Digitally mediated accessibility: A metric combining human perception and generative AI

Abstract

Accessibility metrics often fail to align with actual human behavior due to incomplete spatial knowledge and perceptual biases. The digital era has intensified this gap. Platforms like real-time navigation and social media fundamentally reshape how people acquire information and perceive their spatial options. However, conventional accessibility metrics overlook this digital mediation and struggle to capture large-scale human perception. This study bridges this gap by proposing a novel framework to analyze accessibility through the lens of digital information acquisition and perception. Focusing on discretionary activities, we use restaurant access in Shenzhen as a case study. Specifically, we leverage data from Baidu Map (navigation) and Dianping (ratings) to quantify digitally acquired attributes like travel time, price, and reviews. We then employ a two-stage method to model public perception: first, a human survey identifies how people perceive these digital attributes; second, these findings are integrated with Generative AI (GenAI) in a few-shot learning approach to model city-wide perceptions. Finally, these perceptions are incorporated into the calculation of the digitally mediated accessibility metric, which integrates digital information acquisition and perception. Our findings reveal that the digitally mediated accessibility metric uncovers geographic inequalities in restaurant access that conventional metrics overlook. This research advances accessibility theory by introducing a framework for quantifying digitally mediated accessibility and demonstrates the potential of GenAI in scaling human perception modeling for spatial analysis.