Street network or functional attractors? Capturing pedestrian movement patterns and urban form with the integration of space syntax and MCDA

The space syntax approach has been widely applied in urban studies and practices to investigate urban form and predict pedestrian movement patterns. Prior research has identified the 'circular causality' associated with the ontology of space syntax, indicating that network structure and functional attractors are mutually reinforced by movement patterns in urban spaces. Using the city of Xi'an as a case study, this research aims to deepen the understanding of urban form and human movement patterns from a new perspective that integrates the space syntax approach with multi-criteria decision analysis (MCDA). To quantify the city's attractors and gauge their impacts on its urban form, this research uses available open urban datasets, including street networks from OpenStreetMap (OSM) and Point of Interest (POI) data. Facilitated by sensitivity analysis (SA), the findings suggest that the functional distinctiveness of districts plays a nuanced role in attracting and generating pedestrian flow. It is therefore essential to introduce a 'real-time' representation of urban spaces to supplement our conventional understanding.