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Open Source Maps Are Helping the World Bank Save Lives in Haiti
Feb 20th, 2010 by analyticjournalism

From FastCompany:

Open Source Maps Are Helping the World Bank Save Lives in Haiti BY ANYA KAMENETZFri Feb 19, 2010


maps

An aid worker from the European Commission holds a PDF printout from OpenStreetMaps.

The humanitarian relief effort underway in Haiti is proving the true potential of open source map building. Don't take my word for it, follow the Tweets and blogs of my friend Schuyler Erle. He's on the ground in Port-au-Prince along with Tom Buckley, a developer of mapmaking program GeoCommons Maker. The pair are advising the World Bank on the use of crowd-sourced mapping, primarily through the open-source programOpenStreetMap, in the relief and recovery effort in Haiti. They are also dealing with rain, illness, PowerBar meals, World Bank contacts snowbound back in DC, and bureaucratic alphabet soup.

“Since mid-January, we've seen a whole set of interlocking technical communities swung into gear to piece together geographic information to help relief efforts after the earthquake in Haiti: OpenStreetMap, Ushahidi, CrisisMappers, and so on,” Erle writes. He's an open-source smart maps ninja–cofounder of OpenLayers, author of the books Mapping Hacksand Google Maps Hacks, and creator of a program that allowed for historians to make crowdsourced improvements to the New York Public Library's digital maps archive.

“The most amazing thing to me about this global response to the disaster is the degree to which volunteers have been able to make a significant impact on the relief situation while sitting at their own desks, thousands of miles away. OpenStreetMap, particularly, has been a model of distributed collaboration, with basically no one calling the shots, while a thousand people painstakingly build a map database of Haiti drawn from aerial and satellite imagery that's so detailed that the Ushahidi volunteers have to ask for a simpler version.”

Erle says the humanitarian applications of Geographic Information Systems may truly comes of age as a result of this disaster. “OpenStreetMap really *has* become the gold standard for base map data in the relief and recovery effort in Haiti.”

rubble

Photos by Schuyler Erle via Twitpic


 



"2009 Science and Engineering Visualization Challenge Winners Announced"
Feb 20th, 2010 by analyticjournalism

Press Release 10-028
2009 Science and Engineering Visualization Challenge Winners Announced
http://www.nsf.gov/news/news_images.jsp?cntn_id=116369&org=NSF
Winning entries appear in the Feb. 19 issue of Science
“Branching Morphogenesis” aims to reveal–through abstraction–the unseen beauty and dynamic relationships that exist between endothelial cells and their surrounding extracellular microenvironment. Movies of networking endothelial cells cultured on a 3-D matrix were analyzed to generate computational tools that simulate this process. Next, large-scale templates from simulations were overlaid with more than 75,000 inter-connected zipties.
Credit: Peter Lloyd Jones, Andrew Lucia, and Jenny E. Sabin, University of Pennsylvania's Sabin + Jones Lab Studio
Download the high-resolution JPG version of the image. (9.8 MB)
Use your mouse to right-click (Mac users may need to Ctrl-click) the link above and choose the option that will save the file or target to your computer.
Scanning electron micrograph of tiny plastic fingers around a sphere.
Tiny plastic fingers, each with a diameter 1/500th of a human hair, assemble around and hold a tiny sphere. The image brings to mind global efforts to promote the sustainability of the planet. The image was produced with a scanning electronic microscope and was digitally enhanced for color.

"2009 Science and Engineering Visualization Challenge Winners Announced"
Feb 20th, 2010 by analyticjournalism

Press Release 10-028
2009 Science and Engineering Visualization Challenge Winners Announced
http://www.nsf.gov/news/news_images.jsp?cntn_id=116369&org=NSF
Winning entries appear in the Feb. 19 issue of Science
“Branching Morphogenesis” aims to reveal–through abstraction–the unseen beauty and dynamic relationships that exist between endothelial cells and their surrounding extracellular microenvironment. Movies of networking endothelial cells cultured on a 3-D matrix were analyzed to generate computational tools that simulate this process. Next, large-scale templates from simulations were overlaid with more than 75,000 inter-connected zipties.
Credit: Peter Lloyd Jones, Andrew Lucia, and Jenny E. Sabin, University of Pennsylvania's Sabin + Jones Lab Studio
Download the high-resolution JPG version of the image. (9.8 MB)
Use your mouse to right-click (Mac users may need to Ctrl-click) the link above and choose the option that will save the file or target to your computer.
Scanning electron micrograph of tiny plastic fingers around a sphere.
Tiny plastic fingers, each with a diameter 1/500th of a human hair, assemble around and hold a tiny sphere. The image brings to mind global efforts to promote the sustainability of the planet. The image was produced with a scanning electronic microscope and was digitally enhanced for color.

