Adaptive Behavioral Dynamics in Public Open Spaces During the COVID-19 Pandemic: A Technological Perspective on Urban Resilience

Da Mao (), Huijie Yang, Shaohua Zhang, Haozhe Sun and Xiaojuan Wang
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Da Mao: Henan Institute of Science and Technology
Huijie Yang: Henan Institute of Science and Technology
Shaohua Zhang: Henan Institute of Science and Technology
Haozhe Sun: Henan Institute of Science and Technology
Xiaojuan Wang: Henan Institute of Science and Technology

Journal of the Knowledge Economy, 2024, vol. 15, issue 3, No 96, 12677 pages

Abstract: Abstract The emergence of the COVID-19 pandemic has significantly disrupted urban life, leading to profound changes in the daily routines and behavioral patterns of city residents. This study focuses on a central public open space within a residential area in Xinxiang, China, as a microcosm of urban dynamics during the pandemic. Leveraging an innovative long-term information annotation technology, the research team transformed 3 months of daytime monitoring video data from the early phase of the pandemic into a meticulously annotated dataset containing 115,975 records of spatial behavior. These records were generated through a combination of visual interpretation and spatial positioning techniques. The ensuing comprehensive analysis aimed to discern shifts in residents’ behavioral characteristics, particularly focusing on spatial activity indices and densities. This study not only enhances our understanding of evolving dynamics in public open spaces but also provides empirical support for optimizing and transforming such spaces in the post-pandemic era. Key findings include the utility of long-term information annotation technology for rigorous behavioral analysis, nuanced trends in spatial vitality, age-related variations, identification of predominant resident behaviors, and age-dependent preferences for activity areas. This research contributes to urban behavioral dynamics knowledge and underscores the adaptability of communities in the face of unprecedented challenges, informing urban planning and policy decisions in a rapidly evolving world.

Keywords: Behavioral dynamics; Information annotation technology; Deep learning; Computer vision; COVID-19 pandemic; Urban resilience; Age-responsive design (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13132-023-01591-4

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