Geovisual analysis of VGI for understanding people’s behaviour in relation to multi-faceted context (EVA-VGI 2)

  • Moris Zahtila
  • Maximilian Hartmann
  • Ross Purves
    Ross Purves
  • Prof. Dr. Stefan Wrobel
    Prof. Dr. Stefan Wrobel
  • Dirk Burghardt
    Prof. Dr.-Ing. Dirk Burghardt
  • Alexander Dunkel
    Dr.-Ing. Alexander Dunkel
  • Prof. Dr. Gennady Andrienko
    Prof. Dr. Gennady Andrienko
  • Prof. Dr. Natalia Andrienko
    Prof. Dr. Natalia Andrienko

Volunteered Geographic Information (VGI) in the form of actively and passively generated spatial content offers extensive potential for a wide range of applications. Realising this potential however requires methods which take account of the specific properties of such data, for example its heterogeneity, quality, subjectivity, spatial resolution and temporal relevance. The creation and production of such content through social media platforms is an expression of human behaviour, and as such influenced strongly by events and external context. In this project we will develop geovisual analysis methods which show how actors interact in LBSM, and how their interactions influence, and are influenced by, their physical and social environment and relations.

Visual analytics system supporting exploration of evolution of the popularity of different topics and keywords in social media across geographic locations and users (Chen et al. 2018).

In the first phase of the project, we developed and demonstrated a conceptual model enabling the extraction, analysis and visualisation of events and reactions to events in location based social media (LBSM). A central element of this model and its implementation is the integration of spatial, temporal, thematic and social dimensions combined with an explicit link between events and reactions. The follow up project will concentrate on the development of methods allowing links to be made between reactions, human activities and emotions. Furthermore, the social and physical context of events, and the resulting influences on and from behaviour will form a central research element. The project will explore the representativity of LBSM and its limits with respect to a range of research questions and application fields. An important focus will be on the identification of relationships between different actors and events, supported by the use and further development of a broad palette of methods and workflows from the domain of visual analytics.

As well as the development of generic approaches enabling comparative analysis using geostatistical and visual methods, we will refine, combine and parameterise methods for specific application domains. The focus here lies in three thematic areas to which the project consortium can bring experience and contact to partners from practice: 1) landscape and urban planning for the analyse and evaluation of green areas, 2) transport planning to explore group and individual mobility patterns and 3) political science through the application of comparative visual methods.


  1. Dunkel, A., Löchner, M., & Burghardt, D. (2020). Privacy-Aware Visualization of Volunteered Geographic Information (VGI) to Analyze Spatial Activity: A Benchmark Implementation . ISPRS International Journal of Geo-Information, 9(10), 607. DOI: 10.3390/ijgi9100607
  2. Gründemann, T., & Burghardt, D. (2020). A taxonomy for classifying user groups in location-based social media. AGILE: GIScience Series, 1, 5. DOI: 10.5194/agile-giss-1-5-2020
  3. Gröbe, M., & Burghardt, D. (2020). Micro diagrams: visualization of categorical point data from location-based social media . Cartography and Geographic Information Science, 47(4), 305–320. DOI: 10.1080/15230406.2020.1733438
  4. Andrienko, N., Andrienko, G., Miksch, S., Schumann, H., & Wrobel, S. (2021). A theoretical model for pattern discovery in visual analytics. Visual Informatics, 5(1), 23–42. DOI:
  5. Bahrehdar, A. R., Adams, B., & Purves, R. S. (2020). Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city . Computers, Environment and Urban Systems, 84, 101524. DOI:
  6. Chen, S., Andrienko, N., Andrienko, G., Li, J., & Yuan, X. (2021). Co-Bridges: Pair-wise Visual Connection and Comparison for Multi-item Data Streams . IEEE Transactions on Visualization and Computer Graphics, 27(2), 1612–1622. DOI: 10.1109/TVCG.2020.3030411
  7. Das, R. D., & Purves, R. S. (2020). Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India . IEEE Transactions on Intelligent Transportation Systems, 21(12), 5213–5222. DOI: 10.1109/TITS.2019.2950782
  8. Wartmann, F., Olga, K., & Purves, R. (2021). Assessing experienced tranquillity through natural language processing and landscape ecology measures . Landscape Ecology. DOI: 10.1007/s10980-020-01181-8