Evolution of Urban Spatial Clusters in China: A Graph-Based Method Using Nighttime Light Data

Abstract

An urban spatial cluster (USC) describes one or more geographic agglomerations and the linkages among cities. USCs are conventionally delineated based on predefined administrative boundaries of cities, without considering the dynamic and evolving nature of the spatial extent of USCs. This study uses Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light (NTL) satellite images to quantitatively detect and characterize the evolution of USCs. We propose a dynamic minimum spanning tree (DMST) and a subgraph partitioning method to identify the evolving USCs over time, which considers both the spatial proximity of urban built-up areas and their affiliations with USCs at the previous snapshot. China is selected as a case study for its rapid urbanization process and the cluster-based economic development strategy. Four DMSTs are generated for China using the urban built-up areas extracted from DMSP/OLS NTL satellite images collected in 2000, 2004, 2008, and 2012. Each DMST is partitioned into various subtrees and the urban built-up areas connected by the same subtree are identified as a potential USC. By inspecting the evolution of USCs over time, three different types of USCs are obtained, including newly emerging, single-core, and multicore clusters. Using the rank-size distribution, we find that large-sized USCs have greater development than medium- and small-sized USCs. A clear directionality and heterogeneity are observed in the expansions of the ten largest USCs. Our study provides further insight for the understanding of urban system and its spatial structures, and assists policymakers in their planning practices at national and regional scales.

城市空间群描述了一个或多个地理集聚区以及城市之间的关联。城市空间群的划定, 通常是根据预先设定的城市行政边界, 没有考虑到城市空间群在空间范围上的动态变化和演变特性。本研究利用DMSP/OLS夜灯卫星图像, 定量地检测和描述了城市空间群的演变。我们提出了动态最小生成树和子图划分方法, 来识别城市群随时间的演化。该方法同时考虑了城市建成区的空间邻近性及其与城市群历史状态的关联性。中国经历了快速城市化进程和集群经济发展战略, 因此本文选取中国为研究对象。从2000年、2004年、2008年和2012年的DMSP/OLS夜灯卫星图像中提取城市建成区, 并生成四个动态最小生成树。每个动态最小生成树被划分为不同的子树, 同一个子树所连接的城市建成区被做为潜在的城市空间群。通过考察城市空间群随时间的演化, 得到三种不同类型的城市空间群：新兴、单核和多核。通过排序和大小分布曲线, 我们发现, 大型城市空间群比中型和小型城市空间群经历了更大的发展。十个最大城市空间群的扩张, 具有明显的方向性和异质性。我们的研究, 为理解城市系统及其空间结构提供了进一步的认识, 可以帮助决策者在国家和区域尺度上进行规划。

Un clúster o agrupamiento espacial urbano (USC) describe una o más aglomeraciones geográficas y sus conexiones entre ciudades. Convencionalmente, los USC son delineados a partir de límites administrativos predefinidos de la ciudad, sin considerar la naturaleza dinámica y cambiante del ámbito espacial de los USC. Este estudio usa imágenes satelitales de iluminación nocturna del Programa Satelital para la Defensa Meteorológica/Sistema Operacional de Escaneo Lineal (DMSP/OLS) para detectar y caracterizar cuantitativamente la evolución de los USC. Proponemos un árbol dinámico de alcance mínimo (DMST) y un método de partición de subgrafo para identificar los USC en evolución a través del tiempo, que toma en cuenta tanto la proximidad espacial de las áreas urbanas edificadas como sus afiliaciones con los USC en el panorama anterior. Se seleccionó a China como un estudio de caso a cuenta de su rápido proceso de urbanización y a la estrategia de desarrollo económico basada en clúster espacial. Se generaron cuatro DMST para China usando las áreas urbanas edificadas extraídas de las imágenes satelitales DMSP/OLS NTL acumuladas en 2000, 2004, 2008 y 2012. Cada DMST se partió en varios subárboles y las áreas urbanas edificadas conectadas por el mismo subárbol se identifican como un USC en potencia. Al inspeccionar la evolución de los USC a través del tiempo, se obtuvieron tres tipos diferentes de USC, incluyendo los clústeres de núcleos únicos y múltiples de reciente aparición. Usando la distribución por rango de tamaño, descubrimos que los USC de tamaño grande tienen un desarrollo mayor que los USC de tamaño medio y pequeño. La direccionalidad y heterogeneidad evidentes son observadas en las expansiones de los diez USC más grandes. Nuestro estudio arroja una mayor perspicacia para el entendimiento del sistema urbano y sus estructuras espaciales, y ayuda a los legisladores en sus prácticas de planificación a escalas nacional y regional.

