In recent years, there has been a proliferation of ‘big data’ with a range of parameters and sources, such as location and time data spanning from historical archives to present-day mobile devices, sensors and satellites. This growth has been met with the parallel rise of ‘data science’, which aspires to uncover meaningful patterns in such data. However, richly dynamic interaction between humans and data is often avoided in cases where common knowledge is sufficient for sense-making or translating analysis into action. Consider the main use case for an automotive satnav: a driver requests the best route to a location and simply follows whatever answer is given.
There is a contrasting approach in which human expertise is incorporated in the discovery and understanding of relevant meaningful patterns in complex data. There are ‘human-in-the-loop’ systems that may use AI, ML, or related techniques. In addition, both data visualisation and interaction design can play important roles in interfacing with complex data. An extensive history of human factors research (including HCI, affordance research in psychology, sociotechnical systems analysis, etc.) has sought to understand how people with specific requirements can interface effectively with information sources, to gain insights and/or facilitate informed decision-making.
Research into interfacing with complex data can be integrated with a variety of fields. Examples include the arts (e.g. interactive musical instruments), education (e.g. learning analytics dashboards), and a number of GIS contexts ranging from aeronautics (e.g. air traffic control) to ecology (e.g. spatial ecology, conservation) to digital/spatial humanities (e.g. history, literature). These fields may use conventional interfaces for basic applications, but each field also presents opportunities for pioneering data exploration interfaces in which human expertise (in the associated field) is necessary for analysing data from multiple angles to gain insights.
Insights gained in this way may inform contributions to knowledge in the associated field, such as breakthroughs in ecology or history made possible by a specialised interface to spatiotemporal data. Or, such insights could support informed decision-making, as when a firefighter, crop farmer or policymaker is empowered to discover and act on previously unconsidered evidence. Both scenarios indicate that research into interfacing with complex data must have a firm grasp on the requirements of domain experts, as well as general principles rooted in an understanding of computing, human factors, and related systems.
If you are interested in doing a PhD conducting research on interactive interfaces that:
please get in touch with Dr Adam Linson (firstname.lastname@example.org) to discuss your research proposal.
Depending on the research context, there is a strong possibility for having a PhD supervision team that includes supervisors from the relevant integrated discipline (e.g. environment/earth/ecosystem sciences, music, history, literature, education, etc.).
Ideally you will be familiar with qualitative and quantitative methods of research. Knowledge of the interaction design process and prototyping skills would be highly advantageous; alternatively, you will have a keen willingness to develop these as required to conduct the proposed research. You will have knowledge of the domain in which you want to conduct the research or the ability to quickly develop such knowledge to the required extent.
Background reading (not required):
Examples from within the OU
Elton Barker, Kyriaki Konstantinidou, Brady Kiesling, Anna Foka. (2023).
Journeying through Space and Time with Pausanias’s Description of Greece
Literary Geographies, Special Issue 9.1, 124-160
Brock, Michelle D. and Langley, Chris R. (2019).
Mapping the Scottish Reformation: Tracing Careers of the Scottish Clergy, 1560-1689.
International Review of Scottish Studies, 44 pp. 27–34.
Project blog and interactive map:
Mercure, J.F., Pollitt, H., Bassi, A.M., Viñuales, J.E. and Edwards, N.R. (2016).
Modelling complex systems of heterogeneous agents to better design sustainability transitions policy.
Global environmental change, 37, pp.102-115.
[science-policy interface; environmental policy assessment; climate change mitigation; complexity sciences; behavioural sciences; socio-economic systems; transport]
Kaliisa, R., Gillespie, A., Herodotou, C., Kluge, A., Rienties, B. (2021).
Teachers’ Perspectives on the Promises, Needs and Challenges of Learning Analytics Dashboards: Insights from Institutions Offering Blended and Distance Learning.
In: Sahin, M., Ifenthaler, D. (eds) Visualizations and Dashboards for Learning Analytics: Advances in Analytics for Learning and Teaching. Springer, Cham.
Examples from outside the OU
D'Urban Jackson Tim, Williams Gareth J., Walker-Springett Guy and Davies Andrew J. (2020).
Three-dimensional digital mapping of ecosystems: a new era in spatial ecology
Proc. R. Soc. B. 287:20192383
MultiSat4Slows system for detecting and assessing potentially active landslide regions: Initial results from an ongoing interdisciplinary collaboration
M Sips, M Vassileva, D Eggert, M Motagh
Workshop on Visualisation in Environmental Sciences (EnvirVis) (2023).
S. Dutta and K. Feige and K. Rink and D. Zeckzer (Editors)
For more information about this project, please contact Dr Adam Linson.