The Research On C2C E-shops’ Spatial Distribution And Its Influencing Factors

With the continuous improvement of information network technology and means, the role of e-shops in the form of a new trade style, becomes a new field of business development. As a new commercial carrier, e-shops show an increasingly closer relation with the development of national economy. More and more customers are engaged in shopping online, which brings certain shock to traditional commercial outlets, and proposes a new question to the traditional commercial location theory. In geography, people put more focus on space location characteristics, the analysis of the causes of the distribution phenomenon are still far behind. Based on Spatial econometric geography, I choose Taobao Net as the research object, studying the present situation of spatial distribution and characteristics of e-shops for different levels nationwide by using geographic analysis tools like ArcGis、GeoDa、Spss, etc. The global and local spatial autocorrelation analysis in the spatial distribution of the electronic shops estimate if there exists the phenomenon of spatial autocorrelation, then I run the OLS classic regression, as well as spatial lag model and spatial error model respectively to select the optimal model in favor of further instructions for influencing factors The main contents and results of this article are summarized as follows.(1) Using ArcGis to make different regional classified statistic graphs, grasp the present distribution of regional e-shops in China. Average and Range method, standard deviation, mean square deviation, deviation coefficient, location entropy index and other mathematical statistical methods are used to analyze the regional differences and the degree of specialization in this paper. The results show that the absolute difference of e-shops in the central and east areas is greater than the western regions, but the relative difference is less than the western region; The development of e-shops in each urban agglomeration is not balanced, the three big urban agglomerations in eastern China have a greater competitive advantage; E-shops overall showed a trend of agglomeration and specialization development.(2)Utilizing GeoDa for spatial autocorrelation analysis of e-shops in order to obtain the global Moran’s I value and the P value which shows its significance level. The Lisa figure of gathering generated from local autocorrelation can measure local spatial relationships between each area and its neighboring areas to estimate the hot and cold spot regions with the development of e-shops at present. The results show that e-shops have a positive spatial relativity based on the weight of rook contiguity, but the correlation is still in a low autocorrelation stage. Currently, Jiangsu and Fujian provinces are the hot spot regions in the development of e-shops, on the contrary, most western regions are still considered as the blind areas where their potential remains to be further tapped.(3) Setting the quantity of e-shops as the dependent variable, choosing another nine indexes as independent variables including logistics network number, residents’ income, degree of dissemination of the internet, etc. Running OLS classical model, spatial lag model and spatial error model respectively and comparing the significance of LM and R-LM, I finally select the spatial error model as the optimum selection. The study finds that the quantity of logistics networks, residents’income, internet popularity and total retail sales of social consumer goods have positive correlation effects on the quantity of e-shops, the increase of primary industry is not favorable to the development of e-shops, at the same time, the development of e-shops can be accelerated by stepping up the secondary industry and tertiary industry especially financial sector, industries of trade and commodity circulation and the transportation industry.