Research on the Correlation Mechanism between Street Space Quality and Walking Behavior in Data Environment

It is of great significance to explore the correlation mechanism between urban street space quality and residents’walking behavior for rational and effective allocation of street facilities resources and promotion of healthy and green travel. Taking Qiguitang block in Hefei as an example, the streetscape image is crawled through Python, and the elements of street spatial quality are quantified by a machine learning algorithm, spatial syntax, and ArcGIS. Get travel data through behavior observation, and then build a multiple linear regression model for the correlation study of spatial quality and behavior characteristics to summarize the interaction degree and mode of various influencing factors. The research shows that there is a specific mathematical relationship between walking behavior and street space elements, among which functional formats, walking width, and interface openness have a more significant impact on walking behavior. Accordingly, the optimization strategy of street space in the old city area is proposed to provide a reference for the formulation of Hefei street design guidelines.