UrbanGraphica: Digital Twin for Estimation of Public Spaces Quality

The paper describes methods and process of creating a digital twin of the city on case study of Bratislava called UrbanGraphica. The process is divided into three stages. First stage includes collection of data and modelling of the digital model of the city. The second stage integrates a wide range of information layers from various sources into the model. These collected information layers include data on traffic, vegetation, noise, solar irradiation, shadows, key viewpoints and temperature. In the third stage, these diverse datasets are overlaid to enable a comprehensive scoring system aimed at quantitatively assessing the quality of public spaces. Subsequential validation of this quantitative assessment is based on the comparison with maps of public sentiment, which were obtained from city inhabitants through questionnaires available as open data. This comparative analysis may reveal correlations between the physical and social parameters of the city. Furthermore, these integrated datasets enable the development of advanced machine learning models capable of predicting the popularity of public spaces based on their measurable characteristics. These predictive models are possible to be used to evaluate and refine the design of future public spaces during the planning stages, thereby improving decision-making processes. Additionally, the created digital twin is also utilized for estimating the potential for solar and wind energy production and utilization, thus supporting sustainable development goals of the city. The created digital twin was already published as physical model and as online digital model. Collected data from various sources into one platform provides more comprehensive image of the city. Moreover, utilization of data analytics and machine learning leads to more responsive and sustainable urban environments, contributing to the well-being of the city inhabitants.