The Urban Pulse: Diagnosing the urbanization process as spiky, cyclical, and asynchronous

• 成果类型: Article
• 署名作者: Zhu, Zhe; Fragkias, Michail; Suh, Ji Won; McCoshan, Ella; Chen, Lelin; Song, Kexin; Kong, Juwon; Li, Tian; Seto, Karen C.
• 署名单位: University of Connecticut; Boise State University; University of Victoria; Yale University; Cornell University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2537770123
• 发表日期: 2026-06-16
• 页码: e2537770123
• 关键词: remote sensing; CONSTRUCTION; timeseries; Covid-19; urban pulse

摘要

Many urban indicators predominantly focus on static outcomes, failing to capture the dynamic nature of urbanization. Here, we introduce the concept of the Urban Pulse to characterize urbanization as a process of continual spatiotemporal change. Using dense time series remote sensing data, we operationalize the Urban Pulse for six cities that represent diverse development levels, economies, and geographies: Seattle, Shenzhen, Lagos, Mumbai, Dubai, and Mexico City. We then use the Urban Pulse to assess how COVID-19 affected each of the six cities. The results empirically substantiate three major insights. First, urbanization is inherently spiky, characterized by episodic events that deviate from baseline trends. Second, urbanization is cyclical, reflecting transitions through stages of expansion, stabilization, or decline, and does not occur with predictable frequency, such as seasonal patterns. Third, urbanization is asynchronous, with subcity areas exhibiting uncoordinated development patterns. The Urban Pulse COVID-19 analysis shows that while urbanization processes for some cities have not returned to prepandemic patterns, other cities exhibit a significant shift in patterns postpandemic. These findings suggest that the selection of a neighborhood or subcity area as an observation unit may have little bearing on a city as a whole. By capturing these spatial and temporal rhythms, the Urban Pulse framework provides a comprehensive understanding of process dynamics, enabling researchers to link processes to outcomes more effectively. This approach allows for the recognition of early warning signals at subcity scales and may facilitate targeted interventions at the neighborhood level, where they are most impactful.