Static Matching And Dynamic Association Of Urban Commercial And Residential Spaces Based On POI And Trajectory Big Data

The urban operation system in the information age has become more complex,and all functional spaces are closely interacting with each other by human activities,among which commercial and residential spaces based on residents’ consumption behaviors have gradually become organic physical spaces that are causally symbiotic,spatially dependent and closely connected in urban functional spaces,which profoundly affect the development of urban spatial structures.The rapid growth of urban population and aging may lead to changes in living space,and the change of physical commercial forms under the impact of online consumption has accelerated the reconfiguration of commercial space,and the mismatch between commercial and living space has gradually emerged,and it is difficult to comprehensively understand the matching relationship between urban commercial and living space by relying only on static spatial elements as the research object.It is difficult to comprehensively understand the matching relationship between commercial and residential spaces in cities only by relying on static spatial elements.This paper takes Shanghai as a case study area.On the one hand,we use the POI data of commercial and residential spaces as the research object from a static spatial perspective,use GIS spatial analysis methods to identify the distribution pattern of commercial and residential spaces,and explore the static spatial matching characteristics of the two;on the other hand,we use the trajectory data formed by the interaction of residents’ cab consumption trips in urban commercial and residential spaces as the research object from a dynamic perspective,and study the dynamic association characteristics of commercial and residential spaces from the street scale.On the other hand,based on the trajectory data of residents’ taxi trips in the interaction between commercial and residential spaces in the city,the dynamic characteristics of commercial and residential spaces in Shanghai are studied from the street scale.The main research work and conclusions of this paper are as follows:(1)Identifying urban commercial and residential spatial patterns and static correlation characteristics based on POI big dataIn this paper,the spatial distribution pattern of commercial and residential spaces in Shanghai is thoroughly explored based on GIS spatial analysis method,and the static matching characteristics of commercial and residential spaces are studied by constructing honeycomb grids of different scales.The results show that the overall commercial and residential space integration is high,and each commercial type and residential space show the spatial association characteristics of "integration of high-frequency consumption commercial and separation of non-high-frequency consumption commercial.The matching association between commercial and residential space is mainly coordinated type in central city and backward type in suburban area,and the forward type is mainly located near the commercial center in central city and suburban center,and a small number of them are independently distributed along the rail transit line.(2)Analysis of the characteristics of the association between commercial and residential space based on residents’ consumption tripsUsing the circle analysis method and kernel density estimation,it is identified that the spatial distribution patterns of residents’ consumption trips in each circle are monocentric,bicentric and polycentric.The distribution of residents’ consumption trips in the commercial and residential association network has significant spatial heterogeneity,and the spatial association pattern of the street network shows strong spatial dependence and hierarchical characteristics,and there is a large degree of spatial coupling with the hierarchical distribution of street roles and commercial centers.A multi-center and multi-level spatial association network is formed in the outer ring,and the association strength shows a gradual decay from the inner ring to the suburban ring.The hotspot detection model based on density field extracts residents’ hotspots for boarding and consumption,and deeply analyzes the hierarchical structure and spatial distribution characteristics of OD hotspots for residents’ consumption trips.(3)Research on the characteristics and dynamic correlation models of the association network between commercial and residential spacesThe structure of the spatial association network of commercial and residential based on residents’ consumption trips is complex,and the social network analysis helps to identify the network characteristics and structure.In this paper,we use nodal centrality to divide 215 streets in Shanghai into first streets,central streets and nodal streets,and identify the functional characteristics of each street by weighted access degree.The Infomap algorithm is used to divide the commercial and residential space into communities by streets,and seven communities are identified,whose spatial connotations include administrative district structure,cross-administrative district structure,and coreedge structure;and the spatial association of commercial and residential space within the community network is further mined,and four types of patterns are obtained: monocentric spatial association,bicentric spatial association,multicentric spatial association,and low-level homogenized spatial association.