This study presents Street Review, a mixed-methodology approach that combines participatory research and AI-driven analytics to assess social, demographic, and cultural changes in the use of urban public space. Twenty-eight Montreal residents participated in semi-structured interviews and image evaluations, supported by the analysis of approximately 45,000 street view images collected from Mapillary. Street Review generated visual analytics, such as heatmaps, that correlate users' subjective assessments with physical attributes such as sidewalks, maintenance, green space, and seating. The results reveal differences in perceptions of inclusivity and accessibility across different demographic groups, demonstrating that machine learning models can be improved through careful data labeling and co-creation strategies that incorporate diverse user feedback. The Street Review framework provides a systematic method that urban planners and policy analysts can use to inform the planning, policy development, and management of public streets.
