Exploring the social life of urban spaces through AI

Abstract

We analyze changes in pedestrian behavior over a 30-y period in four urban public spaces located in New York, Boston, and Philadelphia. Building on William Whyte’s observational work, which involved manual video analysis of pedestrian behaviors, we employ computer vision and deep learning techniques to examine video footage from 1979–80 and 2008–10. Our analysis measures changes in walking speed, lingering behavior, group sizes, and group formation. We find that the average walking speed has increased by 15%, while the time spent lingering in these spaces has halved across all locations. Although the percentage of pedestrians walking alone remained relatively stable (from 67% to 68%), the frequency of group encounters declined, indicating fewer interactions in public spaces. This shift suggests that urban residents are using streets as thoroughfares rather than as social spaces, which has important implications for the role of public spaces in fostering social engagement.