Algorithmic Geo-visualization

From theory to practice
Image: A world map showcasing a variety of thematic mapping techniques.

Visual representations, often in the form of maps, are one of the most effective ways for humans to interact with large data sets; they aid with complex cognitive tasks such as discovery and decision making. Information visualization plays a key role in exploring, analyzing and communicating large quantities of data.

Time-varying and dynamic data sets (for example, stock prices, traffic status, or weather) remain a challenge for most visualization algorithms. A primary requirement is stability: small changes in the data should lead to small changes in the visualization. Without stability, there is no cohesion between two visualizations showing similar data, which makes them difficult to interpret.

Visual analysis tools should be easily accessible to allow domain scientists across all areas, specialists and even the general public to explore, analyze and communicate data. However, many cutting-edge geo-visualization techniques are not available in an easy-to-use form; they exist at best as research proof-of-concept implementations. Furthermore, techniques for stable visualizations of dynamic data are still mostly lacking.

This project has two goals:

Develop two eScience tools (an online platform and a code library) to move advanced information-visualization and mapping techniques from theoretic concepts to practical tools, thereby increasing their impact through reusability

Significantly extend the state-of-the-art by developing stable geo-visualizations that can handle large quantities of time-varying data

Other project members currently working on this project include:

Kevin Verbeek

Wouter Meulemans

Jules Wulms

Team

  • Bettina Speckmann
  • Carlos Martinez-Ortiz
    Netherlands eScience Center
  • Rena Bakhshi
    Netherlands eScience Center