I am a user of last.fm since 2005 and I’ve always tried to scrobble as much music as possible (34.711 songs scrobbled at present). Last.fm let’s you have an historical archive of your music listening habits, which is cool but I always missed to have an overview from the point of view of the artist. Is a musician being listened more than last year? Is an artist more listened after an album release? What happens when they are on tour? Could we visualize a change on a listener’s behavior due to specific events?

One effect I wanted to visualize is a likely increase of the artist’s playcounts after he dies. One can assume that the number of playcounts increase, but how long does this effect? Which artists get the greatest increase on their playcounts? So I built a data visualization in order to get some insight about the effects of an artist’s death:

My latest developments which involve exploring multidimensional data are ending up with one of the dataviz techniques that I like most: small multiples. This is a concept introduced by Edward Tufte and is described as:
“Illustrations of postage-stamp size are indexed by category or a label, sequenced over time like the frames of a movie, or ordered by a quantitative variable not used in the single image itself.”

To summarize, it’s the use of the same basic graphic or chart to display difference slices of a data set.

Here below two samples of small multiples we are doing at Siris Academics. The first one is the display of several regions from Italy, quantified by multiples metrics. The nice thing here is that these metrics are grouped by different concepts, such as demography, economics, etc… so based on the shape you are able to detect if a region is performing well in an specific category or not.

I definitively had a lot of fun building this piece:

If you talk about Data Visualization and album covers in the music culture there is a cover that is one of if not the most representative of these two fields. We talk about the cover for Joy Division’s Unknown Pleasures album, who became an iconic image to represent the band.

I try to be up-to-date on data section in digital newspapers, because of data driven journalism is one of my interests, being an exciting field which mixes diferent disciplines. Recently La Vanguardia newspaper has started a data analysis journalism section called VangData, which looks quite promising.

Today they have published a chart to show the average height on OECD countries, here a snapshot:

Although the chart fulfills its purpose which is showing a rank of the countries based on its population’s height, here one of the principles in effective data visualization, the Data-Ink ratio, has definitely a room for improvement.

The exhibit Big Band Data now has moved to Madrid and I was required to adapt my project based on Barcelona in order to show data from Madrid.

That was a good opportunity to adapt my project to another city because that would let me comparing behaviors between 2 cities. It took me a few hours to obtain and transform shapefiles from Madrid, retrieve all the data again and re-run all the data transformation process but the result worth it:

In this occasion not only the city but the neighborhoods and the community of Madrid are also plotted, so you have more insight about the movements of the customers.

As customers are grouped by their origin (based on its postal code), the fact of showing the paths traveled by all the customers from its origin to the point where the purchase is done reveals that some shopping center act as ‘demographic walls’, as customers from the city don’t move to city outsides and customers from neighborhoods don’t go into the city:

Comparing data between Barcelona and Madrid, notice that the behaviors of the customers is pretty similar: the number of transactions increase to a maximum during the previous days before Christmas. It’s then when the activity falls to a minimum. For the rest of the inspected period both cities share a similar behavior. Another expected point is that the amount of expenditure is much greater in Madrid (3x for city’s citizens and around 2x for citizens living close the city)

Elastic lists is a good technique to navigate for a n-dimensional datasets, allowing to apply faceted browsing in a visual way. I believe the original idea is from Moritz Stefaner, you can see his excellent work about that here.

So far it seems there isn’t an implementation of Elastic lists with d3.js, so here it is a first approach, check it out here.

Source code is available, use it as you wish!

Recently I was asked to develop an online interactive piece for the University of La Rioja (UNIR) that allowed readers to explore the crimes in Spain on a number of different metrics.

This piece provides an interactive map, allowing the user to display the crime distribution in Spain based on different criteria such as crime type and subtype, crime status and year.

It was a funny assignment as it covered all the process: starting with preparing all the data and ending with providing visualization and interactivity of the piece.

Link: Crime distribution in Spain