Multidimensional Spatial Vitality Automated Monitoring Method for Public Open Spaces Based on Computer Vision Technology: Case Study of Nanjing’s Daxing Palace Square

Abstract

Assessing the vitality of public open spaces is critical in urban planning and provides insights for optimizing residents’ lives. However, prior research has fragmented study scopes and lacks fine-grained behavioral data segmentation capabilities and diverse vitality dimension assessments. We utilized computer vision technology to collect fine-grained behavioral data and proposed an automated spatial vitality monitoring framework based on discrete trajectory feature points. The framework supported the transformation of trajectory data into four multidimensional vitality indicators: crowd heat, resident behavior ratio, movement speed, and spatial participation. Subsequently, we designed manual validation mechanisms to demonstrate the monitoring framework’s efficacy and utilized the results to explore the changes in vitality, and the influencing factors, in a small public space. Discrete trajectory feature points effectively addressed the literature’s fragmented study scope and limited sample size issues. Spatial boundaries had a significantly positive impact on spatial vitality, confirming the “boundary effect” theory. The peak spatial vitality periods were from 08:30 to 09:30 and from 17:30 to 18:30. A higher enclosure degree and better rest facilities positively impacted spatial vitality, while a lower enclosure degree did not consistently suppress spatial vitality in all situations. Overall, spatial features and spatial vitality have a complex nonlinear relationship.

A Dynamic Prediction Framework for Urban Public Space Vitality: From Hypothesis to Algorithm and Verification

“…For selecting urban zones for case studies, an area that is generally more active, with noticeable variations in activity levels across different time periods is suitable. Previous studies mostly chose bustling commercial districts or residential areas in city centers [19,25,27,28]. This ensures the collection of a sizable amount of data with significant variations, which is more conducive to validation.…”

“…The researchers began their investigation by observing the behavior of individuals and crowds in Rundle Mall over the course of one month, aiming to understand the patterns of crowd behavior. In previous studies concerning the collection of human behavior data in public spaces, researchers conducted on-site street view image collection for each study area, ranging from 1 to 6 days, distinguishing between weekdays and weekends [25,28,31,32]. These studies meticulously controlled factors such as weather and temperature, deliberately selecting days with mild weather conditions.…”

Exploring the Land Cover Material Interaction of Urban Open Space on the Thermal Comfort of Crowds in High-Temperature Environments and Retrofit Strategies: Two Case Studies in the Nanjing Xinjiekou District

“…Since the beginning of the last century, the Xinjiekou area has functioned as the commercial and comprehensive urban center of Nanjing, characterized by dense population and high commercial activity [44]. It typifies high-density urban center areas, exhibiting characteristics such as a dense population, high urbanization levels, extensive construction, and a significant proportion of paved roads [43,[45][46][47]. Due to the urban heat island effect, temperatures in this area are notably higher than those in the surrounding regions.…”