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The data based on location/activity sensing technology is exploding and integrating multi-source data provides us with a new perspective to observe tourist behavior. On the one hand, tourist preferences can be extracted from the attractions generated by clustering. On the other hand, potentially extracted tourist information can provide decision-making support for tourism management departments in tourism planning and resource development. Therefore, developing smart tourism services for tourists and promoting the realization of “smart scenic spots.” A field survey was conducted in Zhongshan Botanical Garden, China, from 3 February to 3 April 2019. This empirical study combines a handheld GPS tracking device and questionnaire survey using SEE to optimize k-means clustering algorithm and explores the spatial–temporal behavior patterns of tourists. The results showed that tourists in the botanical garden could be divided into three behavioral patterns. They are recreation and leisure, birdwatching and photography, and learning and education. The spatial–temporal behavior patterns of different tourists have obvious differences, which provides a basis for the planning and management of smart scenic spots.

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Tourists’ Spatial–Temporal Behavior Patterns Analysis Based on Multi-Source Data for Smart Scenic Spots: Case Study of Zhongshan Botanical Garden, China