Review: The Wall Street Journal Guide to Information Graphics
Feb 18th, 2010 by analyticjournalism

From FlowingData:

 

Review: The Wall Street Journal Guide to Information Graphics

Add another book to the growing library of guides on how to make information graphics the right way. Dona M. Wong, former graphics director of The Wall Street Journal and now strategy director for information Design at Siegel+Gale, provides the dos and don'ts of data presentation in The Wall Street Journal Guide to Information Graphics.

First Impressions

Given Wong's background, you can make a pretty good guess about the examples used. They're not graphics from The Journal but they do look a lot like them. The book description also makes a point of highlighting that Wong was a student of Edward Tufte, which was a big hint on what the book is like.

The guide is on the smaller side at about 150 pages of content, but it's mostly a visual book. There is about as much text as there are graphic examples, which I like. [more]


 

"Predicting the Next Enron"
Feb 17th, 2010 by analyticjournalism

Thanks to Steve Doig for the pointer:

Freakonomics – New York Times Blog
February 17, 2010, 3:00 pm
Predicting the Next Enron
By FREAKONOMICS
Via the Wall Street Journal, here’s further evidence that companies “tweak” quarterly earnings numbers. Joseph Grundfest and Nadya Malenko analyzed almost half a million earnings reports from 1980-2006. They discovered that when companies want to appear more successful than they are, they often massage their per-share earnings numbers upward by a tenth of one cent. The evidence? The number 4 appears significantly less often than expected in the post-decimal digits of earnings reports. In the U.S., per-share earnings are reported as pennies, so bumping that post-decimal digit from a 4 to a 5 results in the overall number being rounded up by a full penny. Grundfest and Malenko call the practice quadrophobia. While the tweaking may be legal in some cases, the authors also found that “quadrophobes are more likely to restate financials and to be named as defendants in SEC Accounting and Auditing Enforcement Releases (AAER).” Or, as Grundfest told the Journal, quadrophobia serves as “a leading indicator of a company that’s going to have an accounting issue.”

"GIS Data Show Relationship Between Violence, Liquor Retailers"
Feb 17th, 2010 by analyticjournalism

From: http://gisandscience.com/2010/02/17/gis-data-show-relationship-between-violence-liquor-retailers/

GIS Data Show Relationship Between Violence, Liquor Retailers

February 17, 2010 in GIS, Social Science

Annual meeting of the American Association for the Advancement of Science

18 – 22 February 2010, San Diego, California

As cities grapple with liquor-related violence, new data suggests zoning commissions may want to take a second look at where they put liquor retailers. IU Bloomington criminologist William Pridemore and Geographer Tony Grubesic are in the midst of analyzing new Geographic Information Systems (GIS) data that seem to suggest violent crime is more likely to occur in the vicinity of stores that sell liquor expressly for off-premise consumption. Violence, they are learning, is less likely to occur near other types of establishments that offer alcohol, such as bars, pubs and restaurants. Pridemore and Grubesic have conducted their studies in Cincinnati (Ohio) neighborhoods using blocks as a unit of analysis. Pridemore led the research and is the session organizer. Grubesic will speak about the scientists’ collaborative research, which is using GIS and other spatial analysis techniques to learn more about human behavior patterns.

“Using GIS and Spatial Analysis To Better Understand Patterns and Causes of Violence,” Monday, Feb. 22, from 9:45 a.m. to 11:15 a.m., Room 5A

Grubesic and Pridemore will take part in a press briefing regarding “Using GIS and Spatial Analysis to Better Understand Patterns and Causes of Violence,” at 2:00 p.m. PST on Sunday, Feb. 21, at the San Diego Convention Center. Please visit the Press Room beforehand for the event’s location (TBD).

To speak with Pridemore or Grubesic, please contact Steve Chaplin, University Communications, at 606-356-6551 or stjchap@indiana.edu.

