Point Pattern Analysis Of The Commercial Facilities Vitality Based On Social Network Review Data

In the field of urban planning,urban public space vitality has been a matter of great concern to urban designer.A large number of scholars have studied the neighbourhoods vitality,the business district vitality,underground space vitality from different angles,and try to explore the influencing factors of urban public space vitality.However,few scholars have quantitatively analyzed the spatial pattern of urban commercial facilities vitality.In the city,commercial facilities play a very important role.In particular,with the rapid development of China's economy,urban residents' demand for commercial facilities showing a variety and high standardization features.Therefore,it is very meaningful to study the space-time distribution character and spatial cluster patter of urban commercial facilities vitality,as well as to explore the influencing factors of commercial facilities development.This helps to rationalize the allocation of urban resources and meet the individual needs of urban residents.The current research mainly has the following problems:(1)At present,most of the researches on the spatial analysis of commercial facilities focus on the spatial characteristics of commercial facilities.There are few studies focus on the spatial distribution of commercial facilities vitality,while the latter is more realistic for the reasonable allocation of urban resources and the supervision and management of urban commercial facilities.(2)Some studies have shown that traditional planar-based spatial analysis methods are not suitable for analyzing geographic events that are constrained by urban road networks.Therefore,in order to analyze the spatial cluster patter of commercial facilities vitality in the road network space more accurately,we need to extend the traditional spatial autocorrelation analysis method.(3)In the traditional regression analysis method,the regression model generally use the same global parameters.When applying the traditional regression analysis method to analyze the influencing factors of commercial facilities vitality,which ignores the spatial nonstationarity characteristics of the influencing factors.In fact,in different regions,it may be differences in the influencing factors of commercial facilities viability.Therefore,the traditional regression analysis method cannot be well adapted to explore the influencing factors of commercial facilities vitality.In view of the above problems,this paper mainly studies the following three aspects:(1)Based on the data of social network,this paper adopted the Network Kernel Density Estimation to study the spatial distribution characteristics of commercial facilities.At the same time,we combine the customer reviews data weight the results,to explore the spatial differences in the urban commercial facilities viability.In addition,in order to explore the space-time distribution of the urban commercial facilities vitality,this paper proposes a Network Space-time Kernel Density Estimation method,which can reveals the space-time characteristics of consumer behavior,and reflects the development process of commercial facilities.(2)In order to analyze the aggregation characteristics of commercial facilities viability in the road network space,in this paper,we propose a network spatial autocorrelation analysis method based on Global Moran's I and Local Getis' G.In the road network space,we quantify the commercial facilities viability,which can reflect the spatial distribution pattern of the commercial facilities vitality.In addition,we adopted the network K function to study the aggregation characteristics of commercial facilities with different customer satisfaction in road network space,and the aggregation characteristics of commercial facilities and subway,commercial center and so on.(3)Based on the traditional Decision Tree Regression Model,considering the spatial nonstationarity,this paper propose a Geographical Weighted Gradient Boosting Decision Tree Regression Model to explore the factors that affect the commercial facilities vitality.These analysis results can provide a quantitative reference for urban management,commercial facility location and other issues.