Enhancing Urban Resilience Through Spatial Interaction-Based City Management Zoning

Abstract

Good city management is essential in mitigating the impact of various crisis events and thus enhances urban resilience. The current zoning system that underlies China’s city management system, however, hardly meets the resilient requirements in failing to appropriately and flexibly allocate patrol personnel under normal and various crisis scenarios. We propose an optimization method that gives rise to a resilient city management zoning system by introducing spatial interaction. We realize this through community detection in a spatially embedded patrol passage cost network. Illustrated by a case in Jiangbei District, Ningbo, China, we show that the optimized zoning system has significant advantages in terms of event coverage, response efficiency, and workload balance in the normal scenario as compared to the status quo with up to 3.6 percent, 5.9 percent, and 34.0 percent performance improvement, respectively. Moreover, the method also achieves similar performance improvement in three typical shock scenarios in city management: epidemic, typhoon, and crowd gathering. We conclude the article with discussions on the findings’ significance in urban resilience building and their methodological and theoretical implications in applied urban sciences.

良好的城市管理对于降低危机事件的影响从而提高城市韧性, 至关重要。然而, 作为中国城市管理体系基石的现行区划体系, 很难满足韧性需求, 在正常和危机情况下不能恰当而灵活地分配巡逻人员。我们提出了一种优化方法, 通过考虑空间相互作用、从巡逻路线成本空间网络中提取社区, 建立了韧性城市管理区划体系。以中国宁波市江北区为例, 我们表明, 与现有区划相比, 优化的区划体系在正常情况下的事件覆盖率、响应效率和工作量平衡方面具有显著优势, 性能分别提高了3.6%、5.9%和34.0%。此外, 该方法在城市管理中的三个典型突发场景(流行病、台风和人群聚集)中也有类似的性能改进。最后, 我们讨论了这些成果在城市韧性建设中的意义及其在应用城市科学中的方法论和理论意义。

Un buen manejo de la ciudad es esencial para mitigar el impacto de varios eventos de crisis y de ese modo fortalecer la resiliencia urbana. Sin embargo, el actual sistema de zonificación que subraya el sistema de manejo de la ciudad en China a duras penas se acerca a los requisitos de resiliencia, al carecer de la idoneidad necesaria para asignar de manera adecuada y flexible el personal patrullero en escenarios normales y de crisis. Proponemos un método de optimización que puede generar un sistema de zonificación resiliente para el manejo de la ciudad en el que se introduzca la interacción espacial. Implementamos esto a través de la detección de comunidades en una red de costes de paso de patrullas espacialmente incrustada. Ilustrando con el caso del Distrito de Jiangbei, Ningbo, China, mostramos que el sistema de zonificación optimizado tiene ventajas significativas en términos de la cobertura de eventos, eficiencia en la respuesta y equilibrio de la carga de trabajo en el escenario normal comparado con el statu quo con hasta un 3,6 por ciento, un 5,9 por ciento y un 34,0 por ciento de mejora en el rendimiento, respectivamente. Aún más, el método logra también un mejoramiento similar en el desempeño en tres escenarios de choque típicos para el manejo de la ciudad: epidemia, tifón y aglomeración de gente. Concluimos el artículo con discusiones sobre el significado de los hallazgos para la construcción de resiliencia y sus implicaciones metodológicas y teóricas en las ciencias urbanas aplicadas.

Key Words:

• city management
• community detection
• resilience
• spatial interaction
• Steiner Traveling Salesman problem
• zoning

关键词:

• 城市管理
• 社区提取
• 韧性
• 空间相互作用
• 斯坦纳旅行推销员问题
• 区划

Palabras clave:

• detección comunitaria
• interacción espacial
• manejo de la ciudad
• problema de viajante de comercio de Steiner
• resiliencia
• zonificación

Acknowledgments

We acknowledge the Urban Management and Law Enforcement Bureau of Ningbo for providing data for this study and advice on shock scenarios and case-disposal priorities. We thank Mrs. Hongbin Yu, Yuqiao Deng, and Fu Li, who are research associates at our lab, for their help with data acquisition and analysis. We thank Professor Hongmou Zhang at Peking University for his advice on trajectory data processing. We thank Ms. Kunjing Xu for her help in processing the pictures.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Supplemental Material

For an introduction to the Chinese City Management Grid, detailed data descriptions, and supplementary notes on the performance evaluation, see Supplemental Materials. Supplemental data for this article can be accessed on the publisher’s site at: https://doi.org/10.1080/24694452.2024.2322477

Additional information

Funding

This research was supported by the National Social Science Foundation of China (Grant No. 22BGL279).

Notes on contributors

Hezhishi Jiang

HEZHISHI JIANG is a PhD Student at the Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 100871. E-mail: [email protected]. His research areas include spatial networks, regionalization, and spatiotemporal optimization.

Liyan Xu

LIYAN XU is an Associate Professor in the College of Architecture and Landscape Architecture, Peking University, Beijing, China 100871. E-mail: [email protected]. His research interests cover spatiotemporal intelligence methods and techniques, and their applications in various aspects of smart cities building.

Jianing Li

JIANING LI is a PhD Student at the College of Urban and Environmental Sciences, Peking University, Beijing, China 100871. E-mail: [email protected]. His research areas include theory and practice of nature-based rural revitalization under the market-based approach.

Jinyuan Liu

JINYUAN LIU earned her FMLA degree at the College of Architecture and Landscape of Peking University, Beijing, China 100871. E-mail: [email protected]. Her research interests include landscape and environmental aesthetics, Chinese aesthetics theory, and rural landscape.

Yao Shen

YAO SHEN is an Associate Professor in the College of Architecture and Urban Planning, Tongji University, Shanghai, China 200092. E-mail: [email protected]. His research interests cover urban modeling, spatial analysis, urban geometry, resilient cities, transport and land use, urban sciences, and complex networks.