My research is in area of spatio-temporal visual analytics, which means that I am developing new analytical and visual methods (and frequently a combination of both, to support the human and computer capabilities in data exploration process) to explore complex spatio-temporal data. My research interests include:
-Geovisual Analytics & Geovisualisation
-Geographic Information Science/Geoinformatics/Geocomputation
-Spatio-Temporal Analysis, Movement Analysis
-Spatio-Temporal Mathematical Modelling
-Information Visualisation and Visual Data Mining
Dr Urška Demšar is Lecturer in Geoinformatics at the Department of Geography & Sustainable Development, School of Geography & Geosciences, University of St Andrews, Scotland, UK. She is originally a mathematician with a degree from University of Ljubljana, Slovenia and has a PhD in Geoinformatics from the KTH Royal Institute of Technology, Stockholm, Sweden.
Active research projects:
Here are some of my recent projects:
1. Investigating eye-hand coordination in use of spatial visual interfaces
Analysis of eye movements provides insights into cognitive processes in human brain during tasks such as reading and exploration of digital displays. In human-computer interaction (HCI), eye movements are studied through eye tracking, which produces spatio-temporal trajectories of gaze direction on the screen. In this project we examine potential linkage of eye and mouse movements in visual exploration of a geographic display. We develop new analysis and visualisation methods for trajectory data to support this exploration.
2. Visualising Movement Trajectories
Recent developments and ubiquitous use of global positioning devices have revolutionised movement ecology. Scientists are able to collect increasingly larger movement datasets at increasingly smaller spatial and temporal resolutions. This project develops alternative geovisualisation methods for spatio-temporal aggregation of trajectories of tagged animals in the context of 3D space-time density. The method was developed to visually portray temporal changes in animal use of space using a volumetric display in a space-time cube.
In 2014 I became Scientist in charge at the University of St Andrews of the Marie-Curie International Traning Network (ITN) Geocrowd: Creating Geospatial Knowledge World (2010-2014). This project supports one of my PhD students, Katarzyna Siła-Nowicka.
4. Regionalisation from flow data
Recent technological advances in spatial data collection have caused an explosion of new data volumes and their availability. One of these data types are flow networks, sometimes also called origin-destination (OD) networks which are now being increasingly captured using various forms of sensor technology from bespoke system which track vehicles and passengers to smart phone locations can that can be associated with individual travellers.
5. Visualising flows
Flow data — which relate to the movement of people, goods, or other entities between locations — can be represented as a directed network, with locations (either origins or destinations) as the nodes and flows (as well as their associated attributes) as the edges. In the last decade, very large flow networks have become available, such as those relating to migration and mobile phone communication, and, as a consequence, new analysis and visualisation methods are now required. In this project we address this requirement by developing new analysis and visualisation methods for large flow networks based on recent research in physics, visual analytics, and geography.
6. Supporting model interpretation through visualisation
Geographically Weighted statistical methods are increasingly popular to understand spatial processes in situations when the data are not modelled well by a universal set of parameters but when there exist regions in the geographic data space where a suitably localised set of parameters provides a better description of the modelled phenomenon. Instead of one global model, such methods produce a different model at each location in geographic space where the local model is based on a geographically weighted subset of data. Because of the phenomenon known in information science as “the curse of dimensionality”, the magnitude of the results from local modelling techniques increases exponentially and can quickly become overwhelming in terms of trying to understand the information conveyed in the results. Exploring these large data sets of results is therefore a problem for a successful interpretation and understanding of the local method. We look at different GW methods and their spatio-temporal versions, and investigate the challenges such highly dimensional results produce in terms of visualising the results.
Tenerelli, P., Demšar, U. and Luque, S., 2016. Crowdsourcing indicators for cultural ecosystem services: A geographically weighted approach for mountain landscapes. Ecological Indicators, 64, pp.237-248.
Mansley, E. and Demšar, U., 2015. Space matters: Geographic variability of electoral turnout determinants in the 2012 London mayoral election. Electoral Studies, 40, pp.322-334.
Siła-Nowicka, K., Vandrol, J., Oshan, T., Long, J.A., Demšar, U. and Fotheringham, A.S., 2015. Analysis of human mobility patterns from GPS trajectories and contextual information. International Journal of Geographical Information Science, pp.1-26.
Demšar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., Weiskopf, D. and Weibel, R., 2015. Analysis and visualisation of movement: an interdisciplinary review. Movement ecology, 3(1), pp.1-24.
Demšar, U., Buchin, K., van Loon, E.E. and Shamoun-Baranes, J., 2015. Stacked space-time densities: a geovisualisation approach to explore dynamics of space use over time. GeoInformatica, 19(1), pp.85-115.