Slow Big Data: exploring the nexus between urban mobility and heritage through GeoAI

Starting from the new mobility paradigm (Sheller, Urry, 2006), this work explores the use of spatial Big Data in the field of urban mobility and their connection with cultural and natural heritage at the local scale. The focus is on the nexus between slow mobility and heritage in Pisa (Italy) at the intra-urban scale, employing GeoAI and big data analysis techniques. First, through the analysis of 151,110 GPS tracks of pedestrian trips recorded by the Strava Metro platform over a period of one year (2022) in the province of Pisa, we describe the most popular locations for slow mobility, and we predict the areas where people tend to linger. Second, we study in depth the case of the municipality of Pisa tocritically discuss: a) the influence of heritage on mobility spatial trends, b) the link between slow mobility and urbanheritage fruition; c) the contribution of big data and GeoAI models to analyze this nexus. The observation of these patterns is helpful in terms of policies: i) for the identification of the most used corridors and alternative solutions for wider access to urbanheritage, which also includes “minor”sites; ii) for the development of spatial models to highlight walking routes in near real-time and encourage slow mobility practices.