Study On The Spatial Pattern And Influencing Factors Of Alibaba's E-Commerce Development Level

Under the situation of information globalization and economic globalization,the rapid development of electronic information industry and the continuous innovation and upgrading of information networks and gradually become an emerging form of commercial trade,and with its strong competitive advantage,it has rapidly spread to international business.The e-commerce industry has effectively promoted the development of industries such as distribution,manufacturing,logistics,broadband,and payment,with new services,new markets and new economic organization,the traditional economy has been transformed and upgraded,which has promoted the sustained and steady improvement of China's GDP.And e-commerce continues to play a role in reducing social transaction costs,improving the allocation of social resources,promoting the development of new commercial civilization and social division of labor.Therefore,it is necessary to further pay attention to and research on e-commerce.From the perspective of spatial econometric geography,this paper takes Alibaba e-commerce as an example,and takes 285 prefecture-level cities provided by Alibaba Research Institute as research objects.Based on the measurement data of e-commerce development level,using geo-analysis software such as ArcGIS and GeoDa.The analysis software analyzes the spatial differentiation pattern of Alibaba's e-commerce development level,and explore the e-commerce development level of 285 cities in space by using exploratory spatial data analysis.Whether there is spatial autocorrelation in the distribution,the OLS classical regression model,spatial lag model(SLM)and spatial error model(SEM)are used to compare and analyze the regression results of the model,and the optimal model,the spatial error model,is selected to influence the influencing factors.Explain that the geographically weighted regression model is used to further analyze the influencing factors.The research shows that from the overall perspective,the overall development level of e-commerce is low,and the geographical distribution is significantly different.The development level of the southeast coastal areas is generally high,and the development level of e-commerce in the inland areas such as the central and western regions is generally low.E-commerce has strong spatial autocorrelation in space,showing the diffusion characteristics of "big gathering and small dispersion".The influence factors have different effects on the development level of urban e-commerce,and there are obvious spatial differences in the degree of influence.The population size has a certain negative restraining effect on the development level of e-commerce.The remaining influencing factors have positive effects on the development level of e-commerce.The degree from the largest to the smallest is the degree of Internet popularization,transportation and logistics environment,education level and economic development level.And information infrastructure.This article is divided into five chapters:The first chapter is the introduction,introduces the research background and significance of the thesis,clarifies the research content,methods and technical routes of the thesis,and sorts out the domestic and foreign literatures.The second chapter expounds the concepts,theories and models involved in the research process.Relevant theories include long tail theory,Metcalfe's law,core edge theory,spatial interaction theory,and cyclic cumulative causal theory.Research models include spatial and quantitative models and GWR models.The third chapter is the analysis of the spatial distribution pattern of China's e-commerce development level.Through a basic overview of the development of e-commerce,spatial variability of e-commerce level and spatial correlation analysis,we have a comprehensive and systematic understanding of the status quo and characteristics of China's e-commerce development level.The fourth chapter analyzes the influencing factors by two model methods.First,the regression results of the model are compared by the OLS classical regression model and the econometric model.The following optimal models are used to analyze the influencing factors.The second is to explore the differences in the influencing factors of different cities through the Geographical Weighted Regression Model(GWR).The fifth chapter summarizes the conclusions and prospects,sums up the innovation points and deficiencies.