Research On Spatial And Temporal Pattern Characteristics Of Mount Emei Network Concern

The rapid development of information technology,Internet of things,PC,mobile Internet of things,big data and blockchain become the dominant force of social development,which has an important influence on our life.It is of great significance to study how to use big data to better integrate into the big data era.Big data comes into being with our daily life,and government departments and tourism scenic spots devote a lot of manpower and material resources to the exploitation and utilization of tourism big data.Better promotion of the development of modern scenic spots.In this thesis,Baidu Index was used as the data source.The provincial network and the administrative region of the city were used as the units to collect the daily attention index of Emeishan Scenic Area from the overall end,mobile end,and PC end from2012 to 2016 respectively..Spatial structure theory was used to analyze the temporal and spatial distribution characteristics and influencing factors of Emei Mountain.Time distribution: Seasonal and annual changes are analyzed using the seasonal index,seasonal concentration index,and interannual change intensity index,and the characteristics of weekly and holiday changes are also summarized.Spatial analysis:Using GIS technology to visualize the degree of attention of the Emeishan network with provinces and regions as units,and using the geographical concentration coefficient,Theil index and its decomposition to study the spatial distribution and balance,and discussed the influencing factors of time and space distribution.The main conclusions include:(1)distribution characteristics of network attention time:Inter annual change: the degree of concern of mobile terminal network is increasing,the PC end is declining,the increase of mobile terminal is greater than that of the PC end,so it is in line with the trend of increasing attention of the whole network.Seasonal distribution: the degree of attention of mobile network and the whole terminal is more than that of PC.Weekly distribution: three ports show a certain weekly distribution precursor effect,and work day network concerns more than rest days.Golden Week: three ports and eleven peaks are significantly higher than May 1.(2)Spatial distribution characteristics of network concernsThe spatial distribution and interannual variation of network concern are different in provinces and regions.The three ports' network attention has always been the highest value in the province.The growth of the adjacent provinces in the whole and the developed coastal areas is obvious,and the attention of East China,southwest and North China is high,and the attention of northwest,northeast and Southern China is low.There is a certain increase in the network attention of mobile terminals,but the magnitude is smaller than the overall end.The changes in the PC end are large and the region is consistent.Concentration and differentiation: the geographic concentration of the whole end and mobile terminals is high,and the PC ends are always low.The inter provincial difference between the whole terminal and mobile phone is bigger and bigger,and the inter provincial difference of the PC terminal is relatively small.The difference between the overall end and the mobile terminal area is rising.The differences in the three ports are rising.On the whole,the difference between the mobile area and the global end is much larger than that of the PC side.The difference between the eastern developed area and the PC end area has increased significantly.The difference between the mobile terminals and the developed areas is quite obvious.The inter provincial difference of the whole end is mainly composed of seven regional differences.The difference between mobile and PC terminals is mainly caused by regional differences.(3)influencing factors:Time distribution is mainly influenced by holidays,seasonal changes,natural disasters,climate and other factors.The three ports of spatial distribution are not consistent with the number of travel agencies,per capita GDP,and space distance factors.