[Source: Indiana University press release]

New book: "GIS for Public Safety"
Feb 16th, 2010 by analyticjournalism

This looks to be a good book on backgrounding how police use — or do not use — GIS so a reporter can ask informed questions. Oh, did I mention that it's free?

http://www.rutgerscps.org/gisbook/dwnld39629.html

“From the BACK COVER

This book, GIS for Public Safety, focuses on ESRI’s ArcGIS functionality (the most popular GIS software, worldwide) and presents many of the tools and techniques that are commonly used by public safety researchers, analysts, and practitioners. It gives simple steps for descriptive, exploratory, and explanatory mapping tasks and includes concise but meaningful discussions to let you critically assess and accurately apply the software to your own unique specialty. This provides a solid foundation for advanced spatial thinking and permits you to utilize GIS technology in your own innovative ways. Its comprehensive content makes it the perfect coursebook or reference manual for students, researchers, crime analysts, and other GIS users at all skill levels. To use a construction metaphor, this book is intended to teach a carpenter what tools are in his toolbox and how to use them. This instills confidence in his ability to apply these tools to any job when needed. Other books teach the carpenter specifically how to build a house. However, skills needed to build a house might fail the carpenter when he needs to build furniture instead. GIS for Public Safety focuses on a complete working knowledge of the toolbox to let the carpenter accurately apply the tools to his or her own unique specialty.”

How-to: Turning Netflix data into map
Feb 8th, 2010 by analyticjournalism

From the Society of Newspaper Designers via FlowingData:

The making of the NYT’s Netflix graphic

January 20th, 2010

One of The Times’ recent graphics, “A Peek Into Netflix Queues,” ended up being one of our more popular graphics of the past few months. (A good roundup of what people wrote is here). Since then, there have been a few questions about the how the graphic was made and Tyson Evans, a friend and colleague, thought it might interest SND members. (I bother Tyson with questions about CSS and Ruby pretty regularly, so I owe him a few favors.)

Most readers are probably interested in the interactive graphic, although I will say that we also ran a lovely full-page graphic in print in the Metropolitan section, which goes out to readers in the New York region. That graphic had a lot of interesting statistical analysis – in fact, it would have been nice to get some analysis in the web version, more on that later – but for this I will focus mostly on the web version. If there are questions about the print graphic, I will make sure I get Amanda Cox to try to explain cluster analysis to me again.

First is the data itself. Jo Craven McGinty, a CAR reporter, was in contact with Netflix to obtain a database of the top 50 movies in each ZIP code for every ZIP in the country. That’s about 1.9 million records. The database did not include the number of people renting the movie – just the rank. (We [more here: http://www.snd.org/2010/01/nyt-netflix-graphic ]




 

 

More Visualization Links on Twitter
Jan 23rd, 2010 by Tom Johnson

Thanks to Steve Doig for the pointer to….

More Visualization Links on Twitter

By: Jeff Clark    Date: Sat, 23 Jan 2010

In a recent post I showed the Top 20 Individual Data Visualizations Mentioned on Twitter and remarked that many of the most frequently mentioned twitter links were to collections of visualizations. Shown below is a meta list of the top collection-type data visualization or infographic links.

Top Collections of Data Visualization Links

  1. 50 Great Examples of Data Visualization – Webdesigner Depot

  2. Data Visualization and Infographics Resources – Smashing Magazine

  3. 15 Stunning Examples of Data Visualization – Web Design Ledger

  4. 20 Essential Infographics & Data Visualization Blogs – Inspired Magazine

  5. Is Information Visualization the Next Frontier for Design? – Fast Company

  6. 28 Rich Data Visualization Tools – InsideRIA

  7. The Beauty of Infographics and Data Visualization – Abduzeedo

  8. 50 Great Examples of Data Visualization – Sun Yat-Sen University

  9. 20 Inspiring Uses of Data Visualization – SingleFunction

  10. 5 Best Data Visualization Projects of the Year – 2009 – FlowingData

  11. Data Visualization: Stories for the Information Age – BusinessWeek

  12. Data Visualization: Modern Approaches – Smashing Magazine

  13. The 21 Heroes of Data Visualization: – BusinessWeek

  14. 20+ CSS Data Visualization Techniques – tripwire magazine

  15. MEDIA ARTS MONDAYS:Data Visualization Tools – PSFK

  16. 37 Data-ish Blogs You Should Know About – FlowingData

  17. 5 Best Data Visualization Projects of the Year – FlowingData

  18. 30 new outstanding examples of data visualization – FrancescoMugnai.com

  19. Infosthetics: the beauty of data visualization – PingMag

  20. 5 Beautiful Social Media Videos – Mashable

Here are the top product type links in the field according to Twitter data between March 24 and Dec 31, 2009.

