Spatio-temporal Changes of Tourists Based on Multi-source data in Chengdu

Abstract

The popularity of mobile internet accelerates the dissemination and communication of information and also changes the way tourists obtain information. Tourists no longer rely on the officially published travel brochures and TV programs to obtain tourism information. Through Twitter, Sina Weibo, Facebook and other We-Media channels, tourists can get first-hand information about the tourist destination. A large number of GPS trajectory data, such as taxi trajectory data and mobile signaling data, are generated through the widely existing GPS sensors and have been widely used in traffic and resident travel research. Since tourists are not familiar with the road distribution and traffic rules of the destination city, taxi car is an important travel method for non-local tourists to choose, and its OD(origin-destination) points reflect the travel needs and travel characteristics of tourists. Therefore, this paper applies the taxi data to the tourism research. In our study, CFSDPF clustering algorithm is adopted to cluster Sina Weibo data to form tourism ROI (region of interest), and the tourism ROI is used to cluster taxi OD data. The travel characteristics of tourists can be fully and accurately reflected through multi-source data. From two different scales of citywide and central city, we can comprehensively analyze the relationship between the travel characteristics of tourists in chengdu and the tourism ROI.