Multi-Source Data Integration for Safety Evaluation of Walking Tourism Routes: Coupling Spatial Analysis of Attractiveness and Carrying Capacity in Macao

The safety of “City Walk” routes in high-density historic districts is a critical constraint for sustainable urban tourism. This study establishes an integrated safety assessment framework for Macao’s eight walking routes by coupling tourism attractiveness with spatial carrying capacity. Utilizing social media big data, multi-source spatial datasets and Spatial Lag Models, we conceptualize “attractions” and “streets” as a continuous system. The results reveal a spatial mismatch: while entertainment and green streetscapes drive attractiveness, excessive amenities in narrow alleys reduce perceived safety. A “crowded core–empty periphery” capacity pattern creates significant risks, with approximately 39% of nodes classified as medium-to-high risk due to high attractiveness overloading low carrying capacity. By diagnosing these “high-attractiveness, low-capacity” conflicts, this study demonstrates the effectiveness of multi-source data fusion in identifying resilience weaknesses, offering actionable insights for smart tourism management and the promotion of social sustainability in high-density destinations.