Processing ported to Javascript
Processing is an easy to use programming language designed to make creating data visualizations easier. Processing simplifies the syntax of writing programs that draw graphics and use animation. The language was designed to be easy enough to be used by designers, to abstract most of the complication of writing the same functionality in Java. Each Processing application is finally converted into Java and can be either uploaded to a website as an applet or as a standalone Java program.
John Resig, has now ported the Processing language to Javascript. Using this library now you can write processing code that gets dynamically converted into Javascript and loaded directly in the browser, without having to load a Java Applet. Resig has a long list of examples using his Processing Javascript Library.
Baseball Visualizations
The new baseball season has just started and like every year the race is on to win the World Series. Baseball is probably the richest sport when it comes to statistical data and analysis, yet for a sport so rich in statistical data a search in the custom google data visualization search engine, and the infovis image search database yielded very few results. These are some of the more interesting baseball visualizations I found around.
Salary vs Performance - Ben Fry, one of the authors of the Processing programming language uses his freely available tool to visualize which baseball teams are spending their money well, and how does each team position changes over the course of the season? The last applet uploaded looks at the teams and their salaries in 2007.
Baseball Visualization Tool - This is a commercial tool that uses a pie chart to guide the manager whether to pull the pitcher or not. The fuller the pie chart the more the pitcher should be changed.
Baseball race - This visualization tracks the progress of each team in a season as the season progresses. The dataset used for this application starts from 1901 and continues till the present day. The data is freely available from Retrosheet, a baseball scores database.
Bivariate Baseball Score Plots - The bivariate baseball score plots present summary information for MLB teams game scores. The scores are visualized using a bivariate baseball score plot with each game being a point in a two-dimensional grid.
Chernoff Faces baseball managers - A visualization coming fresh off the press that uses Chernoff faces to display baseball manager stats. The features of the face like face height, width, nose size, mouth curvature, etc. change according to the values of the attributes they are representing.
Mitchell Report Visualization - In December 2007 a 409 page report was published detailing the use of steroids in Major League Baseball. A social network of connections between players and trainers mentioned in the Mitchell Report was created using Social Action, a tool developed by the HCI Lab of Maryland University.
Bill James - A video interview and a newspaper interview with the most popular baseball statistician, and also the inventor of the term used to describe baseball analysis - Sabremetrics.
Using faces to display data
Dr. Steve C Wang used a data visualization technique called Chernoff faces to display some characteristic of baseball managers in 2007. The technique was developed by Herman Chernoff in 1973, and the idea behind it is to display different data attributes as facial features such as curvature of the mouth, length of nose, direction of eyebrows. In Dr Wang’s graphic, the number of lineups used by the manager is the length of face, width of eyes and ears; the number of pinch-hitters is the width of the hair, and the width of the face.
Using this technique one can display many different attributes of a data set in a single face then allow the user to compare the different faces to analyse the data. In fact Chernoff claims that up to 18 data elements can be displayed using this method, allowing the user to visually cluster the data.
How effective are Chernoff faces in conveying information? Maybe the faces do not covey information at first glance, and they need a lot of referencing to the face legend, however I think they make an interesting and fun way of displaying information. The sole fact that this technique made the pages of the NY Times is enough proof of this. I’m sure that if the same data was displayed with bar graphs and pie charts it wouldn’t make any headlines. Most user studies in visualisation take into account the efficiency (speed in answering / accuracy of answer) of the technique, however techniques like Chernoff face maybe aren’t suited for answering questions fast, but they are catchy and media friendly.
More on Chernoff Faces
Critique by Robert Kosara
An Experimental Analysis of the Effectiveness of Features in Chernoff Faces
Chernoff Faces in Psychology
Data Mining and Info Vis for this week
Nat Torkington from O'Reilly Radar published an interesting weekly roundup post of the Data Mining and Visualization posts. The most interesting posts mentioned are: Catching a poker cheat with data mining, SNA toolkit for R and a link to a machine learning blog called Machine Learning (Theory)
175 Visualization Resources
The title says it all really. Meryl.net published a a very long list of visualization examples, blogs, influential vis people. Worth a look.
In no. 28 there's the Felton Annual report which I was planning to blog about, some time in the future. It's a personal annual report presented in a very creative way.
Info Graphic Humour
Which is the most popular social network site in your country?
The numbers speak for themselves – Social Networking Sites are popular all over the world.
- In 90 (79%) countries a major social networking site features in the top 10 sites of that country.
- In 19 of these countries, the social networking site is the highest ranking site in the country – ranking higher than any search engine.
- From a sample of 116 countries only 2 (Taiwan and Vietnam) didn’t include a popular social networking site in the list of the top 100 websites.
The popularity of social networking sites is no surprise, and several statistics (1, 2, 3) have been published about the major social networking sites like facebook and myspace. There are however few reports on the use of these sites by geographic region. The only geographic distributions I came across were from Comscore, the Social Network Sites paper published in JCMC, and ValleyWag .
Using the Many Eyes platform I created three different visualizations of the most popular SN sites used in each country. The data used for determining the country popularity was collected from Alexa ratings. For more information on how the data was extracted see – how to collect geographic website rankings from the internet.
The world map is a colour coded map with each social networking site represented in a different colour. Where data wasn’t available, the country border is not displayed. If you click on a site from the list on the right, the countries that use that site are highlighted.
This second display shows a rectangular table display (treemap) of the data divided either by social networking platform, or by country. To alter between the displays reorder the treemap hierarchy by dragging the ordering on top of the visualization display.
The third visualization shows the ranking of the social network sites, and the number of internet users in each region. In the darker coloured regions, social networking sites ranked higher than other websites. The size of each rectangle is proportional to the number of internet users in the country, the bigger the rectangle, the more users there are.
Which visual representation of the data set do you prefer, and why? Do you think that one of the displays is superior to the others? Can you think of other different ways to present this data graphically? The aim of this exercise is to display some interesting data using Many Eyes and stimulate discussions on the different visualizations and data presented.
If you’d like to voice your comments, comment on the specific visualization by clicking the comment link in the respective visualization. The data used to generate the results is freely accessible on the Many Eyes site. You can use the uploaded data to create other visualizations in Many Eyes. After all, if you reply with a picture it’s like you’re writing a thousand words, isn’t it.
Viewing websites as graphs
Aharef wrote a java applet that displays the structure of a web page as a graph of nodes. Each type of node is coloured in a different colour, so for example links are blue, tables are red and images are violet. You can generate the graph for any website from here. This is the graph for mindspill.
Tip 22: Tracking down large files
Do you want to know which files are filling your disk? Do it with ease and style with SequoiaViewer. This free small application maps out files on disk and represents them as squares whose size is dependent on the size of the file. Files are bundled together logically according to the containing folder and different colour schemes can be selected to colour code different file types. If you scroll the mouse on the square you'll see the name of the file.
If you like this unusual information representation you might want to check this site: information aesthetics
Source: Computer Active: PC Tuning Solutions
This article is part of the Tip of the day project
