Mapping destination images and behavioral patterns from user-generated photos: a computer vision approach

Destination image studies were traditionally based on questionnaire surveys, but the recent rise of user-generated content and social media big data analytics provide new opportunities for advancing tourism research. This study adopts one of the latest artificial intelligence computer vision technologies to identify the differences in the perceived destination image and behavioral patterns between residents and tourists from user-generated photos. Data were mined from Flickr, which yields 58,392 relevant geotagged photos taken in Hong Kong. The findings reveal that the perceptual differences between the two groups lay on seven types of perceptions. The differences in spatial distribution and behavioral trajectory were visualized through a series of maps. This study provides new insights into the destination image which has implications for the tourism promotion and spatial development of the destination.