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Visualizing in Valencia

February 19th, 2016

In November I had the opportunity to travel with Tapio Nurminen to Valencia, Spain as part of the CAP (Collaborative Analytics Platform) Review organized by ITEA (Information Technology for European Advancement). We participated along with other partner institutions and presented a product demonstration we had developed for the project.

For most of our stay in Valencia, the weather was warm, although according to the locals, the weather should have been colder for this time of the year. But it was warm enough to have a quick swim in the chilly ocean waters in an afternoon or a jog through the Turia Garden riverbed that crosses the city.

The city has a mix of modern and old architecture, with churches more than 800 years old as well as modern structures like the Assut de l’Or Bridge and the City of Arts and Sciences. Along sea side, old traditional houses can also be seen with decorative tiles on their facades.

ITEA 3 logoThe venue for the event was the Escuela Politecnica de Valencia on the north side of the city, its modern facilities and the hospitality of the organizers allowed us to have productive meetings. It was an interesting space for sharing knowledge and getting an update on the projects that partners were working on, some with new ideas on data analytics, services and tools. Participants included partner institutions from around Europe and from Korea. We had discussions on the progress of the whole project and presentations from each of the partners.

For the demonstration, we and our partners developed an interactive visual analytics tool for the analysis of network traffic events, designed to allow the filtering of event data based on selected characteristics such as time, location, etc. We developed the tool as a Javascript web application using Angular JS, dc.js (a chart library based on D3.js) for the visualizations along with Crossfilter for the data manipulation and Leaflet for geolocation mapping.

angularThe development presented several challenges, among them, managing the latency and handling of large amounts of data interactively avoiding delays when data is filtered or browsed. Crossfilter and dc.js support handling thousands of datapoints which is why we chose to incorporate these Javascript libraries. Another challenge was selecting the right combination of visualizations to represent relevant data across the available features in the dataset. We could create suitable charts by experimenting with cross-filtering data among associated features. There are further improvements to be made, and we will continue to develop our tool with new visualization techniques, adding usability and improving performance.

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