Exploring tourist spatiotemporal behavior differences and tourism infrastructure supply-demand pattern fusing social media and nighttime light remote sensing data

Increased tourism forms subdivide the tourism market. Therefore, distinguishing spatiotemporal behaviors of different types of tourists is of great significance for tourism marketing and planning. In this study, we constructed a Spatial-Temporal-Preference model based on spatial clustering, seasonal-trend decomposition and statistical analysis, using multi-source data. It provides a novel frame for comparing tourist behavioral characteristics from a new perspective. Meanwhile, providing a method to rapidly identify the Tourism Infrastructure Supply and Demand Pattern (TISDP) fusing nighttime light and social media data. Then the framework was applied to analyze the differences between sightseeing tourists (STs) and cycling tourists (CTs) in Yunnan Province, China. The results showed clear spatial differences in location popularity and flow data with STs favoring northwestern Yunnan, and CTs favoring eastern Yunnan. Tourist behavior varied also seasonally and tourists preferred visiting minority areas or areas with better infrastructure. The tourism industry in the study area developed rapidly after 2013, although still with an unbalanced infrastructure supply and demand. The CTs loss areas were greater in northeastern and northwestern Yunnan and south of Kunming. The proposed methods help to identify differences in tourism activities and has important implications to tourism infrastructure planning and sustainable development.