This paper proposes a data-driven methodological framework for the analysis, simulation, and design of public space through predictive survey techniques. The research integrates digital survey methods, big data acquisition, and artificial intelligence to construct a three-dimensional model capable of supporting decision-making processes in urban design. The approach is structured into four phases: historical and contextual analysis, morphometric data acquisition through photogrammetry and LiDAR, digital model construction, and predictive simulation.
The study develops a digital twin environment in which heterogeneous data—collected from field surveys, low-cost sensors, and human-generated sources—are integrated and analyzed. As illustrated in the workflow diagram (Fig. 1, p. 705), the system combines physical and digital datasets to simulate future scenarios and evaluate public space quality. The methodology incorporates both quantitative metrics and qualitative assessments based on Gehl’s criteria, enabling the interpretation of spatial use, environmental conditions, and user behavior. The results demonstrate that predictive survey, combined with IoT and machine learning techniques, can support the design of responsive and adaptive urban environments, while highlighting current limitations in data acquisition, integration, and systematization.