Abstract
The article uses visual Large Language Model-enhanced street-view analysis to examine day–night heterogeneity in the relationships between street green-space features and crime incidents. It combines fine-grained visual interpretation of streetscape environments with temporally differentiated crime patterns to assess whether the same green-space characteristics are associated with different urban-safety outcomes during daytime and nighttime.