The Consistency Evalution Of The Semantic Position And The Check-in Location Of Weibo

The arrival of the era of big data is transforming people's social life,Weibo has quickly entered people's lives and developed into people's daily entertainment,dating,and information dissemination tools.Weibo is a new type of social media platform that brings new opportunities for studying on tourism behavior and tourism planning.Weibo data with location information links virtual cyberspace to real geospatial,user behavior preferences and daily behavior trajectories contained in location information have received more and more attention,but there has been less research on the relationship between location and geolocation mentioned in Weibo texts,and even fewer assessments of consistency.Therefore,this article uses Jiuzhaigou's Weibo data as the data source to study the consistency of Weibo text location and Check-in location.The specific research methods and results are analyzed as follows:(1)Processing of data.Classifying Weibo data of Jiuzhaigou from 2012 to 2017 by text content,and filtering out the Weibo data of scenic spot and scenery line,then indicating the name of the scenic spot and the name of the scenic line on Weibo to facilitate later analysis.(2)Determining the spatial scope of attractions and sight lines.For scenic spot,divide the radius of the attractions measured by Google Earth into three categories and take the average of each category,and add the 200-meter positioning error as the attraction radius,establishing the spatial extent of the scenic spot in the form of a buffer zone.For the scenery line,the spatial range of the four lines is determined by the fusion of the Thiessen polygon range of the scenic spots to form the scenery line range.(3)Calculating the consistency of the semantic position of text on Weibo and Check-in location of scenic spots and lines on a spatial scale,and visually displaying it in ArcGIS.For the scenic spots,in addition to the Wucaichi and Changhai,the scenic spots of Wuhuahai,Zhenzhutanpubu,Nuorilangpubu,Shuzhengquhai and so on,which microblog text locations has a high degree of consistency with its Check-in location,at the same time,its popularity is also high.On the scenery line,the degree of consistency is Rizegou> Shuzhenggou> Zechawagou,and Rizegou is a beautiful scene with the most dense and consistent scenes.(4)On the time scale,calculate the consistency between the semantic position of text on weibo and the Check-in location of the sights and scenic lines,and visually display it as a statistical map.On the year scale,in the sights,from 2013 to 2017,the consistency of semantic position of text on weibo and Check-in location decreased year by year,and the location consistency in 2017 was even less than 20%;On the scenery line,the consistency between the position of Weibo text and the Check-in location suddenly dropped in 2014,and the change was small from 2014 to 2017.On the monthly scale,the consistent change rules of scenic spots and scenery lines are basically consistent,.both have the highest consistency in June,and the lowest in February and March.Consistency fluctuates greatly from January to June,and there is not much fluctuation from June to December.In addition,the consistency in September is higher on the scenic spots,and the consistency in September is lower on the scenery lines.On the hourly scale,the consistency between the scenic spots and scenery lines shows a "pyramid" structure with the spire at 9-12,which is the highest in the period of 9-12,and the lowest in the period of 21-24.(5)In the space-time scale,calculating the consistency between the semantic position of text on weibo and the Check-in location of the scenic spots and scenery lines,and visually display the maps based on the consistency of scenic spots and the histogram of the consistency of horizons,and calculate the change rate between years,seasons,and time periods.In the sights: The consistency level of each attraction in that year changed from 2012 to 2017,and the consistency level fluctuated the most from 2016 to 2017.The consistency of different attractions is obvious in the four seasons,and the consistency in winter is generally low,scenic spots in Shuzhengou generally have a high degree of consistency in autumn,and the consistency of the spots in Rizegou in spring and summer is higher than in autumn.Overall,the rate of change in the consistency of scenic spots from autumn to winter is the largest,the magnitude of the difference in scenery caused by seasonal changes is directly related to the change rate of seasonal consistency.At 8-12 and 12-16,Rizhegou has a high degree of consistency,and at 16-20 Shuzhengou has a high degree of consistency,the formation of this consistency law is related to the order of tourists in the scenic area,which reflects the behavior pattern of tourists to a certain extent.In terms of the rate of change,the consistency of the scenic spots changes at different times,and the consistency of the 12-16 to 16-20 periods changes dramatically.On the scenery line: the consistency between the semantic position and the sign-in position of the four seasons of the three scenes is not obvious;the change rate of consistency in autumn and winter was the highest in shuzhenggou,while that in spring and summer was the highest in rizegou and zechawa.The rate of change of scene line consistency is more obvious between years,the change rate of scene consistency in 2016-2017 was significantly lower than that in 2012-2104 and 2014-2016.(6)Using the total microblog data,the consistent Weibo data of scenic spots and the consistent Weibo data of scenery lines to do Hot Spot Analysis,and comparing the difference between their cold spots and hot spots.It can be found that the hotspot areas where the text position of Weibo is consistent with the Check-in location have a high degree of consistency with well-known attractions.(7)Tourist gender,authentication type,registration place,emotional value,etc.also have a certain impact on the degree of consistency,and high levels of negative emotions may lead to low levels of agreement.The results of contingency table analysis shows that the relationship between the location of the Weibo's text and the Check-in of the microblog point is relatively strong.