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As a significant public place, the commercial area has a potential correlation between its built environment and human activities. However, the current research primarily concentrates on the internal environment of the store and customer satisfaction, while the impact of some environmental features of the outer space of the business district on visitors is seldom systematically discussed. This study takes four commercial districts in Shenzhen as examples, and the streets were categorized into five types based on street characteristics using the cluster analysis method. The relationship between each type of street and the population distribution in the region was subsequently discussed. To this end, a holistic approach was adopted, integrating multi-source urban data such as street view panorama, points of interest (POI), and street and building vectors to describe the built environment. Furthermore, the distribution of people at different times, based on location-based services (LBS) data, was combined to establish statistical models of various streets in commercial districts and evaluate the relationship between street characteristics and human activities. The results demonstrate that the relationship between population distribution and spatial characteristics is different in the five types of streets. Different types of streets have their own advantages, and human activities in the business district are often not affected by this advantage, but by other characteristics. The impact of these factors varies significantly between weekdays and weekends. By systematically categorizing street types and assessing the impact of environmental factors on pedestrian flow, this study sheds new light on the renewal and development of urban commercial districts in the future.

In order to structure an efficient and comfortable commercial district for pedestrians, we need to understand the interaction between pedestrian walking behavior and the complex elements of the built environment. Previous studies have focused on people’s activities in the context of the neighborhood rather than the commercial district. This study investigates the potential associations between multi-dimensional environmental factors and pedestrians under various temporal distributions in a densely populated commercial district. Multi-source urban data and semantic segmentation technics have been adopted to measure the built environmental quality from four classic dimensions of urban design, and combining the observations of pedestrian volumes of representative streets in the commercial district, we assess the relationship between the two at different times on the basis of a generalized linear model (GLM). The analytical results identify that the Morphology, Visual perception, Function, and Street configuration features of the commercial environment have a significant impact on walking activity, and temporal differences exist. The findings highlight the importance of built environment quality to pedestrians and street attractiveness, and inform designers, stakeholders, and municipalities on the revitalization of traditional commercial districts.