Dataspace: Predictive Survey as a Tool for a Data Driven Design for Public Space

The decision-making process based on the interrogation and prediction of Big Data is a research topic that has animated the interest of many fields of investigation in recent decades. Among these, the architectural field has also participated in this widespread interest involving, in particular, the disciplines of Drawing and Representation. In this context, technological progress has transformed the way of knowing and investigating architecture with research approaches characterized by a multidisciplinary vision. The use of these new technologies has in fact developed new ways of thinking about architecture while representing, at the same time, a key factor in the process of analogy-digital transition still underway. In this mixture of physical and digital fits the theme of city models that follow development strategies based on Artificial Intelligence and Internet of Things applications that allow for the processing and analysis of the enormous volume of data they generate. It is from this assumption that this contribution aims to analyze the quality of public space by defining, through survey techniques, a three-dimensional model in which to simulate a series of heterogeneous data collected through field surveys. The aim is to define a future sensors system to support a design based on real data and, therefore, a prototype of a forecasting model. Topics that are trendy in the predictive survey that associates morpho-metric information with a continuous flow of heterogeneous data obtaining what is commonly called Digital Twin.