Environmental assessment in high-density urban areas faces significant challenges due to complex building morphology and the Modifiable Areal Unit Problem (MAUP). This study proposes a morphology-adaptive computational framework that integrates the Homogeneous Unit of Building Morphology (HUBM) with geospatial modeling to enhance environmental assessment processes. Using Macao as a case study, the framework quantifies local and accessibility-based ecosystem service flows and evaluates ecological resilience via ecological security patterns and spatial elasticity indices. The results demonstrate that HUBM substantially reduces MAUP-induced biases compared to traditional grid-based approaches, maintaining statistical significance in spatial clustering analyses across all scales. Functionally, ecosystem service value (ESV) analysis reveals that natural green spaces provide more than three times the total ESV, predominantly offering regulating services, while artificial green spaces primarily deliver localized services. Accessibility analysis highlights considerable spatial inequities, with natural green spaces exhibiting a significantly higher recreational accessibility index. In terms of ecological security patterns (ESPs), natural green spaces function as core ecological patches, while artificial green spaces dominate connectivity, accounting for 75% of corridor length and 86% of node density. Natural green spaces exhibit significantly greater ecological resilience. These findings highlight the complementary roles of natural and artificial green spaces in dense urban environments and underscore the need for adaptive spatial analysis in urban planning.

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.