Key Words:

• dynamic minimum spanning tree
• nighttime light data
• urban dynamics
• urban spatial clusters

关键词:

• 动态最小生成树
• 夜灯数据
• 城市动态
• 城市空间群。

Palabras clave:

• árbol dinámico de alcance mínimo
• clústeres espaciales urbanos
• datos de iluminación nocturna
• dinámica urbana
• política urbana

Supplemental Material

Supplemental data for this article can be accessed online at http://dx.doi.org/10.1080/24694452.2021.1914538. The supplemental material consists of five sections. Section A is an explanation of graph theory and MST. Section B provides the indicators for partitioning the DMST based on Gestalt theory. Section C includes the uncertainty and sensitivity analysis of the thresholds for indicators. The calculation of SDE is shown in Section D. Section E gives the comparison of the identification of the LDP cluster based on the method proposed by B. Yu et al. (2014) and our method.

Acknowledgments

The authors are thankful to the editor and anonymous reviewers for their valuable comments and suggestions that improved this article. Bailang Yu and Yan Liu served as the corresponding authors for this article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 41871331 and 41801343), the ECNU Academic Innovation Promotion Program for Excellent Doctoral Students (Grant No. YBNLTS2019-001), the China Scholarship Council (supporting Congxiao Wang to undertake part of the work at the University of Queensland; Grant No. 201906140197), the Major Program of National Social Science Foundation of China (Grant No. 17ZDA068), and the Innovation Program of Shanghai Municipal Education Commission (Grant No. 15ZZ026).

Notes on contributors

Congxiao Wang

CONGXIAO WANG is a PhD Candidate in Geographic Information Systems at the Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China. E-mail: [email protected]. Her research interests include nighttime light remote sensing and its application in urban research.

Bailang Yu

BAILANG YU is Professor of Geography at the Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China and also at the Research Center for China Administrative Division, East China Normal University, Shanghai 200241, China. E-mail: [email protected]. His research interests include urban remote sensing, nighttime light remote sensing, LiDAR, and object-based methods.

Zuoqi Chen

ZUOQI CHEN is an Assistant Research Fellow of Geography at the Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, and the Academy of Digital China, Fuzhou University, Fuzhou 350002, China. E-mail: [email protected]. His research interests include urban remote sensing, nighttime light remote sensing, and the development of geographic information systems.

Yan Liu

YAN LIU is Professor in Geographical Information Science at the School of Earth and Environmental Sciences, The University of Queensland, Brisbane, Australia. E-mail: [email protected]. Her research interests include urban modeling and geo-simulation, spatial data analytics, and spatially integrated social studies.

Wei Song

WEI SONG is Professor of Geography in the Department of Geography and Geosciences, University of Louisville, Louisville, KY 40292. E-mail: [email protected]. His research interests include urban geography, location and transportation analysis, quantitative methods, and applications of geographic information systems.

Xia Li

XIA LI is Professor of Geography at the Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China. E-mail: [email protected]. His research interests include cellular automata, land use modeling, global land use simulation, and spatial optimization.

Chengshu Yang

CHENGSHU YANG is a PhD Candidate in Geographic Information System at the Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China. E-mail: [email protected]. His research interests include nighttime light remote sensing and its application in urban research.

Christopher Small

CHRISTOPHER SMALL is Lamont Research Professor of Marine Geology and Geophysics at the Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964. E-mail: [email protected]. His research interests include geophysics, land surface processes, remote sensing, and population and environment.

Song Shu

SONG SHU is an Assistant Professor of Geography in the Department of Geography and Planning, Appalachian State University, Boone, NC 28608. E-mail: [email protected]. His research interests include remote sensing applications applied to Arctic snow, lake hydrology, water resources, cryospheric processes, and global climate change.

Jianping Wu

JIANPING WU is Professor of Geography at the Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China. E-mail: [email protected]. His research interests include remote sensing and geographic information systems.