Top Data Visualization Product Links Mentioned on Twitter

  1. Axiis : Data Visualization Framework

  2. The JavaScript InfoVis Toolkit

  3. Microsoft – What is Pivot?

  4. Many Eyes

  5. Roambi – Your Data, iPhone-Style

  6. Flare – Data Visualization for the Web

  7. Gapminder.org – For a fact based world view.

  8. SpatialKey – Location Intelligence for Decision Makers

  9. Tableau Software – Data Visualization and Business Intelligence

  10. SIMILE Widgets

and finally:

Top Data Visualization Websites Mentioned on Twitter

  1. Information Is Beautiful | Ideas, issues, concepts, subjects – visualized!

  2. FlowingData | Data Visualization and Statistics

  3. Information Aesthetics | Information Visualization & Visual Communication

  4. visualcomplexity.com | A visual exploration on mapping complex networks

  5. DataViz on Tumblr


Charting the Beatles
Main


How to Make a Heatmap – a Quick and Easy Solution
Jan 21st, 2010 by analyticjournalism

Thanks to Nathan at Flowing Data:

How to Make a Heatmap – a Quick and Easy Solution

How to Make a Heatmap – a Quick and Easy Solution

How do you make a heatmap? This came from kerimcan in the FlowingData forums, and krees followed up with a couple of good links on how to do them in R. It really is super easy. Here's how to make a heatmap with just a few lines of code, but first, a short description of what a heatmap is.

The Heatmap

In case you don't know what a heatmap is, it's basically a table that has colors in place of numbers. Colors correspond to the level of the measurement. Each column can be a different metric like above, or it can be all the same like this one. It's useful for finding highs and lows and sometimes, patterns.

On to the tutorial.

Step 0. Download R

We're going to use R for this. It's a statistical computing language and environment, and it's free. Get it for Windows, Mac, or Linux. It's a simple one-click install for Windows and Mac. I've never tried Linux.

Did you download and install R? Okay, let's move on.

Step 1. Load the data

Like all visualization, you should start with the data. No data? No visualization for you.

For this tutorial, we'll use NBA basketball statistics from last season that I downloaded from databaseBasketball. I've made it available here as a CSV file. You don't have to download it though. R can do it for you.

I'm assuming you started R already. You should see a blank window.

Now we'll load the data using read.csv().

nba <- read.csv("http://datasets.flowingdata.com/ppg2008.csv", sep=",")

We've read a CSV file from a URL and specified the field separator as a comma. The data is stored in nba.

Type nba in the window, and you can see the data.

Step 2. Sort data

The data is sorted by points per game, greatest to least. Let's make it the other way around so that it's least to greatest.

nba <- nba[order(nba$PTS),]

We could just as easily chosen to order by assists, blocks, etc.

Step 3. Prepare data

As is, the column names match the CSV file's header. That's what we want.

But we also want to name the rows by player name instead of row number, so type this in the window:

row.names(nba) <- nba$Name

Now the rows are named by player, and we don't need the first column anymore so we'll get rid of it:

nba <- nba[,2:20]

Step 4. Prepare data, again

Are you noticing something here? It's important to note that a lot of visualization involves gathering and preparing data. Rarely, do you get data exactly how you need it, so you should expect to do some data munging before the visuals. Anyways, moving on.

The data was loaded into a data frame, but it has to be a data matrix to make your heatmap. The difference between a frame and a matrix is not important for this tutorial. You just need to know how to change it.

nba_matrix <- data.matrix(nba)

Step 5. Make a heatmap

It's time for the finale. In just one line of code, build the heatmap (remove the line break):

nba_heatmap <- heatmap(nba_matrix, Rowv=NA, Colv=NA,

col = cm.colors(256), scale="column", margins=c(5,10))

You should get a heatmap that looks something like this:

Step 6. Color selection

Maybe you want a different color scheme. Just change the argument to col, which is cm.colors(256) in the line of code we just executed. Type ?cm.colors for help on what colors R offers. For example, you could use more heat-looking colors:

nba_heatmap <- heatmap(nba_matrix, Rowv=NA, Colv=NA,

col = heat.colors(256), scale="column", margins=c(5,10))

For the heatmap at the beginning of this post, I used the RColorBrewer library. Really, you can choose any color scheme you want. The col argument accepts any vector of hexidecimal-coded colors.

Step 7. Clean it up – optional

If you're using the heatmap to simply see what your data looks like, you can probably stop. But if it's for a report or presentation, you'll probably want to clean it up. You can fuss around with the options in R or you can save the graphic as a PDF and then import it into your favorite illustration software.

I personally use Adobe Illustrator, but you might prefer Inkscape, the open source (free) solution. Illustrator is kind of expensive, but you can probably find an old version on the cheap. I still use CS2. Adobe's up to CS4 already.

For the final basketball graphic, I used a blue color scheme from RColorBrewer and then lightened the blue shades, added white border, changed the font, and organized the labels in Illustrator. Voila.

Rinse and repeat to use with your own data. Have fun heatmapping.

 

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