Visualisation & auralisation
The use of digital tools and open data is widely promoted to support inclusive urban planning and transformation. In particular, visualisation and auralisation provide unique opportunities for supporting informed design decisions in urban planning and analysis.
Visualisation provides ways to enhance communication, to effectively study planned environments, and contributes to recognition of patterns and explanations of complex relationships. Visualisation is here addressed in a broad sense and comprises digital tools and approaches which are based on, for example, 2D/3D visualisations, geovisualisation, modelling and gaming. Auralisation can be seen as the process of simulating a listening experience. Being able to listen to a planned environment before it has been built is not only informative for decision makers at all levels, including citizens, but can also be used as input for further computer-aided analysis and design.
The main challenges faced in urban visualisation and auralisation modelling are access to information, multiple-scale data in visual analytics, as well as the variety of data available from diverse domains with different and incompatible formats. It is therefore necessary to manage large volumes of data from various sources, to integrate traditional datasets, open data and crowd-sourced data: as well as to integrate qualitative and quantitative data. Another challenge consists of representing different kinds of data in the same system and creating comprehensible visualisations. A comprehensible visualisation/auralisation must include aspects of the visible physical environment (built structures, traffic, water levels); invisible aspects (air pollution, sound/noise and energy distribution); and data concerning social and societal aspects, such as safety, health and wellbeing. In addition to challenges related to data management and representation, auralisation may be computationally expensive and relies on a combination of high-performance computing, model simplification and model order reduction.