Digital twins attract significant interest in urban research. Public and private stakeholders show strong expectations about how, by engaging in new technologies, sensors, data and models that interact in real-time with citizens, the functioning of cities can be improved. Recurring use cases are trip routing, air pollution and health-related information or improvement of urban infrastructures.
While the value of smart datasets and the usefulness of digital ‘mirrors’ seem clear for an efficient monitoring of flows (infrastructures or energy) or for short-term processes (quick daily decisions or understanding human reactions to urban stressors), they are confronted with a three challenges. First, it is unclear how those digital tools can engage with mid/long-term social and environmental processes, which sustainable planning policies need to address (Batty, 2018). Second, as new data emerges with increasingly higher spatial and temporal resolution, methods that can cope with these dimensionalities are required (both for analysis and for parsing information to citizens and stakeholders). Third, there is still a lot to learn about how to trade-off different degrees of virtualisation. In practice, there is a continuum between the immersive virtual realities (VR) which can be parameterised to test scenarios, or the augmented reality (AR) approach, which cannot adapt to context but adds ‘real’ information or simulated stimuli through wearables. The question of what are the best research methods applicable to a particular urban planning question that pertains to mid/long-run processes is still difficult to answer and the methods are rarely compared.
In this project, we propose to systematically compared VR and AR results to address a particular planning question (to be defined). The project will aim to address three main research questions:
- To what extent do approaches using VR and/or AR environments contribute to a more accurate and effective assessment of the urban environment?
- To what extent can richer and/or larger datasets, of different nature (e.g. alphanumerical and graphical data) be used to deliver useful analytical and simulation tools?
- How can these methods be coupled with more traditional engagement methods to create effective decision-support tools for urban planning and urban design?
The thesis will specifically assess the perception and the valuation (in monetary terms) of given urban features (e.g. density and greenness) using two approaches: (i) an immersive VR landscape derived from open LIDAR data and Google Street View onto which variations of urban properties (e.g. greening or building heights) will be simulated, and (ii) a walking field experiment where digital information (socio-economic, price information,…) is added to complement the user’s perception. The research will be applied to two case studies, Belval in Luxembourg and the University of Manchester South Campus neighbourhood.
The project will make use of different datasets with geographical and numerical data, including (but not limited to) digital maps, Google Street view imagery, LIDAR data, local area data and other relevant data to characterise urban environments. The project will be associated with the LISER Geo Data Center and the Prototype Belval project and make use of existing VR technology in both LISER and UoM.
Batty, M. (2018). Digital twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817–820. https://doi.org/10.1177/2399808318796416
Project reference: MN42
Deadline: 14th April