The recently published autobiography by Stephen Fry titled The Fry Chronicles is also available as the iPhone app myFry. The app functions as a visual index of key theme tags within the book, all of which have been divided into 4 major groups: People, Subjects, Emotions, and Fryisms.

The entirety of the book is represented by a circular wheel of spines, each of which represents a section of text. The arcs around the outside of the wheel connect sections that are tagged with the same theme. Through interacting with the scroll wheel, the user can explore the text and read sections in chronological order, by theme, or in any order he or she chooses. As Stephen Frys autobiography was written in a style that was suited to splitting the text into separate moments in Frys life, the visual index offers the reader a different way of engaging with this book.

The application is the result of a collaboration between the book’s publisher Penguin, Dare Digital and Stefanie Posavec. Here’s a brief introduction from Stephen himself explaining the application:

http://www.youtube.com/watch?v=kxLpMMzXVCk

Together with Kim Rees from Periscopic I have reviewed the social data visualization application Swivel. Swivel is a simple to use web application that lets you visualize public or private data sets and collaborate openly or in closed user groups. My review is the first post in a series of reviews that will be published over at Information Aesthetics. Following up are reviews of the online tool Many Eyes and the desktop application Tableau Public.

Read the full review on Infosthetics.com and let me know your feedback and thoughts over there in the comments.

You you’re savvy enough you could write a custom script (maybe with Python or Ruby) that goes through your rows in your file, makes a request to one of geolocation APIs (like Yahoo! or Google) with the address data and write the longitude & latitude in your file. An experienced developer can write such a script in a manageable time. But how do you handle such a task if you haven’t the needed programming skills? Here comes Mapspread to the rescue.

The Tool

Mapspread from Poly9 let’s the user import data in various formats like KML, CSV, XLS or Shapefiles. The data can be collaboratively edited and address data gets geocoded automatically. The user can then publish the enhanced data as GeoJSON, GeoRSS, CSV, KML etc. The keyfeature of Mapspread might be the creation of Google Maps applications with the edited data plotted on it. This is perfect if you quickly want to create an interactive map to include in your website but let’s see a short use-case that only needed the geocoding part of Mapspread.

Use-Case

I received an Excel file from a client containing a ZIP code, country abbreviation, and some more data needed for our application per row. The end result is a custom ModestMaps application built with Adobe Flex that runs in the intraned of the company so I needed this data extended with longitude & latitude per entry.

1. First, I opened the Excel file in Google Spreadsheets to make some small changes like translating the table headers from german to english so Mapspread would understand what type of data is in the columns.
2. I exported the Google Spreadsheet as a CSV file and saved it locally.
3. Then I uploaded the modified file to Mapspread and imported the data as a new datalayer (the geocoding process may take some time depending on the size of the uploaded file).
4. Finally I was able to publish the datalayer as a CSV file which I download back to my machine.
5. Now I simply imported the CSV file in my Flex application and used it to plot the data on the map.

For me, the process of geocoding data without having to write a custom script has just been simplyfied using Mapspread. Do you have experiences with Mapspread or know any other services that provide similar functionality? Please share them in the comments.

Tools that help users discern patterns in data–dynamic graphs, charts, maps, plots, etc.

See the list over at Digital Research Tools Wiki.

View or download the presentation video, audio & slides from the official VAST website.

The project creators describe the three pieces of the product as follows:

1. Draw on extensive, organized data.
2. Explore and grasp complex patterns through intuitive visual analytics.
3. Deliver new insight with compelling images and video.

UUorld seems to provide a solid solution for mapping data and creating dynamic visualizations without the effort of creating a custom application for the visualization. Another big bonus is the large dataset that the kind guys of UUorld provide. There’s a plethora of statistical data to enhance existing data, create mashups or just to play around with.

The application is available free of charge for personal and non-commercial usage with a reduced feature catalog. The Pro Version comes with more extensive export & saving options and will cost you 49$. It’s available for Windows, Linux or OS X.

Project components

• The field this visualization takes place in is software developement visualization. The goal is to understand how software is built by showing code, commits, contributors, connections, calls, classes – the mighty “c” words gang, you see. During our studies at the IAD program, we’ve digged deep in this field and came up with a variety of possible solutions. You can find the documentation of my own project at artillery.ch.
• The analyzed data comes from GitHub, a superb code hosting platform that has rised to stardom in the community lately. For the visualization Ilya used the GitHub API to look up all the public repositories and to create a timeline of all the made commits to the repos.
• Let’s take a short look at code_swarm: The best description of code_swarm is ‘organic software visualization’.
  

  It eschews traditional data confinement in space and lets the elements play together in freeform and unpredictable ways.

The code_swarm code is open-source and available at Google Code and the official project website is at vis.cs.ucdavis.edu/~ogawa/codeswarm sporting some videos of well known software projects (Apache, Eclipse, Python).

You can further explore different shades of a color and their percentage in the color related to the other shades.

Beside the single visualization of a color set, the user has the possibility to compare to different color sets. Let’s say you want to compare what colors european people in spring ’08 used to the colors american color-enthusiasts used in autumn ’08 for the tag “hot”. The comparison could lead to an understanding of diverting perceptions depending on geographical and seasonal differences.