Graphic showing distribution of songs by year and country of origin.

Mining the data from the top one hundred songs not only adds a unique visual appeal to the story, but also uncovers other underlying facts and new stories within the data. Charting the songs, it’s clear that the 1990s was the most favoured decade for songs, and that 1997 topped the list.

The two most interesting visual stories to grow from the data were charting songs across the years, and also the country of origin of the bands – a pie chart that makes the domination by UK and US artists obvious.

A few other interesting facts popped up while working with the data, but didn’t fit into a simple chart. The wide horizontal format of the years graph and the pie chart of the countries left a space on the left that worked well for a simple bullet list of numbers highlighting these random facts and rounding out the graphic.

Repeating the process

Graphic showing top artists by state, and friends per local artist.

MySpace Australia’s list of their top 20 artists by state presented a great opportunity for the same approach, thankfully this time with enough lead time before release that we could really dig into the data.

Mining the data provided in their press release and adding to it with more research to fill out details such as number of friends, record label details and location, three compelling stories came to the surface. Visually it’s clear that over half of the artists were signed to major record labels; almost all states had more international than local bands in their top twenty; and that when comparing the number of friends for local artists, there was a clear hierarchy.

A couple of other interesting views came through in the data that we didn’t use in the final chart. For example, all the Australian artists combined don’t have as many friends as just T.I, the top International artist.

Looking at the distribution of artists across the different state top 20s it’s also very clear that the majority of artists only appear in the list for one state.

An unused, alternate version of the MySpace Top 20 data.

With the other three MySpace charts being both clearer and much simpler to understand, neither of these last two visualisations made it through to the final story.

Through this process of charting and visual editing, the Hottest 100 and MySpace Top 20 charts turned otherwise text-heavy stories into richer, more engaging coverage.

It is certainly a great way to embrace your users and hold their hand through what is large number of changes to the site. And by using the voice and face of someone that the listeners and users all know, I can imagine that they won’t be receiving a large backlash to the redesign.

Update

The new NPR.org redesign

The new NPR.org redesign has been launched and is a vast improvement on the previous site. Plus, the NPR blog has a series of posts from the redesign team discussing the changes to processes, tools, and changes that do and do not affect users.

Nicholas Rapp has written a post over at his personal blog discussing the creation of a graphic on American vehicle carbon dioxide emissions. The creation of this graphic demonstrates how data can be interpreted in different ways depending on the method that it is displayed.

The first map showing emissions come from major cities

In his post you can see a map of the USA with two versions of the same data overlay; the first has the total emissions per county per year, the second has total emissions per capita per year. The interesting thing is the two stories that the data tell; naturally the first says that all the emissions come from the major cities. But when you look at the second map, there as a noticeable difference in the indicators.

The second map tells a different story

Cities like Los Angeles have a large emission marker on the first version (per county) but a basically non-existent in the second version (per capita). Where as places in the middle of the country have a much larger indicator in the second version. I’m not going to go into what the data is implying (like less people in the country and further to travel etc) but it does demonstrate that from the same data two different stories can be told.

I think it is important to know what story you want to tell when designing a visualisation around data, though sometimes it may not hurt to test the data against some different filters/categories to see if there is something else that maybe more interesting.

Update

Carbon emissions from road transport

Since this post is about vechile carbon emissions, you might like to have a look at a data set The Guardian has released on carbon emmissions from vechiles around the world. From this data they have also produced a really nice clean graphic with the 10 largest polluting countries.

We have been working on creating a new service that make lots of data instantly available for intuitive, visual exploration. Today’s launch is a first step in that direction.

Starting with data from the U.S. Bureau of Labor Statistics and the U.S. Census Bureau’s Population Division, Google are using Flash to allow users to chart and analyse unemployment or population statistics. While you’re able to create links to your view of the data, for the moment it’s not possible to embed them in other web pages.

It’s clear that Google have plans to add plenty more datasets to the service which, given it comes from the engine behind the famous Gapminder system, could have a big impact on how easily we can interpret data.

Currently the story of the H1N1 Swine Flu is being approached in two ways on a map:

1. How far the virus has spread and how many people it has effected.
2. How far the virus has spread and how fast it traveled that distance.

A simple map using Google

The Google map is a good example of coverage though it can be hard to read how many people effected by the virus as you need to click on each point to read the information, though it doesn’t give you any sense of how fast it has spread.

The Wall Street Journal's interactive map

The Wall Street Journal has an interactive flash map which is easier to use, with more visual feedback on the spread and number of people effected.

The Guardian's map shows the spread of cases day by day

For portraying the sense of speed that the virus has traveled, The Guardian map I believe to be one of the best. Not only does it show markers for the types of swine flu cases (suspected and confirmed outbreaks), it also has a click-able time line that updates the map with the spread of the virus day by day. This is very effective at telling the story of a global emergency, by clicking each date you see the dots on the map grow, visually reinforcing the speed and spread much better than text ever could. But it is hard to gauge the number of people effected, except for reading a single overall paragraph each day.

The New York Times swine flu map

A half way point between telling these two stories on a map is over at the New York Times. Their flash map, similar to the Wall Street Journal map is accompanied by a day by day excerpt in the right hand column. This doesn’t really give the sense of speed that the virus has spread compared to the Guardian version, though you can read about the unfolding event while obtaining good information on the number of people effected from the map.

Overall, I don’t think we have seen the last versions of these maps from the news site. I am sure that before the end of (or very soon after) this global event there will be an interactive map, probably with a multimedia component, produced that will tell the story of speed, spread and effect that the H1N1 Swine Flu had on the world.

Opening photo by Wade From Oklahoma