Theoretical (Analytical):

Practical (Implementation):

Literature Work:


Overview

Thematic maps such as choropleth or cartogram maps are a well-known technique for displaying geography related statistical information, such as demographic or epidemiological data. Due to their simplicity they are widely adopted beyond the visualization community.

A major drawback of this technique is the comparison of information at different points in time, since the map can only display one temporal instance of the data.

Problem Statement

The identification, localization and development of volatile spatiotemporal patterns such as an epidemic outbreaks are hard to capture on a static map. The aim of the project is to develop a Visual Analytics system that supports the investigation of spatiotemporal datasets in accordance to specific predefined tasks. 

Tasks

  • Evaluate and compare existing approaches for visualizing temporal trends on thematic maps
  • Define tasks relevant to spatiotemporal data exploration 
  • Develop a novel visualization technique within a Visual Analytics framework that supports these tasks
  • Apply on a real-world dataset (e.g. COVID-19 cases or various statistics from Eurostat)

Requirements

  • Interest in spatiotemporal data
  • Basic knowledge about information visualization, data mining and geographic information systems
  • Good programming skills in: JavaScript/TypeScript, D3.js, Python

 

Scope/Duration/Start

  • Scope: Bachelor / Master
  • 3 Month Project, 3 / 6 Month Thesis
  • Start: immediately

Contact

References

  • Cybulski, P. An Empirical Study on the Effects of Temporal Trends in Spatial Patterns on Animated Choropleth Maps. ISPRS Int. J. Geo-Inf. 2022, 11, 273. https://doi.org/10.3390/ijgi11050273
  • Wickham, H., Hofmann, H., Wickham, C. and Cook, D. (2012), Glyph-maps for visually exploring temporal patterns in climate data and models. Environmetrics, 23: 382-393. https://doi.org/10.1002/env.2152
  • J. Buchmüller, D. Jäckle, E. Cakmak, U. Brandes and D. A. Keim, "MotionRugs: Visualizing Collective Trends in Space and Time," in IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 76-86, Jan. 2019, doi: https://doi.org/10.1109/TVCG.2018.2865049