some fancy graphing API? Using Everyblock as an example, data visualization can serve as part of a navigation system as opposed to a standalone graphic:

Sometimes the visualization isn’t at the center of attention.

Make sure you check out Everyblock, a site that is all about the data in your very own neighborhood, to see these maps and graphs in action.

[Thanks, Jodi]

Amanda Cox, of The New York Times, made another excellent graphic (and I wouldn’t expect anything less). We see an entire story between Obama and Clinton – positions taken, counties won, and counties lost. Go ahead and take a look. Words bad. Picture good. Ooga. Booga.

[via Infographics News]

Moritz has been working on visualization of a hierarchical glossary carefully named “Glossary Visualization” versions 2-5. Not sure where version 1 is. Being a network graph, I can see this getting chaotic when there are more words (or categories) involved, but then again, maybe that’s all the words. In either case, it beats browsing through words in a dictionary; although, these prototypes don’t include definitions yet.

In the most recent version, words are represented as a DOI tree showing only the categories. Click on a category and view the sub-categories.

All four versions were implemented using the recently-mentioned Flare visualization toolkit.

What do you think – cluttered or just right?