| The popularity of social networking and location-based services (LBS) has changed tourists' traveling behavior. Before the trip, through the network tourists obtain information, purchase tourism products. During the trip, tourists use social networks to get location information and tourist guide service. Then after their trip, tourists use social media to share experience and comment. The whole process of travel produce a lot of spatial-temporal information (data), which constitute the "Tourists'Digital Footprints", also can reflect the tourist spatial behavior trajectory. At the same time, the development of information and communication technology promotes the obtaination of "Tourists'Digital Footprint". In this environment, it's possible to use social media and mobile internet data to study tourists' spatial-temporal behavior.Under the guidance of the tourist behavior theory?theory of time geography? spatial and temporal data mining theory, the related literatures home and abroad had been sorted out firstly, then determined to study tourists' micro scale space spatial-temporal behavior based on multiple data sources. This article selected Mount Hua as a case, using Sina Micro-blog data? travel notes data and field survey data, using mathematical statistics method and visualization method and analyze tourists' space-time behavior, in order to build the spatial-temporal behavior patterns and enrich the tourist spatial behavior research content and research methods, to explorethe strengths and weaknesses of these data in the course of the study in micro scale space spatial-temporal behavior. Main research content as follows:(1) Using micro-blog data to study the time behavior of tourists is scientific and representative. Through the comparative analysis of the variation of number of microblog and Huashan Scenic Area official microblog released real-time monitoring of tourists amount of data analysis results. It is found that both of them show a high degree of similarity.At the same time, the field investigation in observation results and micro data analysis results are basically the same.(2) The behavioral variation of tourists in Mountain Hua shows a "3M" mode. Tourists were most active when they just enter the scenic area, reach the summit, see the sunrise and to leave the area.In the future, in the study on tourist behavior can pay more attention to these special points in time.(3) Mountain scenic tourist space reflects the preferences of the transport hub and the strong attraction tourism node. Scenic spots and a strong attraction of the tourist node is a hot area of tourist space distribution. Mount Hua Tourist time-space distribution rules analysis results show that, as an important transportation hub in Mount Hua, during the operation of the ropeway, ropeways of west peak or north peak under the station and the surrounding region tourists are a large number of agglomeration.At the same time, north peak is an important transportation hub in Huashan Scenic Area. The tourist node which has tourist attraction is also tourist attraction.The innovations in this article lay in the choice of data firstly. Different from traditional research, we try to select tourists' tourism digital footprint and traditional field survey data to study tourists' spatial-temporal behavior, and attempt to using a variety of data (micro blog data, travel notes data, survey data) to support each other, complement each other, the research ideas and research methods are innovative. Secondly, this study firstly attempted mining tourists' travel paths which contained temporal and spatial information from travel notes, using the method of Markov chain, analyzing tourists' spatial-temporal behavior patterns, finally obtain tourists' travel flow with time elements. This study enriched the research methods and content of tourists' micro scale space spatial-temporal behavior. In this paper, the research perspective and methods have reference value for the study of tourists' micro scale space spatial-temporal behavior. |
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