Open data in this area creates new opportunities for researchers, the institutions themselves, but also for the public. In an attempt to reach a larger audience many institutions already offer interfaces to access their data. Most of these interfaces only rely on the search slot. But the search slot is not the most engaging interface. The following experiments try to explore the idea of generous interfaces, inviting users to visually explore the datasets, fostering serendipity and the chance to gain new insights through visual exploration.
One institution opening their data vaults to the public was the German National Libraray (DNB). The DNB has already developed an API allowing other data providers to sync datasets. In addition to that they also offer free downloads of database dumps in XML format.
For those interested in creating their own visualizations, I developed a PHP parser that allows the handling of large (10GB+) XMLs and the extraction of specific parameters which are then stored in a SQL database: https://github.com/sebastian-meier/dnb-visualization
This visualization allows the user to display each person’s data stored in the DNB database and see (if available) where this person was living or working. Furthermore, the user can see who else was living in that location during that specific time. The idea behind the visualization is to illustrate the social environment in which a person was working, showing all the people somebody was surrounded by. To emphasize this, the user can filter the persons for each location by profession, trying to identify who was working in the same field during a specific time. From here on, the user can select other persons in the same locations or, if the user finds a city that seems interesting, she can also select this city to explore it from a broader perspective in the ‘city visualization’.
See for yourself: Visualization of Leonardo da vinci
In the ‘city visualization’ the user can pick one or up to three cities and see how many people were living in that location at a certain time (according to the DNB). The user can furthermore filter the data by professions. This visualization does not only show how many people listed in the DNB lived in the same city but it allows the visual exploration of multiple dataset. The user is enabled to detect patterns by means of visual comparison. In the image below we see (from top to bottom) Vienna, Amsterdam and Florence. Most location datasets in the DNB have their peak during the 20th century, but through this visualization we can see very clearly how some historically significant cities like Florence have their peak in a different period, or how Amsterdam was booming in the 17th and 18th century.
Compare cities yourself: City comparison
The geovisualization is looking at the DNB data from an even broader perspective when zooming out from the city level to a world view. The visualization of the data allows the user to see which locations are available in the database and furthermore explore where and when people lived in those locations.
This enables the user to see how the dataset is growing throughout the decades:
Or to see the difference between the number of locations in a certain area or the number of people represented in those locations:
Explore the map yourself: Map Visualization
The visualizations allow the user to explore the data, but it has to be kept in mind that the visualizations are only representing the collected data of the DNB, conclusions deriving from the visualizations should be questioned carefully.
If you are interested in the techniques behind the visualizations, the source code is also hosted on github: https://github.com/sebastian-meier/dnb-visualization