Do Streetscapes Still Matter for Customer Ratings of Eating and Drinking Establishments in Car-Dependent Cities?
This provides context-sensitive urban planning insights for improving customer experiences at eating/drinking establishments, though it appears incremental in applying existing computer vision methods to a specific domain.
This study investigated how indoor/outdoor aesthetics and streetscapes affect customer satisfaction at eating/drinking establishments in Washington, DC, finding that streetscape quality's influence decreases in car-dependent areas while indoor/outdoor environments remain significant.
This study examines how indoor and outdoor aesthetics, streetscapes, and neighborhood features shape customer satisfaction at eating and dining establishments (EDEs) across different urban contexts, varying in car dependency, in Washington, DC. Using review photos and street view images, computer vision models quantified perceived safety and visual appeal. Ordinal logistic regression analyzed their effects on Yelp ratings. Findings reveal that both indoor and outdoor environments significantly impact EDE ratings, while streetscape quality's influence diminishes in car-dependent areas. The study highlights the need for context-sensitive planning that integrates indoor and outdoor factors to enhance customer experiences in diverse settings.