The Research Project
Visualizing multivariate data on geospatial maps is a challenging task. Common approaches include bivariate choropleth maps, glyph overlays, cartograms, or combinations of these techniques. Incorporating uncertainty, especially when it changes over time, further increases the complexity of the visualization. The expected outcome of this thesis is to explore these approaches and develop a method that can effectively show continuously evolving uncertainty in multivariate geospatial data without overwhelming the user.
The Research Questions
* Literature review of existing techniques for visualizing multivariate geospatial data
* Review of methods for representing uncertainty in geospatial visualizations
* Identification of perceptual, cognitive, and interaction-related challenges when visualizing multivariate and uncertain data
* Exploration and comparison of alternative visualization strategies for conveying continuously evolving uncertainty
* Conceptual design or exploratory implementation of a visualization approach for multivariate geospatial data with evolving uncertainty
* Evaluation or reflective analysis of the proposed approach with respect to interpretability and user cognitive load
Your Qualification
* Good understanding of visual analytics and data analysis.
* Experience with advanced visualization libraries (preferably d3.js)
* Expierence in data processing using programming languages such as Python, R, or Julia
* Scientific writing
We look forward to getting to know you!
If you have any questions about this position (Vacancy-ID 4143) please contact:
Prof. Dr. Andreas Gerndt
Tel.: +49 531 295 2782