Research On Social Behavior Of Urban Street Space From The Perspective Of Actor Network And Environment Affordance Integration

In the context of rapid urbanization,the crude construction of cities has led to the lack of friendly spatial environment for people to interact with each other in some living streets(hereinafter referred to as streets).In response to this problem,traditional street space research has conducted a lot of studies on the quality of the physical space environment of streets and the behavioral characteristics of people in streets,but their studies have mostly adopted a binary perspective of the relationship between people and the spatial environment,lacking a holistic focus on the multiple elements that influence human behavior in streets and the networks they constitute.Therefore,this study combines actor network theory and environmental affordance theory,and focuses on the social behavior of people in urban street space and its correlation with other related multiple elements.The purpose of this study is to provide reference for the planning and construction of more humane urban streets in China.Based on the review and integration of actor network theory and environmental affordance theory,this study proposes a general analytical framework of "social-space(environment)-behavior" for social behavior in street space.In this framework,the social behavior network is composed of three elements: human intention,material affordance and social reality and the relationship between them.The authors use behavior maps and social indices to describe the spatial and temporal distribution patterns and intensity characteristics of human social behavior.Based on this,this study adopted spatial syntactic analysis and cluster analysis to identify three sample streets in Wuhan: Jianghan2 nd Road,Jianghan 3rd Road and Jianghan 4th Road.Based on the video recordings obtained from the field research in the three sample streets,the results of the on-site questionnaire survey and the sketch of network relationships,the following research was conducted: the paper firstly analyzed the type characteristics of social behaviors in street space and the inner mechanism of their occurrence systematically,and then identified and analyzed the behaviors of people in the videos with the help of machine learning technology,and obtained the map of social behaviors in street space and the social index of The results of the social behavior map and social index in street space are obtained.Based on the behavioral map,the paper further investigates the spatial and temporal distribution characteristics and patterns of social behaviors in street space;meanwhile,based on the social index,the paper explores the intensity characteristics of social behaviors in street space and their correlation with the quality of street space.This study found that:(1)By combining the two theories of actor network and environmental availability,a new analytical framework of "social-spatial(environmental)-behavior" is obtained,which can help make up for the shortcomings of traditional research and reveal the type characteristics,spatial and temporal distribution,and intensity patterns of social behavior in street space.(2)In the street space,different types of social behaviors show different spatio-temporal distribution patterns,which are summarized in this study as five types: regular concentration type,tidal concentration type,unconventional concentration type,unconventional dispersion type and unconventional anchor point type,and the spatial characteristics and temporal patterns of social behaviors of different patterns differ significantly.(3)This study finds that the intensity of social behavior can be characterized by the social index,and the absolute social index is influenced by the change of pedestrian flow between weekends and weekdays,and its data fluctuates greatly;the relative social index is influenced by the affordance of street space environment,and its data does not fluctuate greatly between weekends and weekdays.Overall,the relative social index can reflect the intensity of social behavior on the street more realistically.In terms of the correlation between the intensity of social behavior and the quality of street space,the five street space quality indicators that have the most significant impact on the relative social index are pedestrian width,density of leisure facilities,density of storefronts,density of other facilities,and pedestrian friendliness.The street space quality indicators that have a significant impact on the absolute social index are pedestrian walkway width,guardrail density,shade density,street interestingness,and color vibrancy.This paper combines actor networks with environmental affordance theory,and seeks to innovate the theoretical framework by constructing a "social-spatial(environmental)-behavioral" analysis framework;At the same time,the ethnographic "thick description" method and topology are applied to the study of social behavior in street space.,Crowd recognition and dwell time statistics with the help of machine learning algorithms,laying the foundation for scientifically revealing the characteristics and laws of social behavior in street space.