Color or style, which one matters more? Testing the influence of spatial characteristics on visitors’ perception and behavior via CNN-XGBoost

Urban regeneration intensifies long-standing challenges of spatial fragmentation, declining visual diversity, and weakened local identity driven by rapid urbanization globally. Urban visual features, as the most immediate layer of urban form, strongly shape perception and behavior. Yet, the distinct roles of urban architectural color and style remain poorly specified. This study addresses the central question of which visual attribute exerts stronger effects—color or style—by developing an integrated framework that combines visual computing, spatial quantification, and perception modeling. Meizhou Island in southeast China provides the empirical setting. Color attributes—complexity, harmony, saturation, and value—were extracted from street-view images, while architectural styles were identified through a Convolutional Neural Network (CNN) model. Ordinary Least Squares (OLS) regression and eXtreme Gradient Boost (XGBoost) were employed to test associations and interaction effects. Findings show that color features, particularly complexity, have higher predictive power for both perception and behavior than architectural style. However, high color complexity and saturation, while enhancing visual appeal, reduce behavioral engagement measured by visit frequency from geo-tagged user data, exposing a perception–behavior gap. Architectural style exerts weaker effects and functions primarily as a symbolic cue rather than a behavioral driver. Moreover, urban morphological characteristics, especially functional diversity, significantly moderate how visual attributes influence outcomes across different levels of complexity. The findings highlight the need to integrate color strategies into regeneration, and to align visual expression with spatial function to foster perception- and behavior-driven urban interventions.