Using Crowdsourcing Geographic Data For Spatio-Temporal Diversity Analysis Of Tourists’ Perception

The efficient management of tourists attraction and the sustainable development of the tourist market need the information of tourist perception as the auxiliary decision data.In the era of Web2.0,the crowdsourcing geographic data generated by social media users is rapidly accumulating.The temporal and spatial geographic data can provide tourists’ real travel behavior information.This paper applied the crowdsourcing geographic data to the field of tourism management,maked up for deficiencies in traditional tourist surveys,provided a new perspective,new ideas and new methods for traditional tourism research.Taken the Baotu Spring Park of Jinan as the research object,the main research results were as follows:First,called the social media API(Application Programming Interface)interface and website reviews crawler to obtain the user’s generated geographic data related to the Baotu Spring Park.With the help of geographic information data,using the method of kernel density,the hot spot region of tourists were presented in different spatial scales,and analyzed the tourist daily travel regularly;collected tourists home location information,analysis and visualization,found that Baotu Spring scenic tourists mainly located in the range of 500 km,Beijing,Shanghai,Guangzhou,and other economically developed areas,using the geographic concentration index,the phenomenon of tourist market concentration was analyzed.At the same time,the influence of the "time and space compression" effect on the tourists attraction of the high speed railway was discussed,and the location of the potential tourist destination of the Baotu Spring Park was found.Secondly,using the text word segmentation,word frequency statistics and semantic network analysis technology,we processed and analyzed the geographic data of the public,obtained the overall perception impression and perception model of tourists to Baotu Spring Park,and made clear the tourists’ hot spots in the travel;using LDA(Latent Dirichlet Allocation),K-means topic clustering technology,the geographic data obtained were clustered according to the theme.In the time span of this study,the hot topics and hot topics of the tourists were quickly extracted.and the important events in the scenic spots are reflected in a centralized way,tourists’ comments and views on hot topics have been concentrated,the heat change trend of different hot topics and hot topics was intuitively expressed.Finally,on the basis of the above research work,classified the tourist reviews according to the home location and hot topics,on the spatial scale and the time scale,a dictionary based sentiment analysis method was used to calculate the tourists’ sentiment differences of different home location,the overall sentiment fluctuation of the tourists,and the tourists’ sentiment fluctuation on the specific topic events.From natural conditions and social humanities,combined with the characteristics of Baotu Spring Park,the factors affecting tourists’ emotional changes were analyzed.According to the characteristics and rules of the tourists’ reviews found,tourist attraction management methods and countermeasures were put forward,in order to contribute to the sustainable development of tourism industry.Through the application of crowdsourcing geographic data to the Baotu Spring Park,this paper proved its significance and potential value in the management of tourist attraction The results show that a series of research methods used in this paper can be used to find potential tourist sites,attract potential tourists,extract tourists’ attention,improve tourist satisfaction and improve the quality of network evaluation,thus promoting the sustainable development of tourism destinations.