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
- Human-Computer Interaction
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 HCI, usability of interactive displays is also evaluated through mouse movements. These can be collected accurately, easily, remotely and on a large scale to uncover differences in how users experience a particular display. In absence of costly eye tracking tests, mouse trajectories are used as a proxy for users’ perception of display. 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.
This project is a collaboration with Arzu Çöltekin (University of Zurich, CH) and Jed Long (University of St Andrews). The work is supported by a grant from the Royal Society (2013-2014), on which me and Dr Çöltekin are Co-Investigators.
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. These data consist of trajectories in space and time, represented as time series of measured locations for each tagged animal. Methods for analysis and visualisation of such data have been developed for much sparser and smaller datasets obtained through very high frequency (VHF) radio telemetry. They focus on spatial distribution of measurement locations and ignore time and sequentiality of measurements. 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.
This project is a collaboration with Kevin Buchin (Technical University of Eindhoven, NL), Emiel van Loon and Judy Shamoun-Baranes (both from University of Amsterdam, NL) started through COST Action MOVE (IC0903, 2009-2013).
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. Examples are transportation networks (e.g. flows of passengers between subway stations), migration/commuting networks and mobile phone communication networks. In the geographic tradition which can be traced back forty years at least, one of the uses for flow networks has been in context of regionalisation: flow information was used to derive regions of functional interaction between origin and destination locations. However, methods for regionalisation from flow data have been forgotten after a start in 1970s, due to the lack of capabilities in computing power. In this work we revive these half-forgotten methods and develop new ones based on old work and network methods from complex networks research in contemporary physics.
This is a collaboration with Jon Reades (King’s College London) and Ed Manley and Mike Batty (CASA, University College London). It was intitated within the COSMIC: Complexity in Spatial Dynamics (ERA-NET on Complexity, 2010-2012) project, in which I was a Co-Investigator and continues past the project’s end.
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; methods that should also account for the spatio-temporal dynamics of these networks. 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.
This project is a collaboration with Iain Dillingham (University of St Andrews).
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. In this project we suggest that this task could be facilitated by geovisual analytics. 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. The task of adequately exploring such large and complex data sets of results emanating from spatio-temporally weighted statistical methods requires new approaches in data visualisation and visual analytics.
This was a Science Foundation Ireland (SFI) Research Frontiers Programme project on Combining Spatial Statistical Modeling and Geovisual Analytics: the example of Geographically Weighted Local Statistical Methods (2009-2013).
7. StratAG – Strategic Research Cluster in Advanced Geotechnologies
During my time in Ireland I was part of the Strategic Research Cluster in Advanced Geotechnologies (StratAG) (2009-2012). In this cluster I ran a collaborative project with the Geological Survey of Ireland (GSI) on the topic of Improving Seabed Classification from Acoustic Data using Machine Learning and Visual Data Mining. I was the head of the research group consisting of three StratAG academics, two StratAG postdocs, two StratAG PhD students and two external collaborators from GSI. In addition, one of my PhD students received his PhD on a topic related to this work.