Evaluating urban development in China's resource-based cities: a new perspective using nighttime light data

Abstract

ABSTRACT Understanding the urbanization and transformation trajectories of resource-based cities (RBCs) is pivotal for China’s sustainable development goals. This study introduces a novel regression-based algorithm for assessing urbanization patterns. We delineated the urban boundaries of 335 cities and employed time-series nighttime light data from 2001 to 2020, shifting the analysis from a pixel-scale to an urban-scale perspective. Our analysis reveals distinct disparities within the 125 RBCs when compared to the national average, leading to their categorization. The key findings include: (1) Within Forest and Coal RBCs, numerous areas have stabilized after experiencing contraction. Geographically, a significant number of RBCs in the Northeast, North, and Northwest regions are experiencing or have experienced substantial urban shrinkage. The developmental status of RBCs exhibits a spatial positive correlation. (2) Although the government categorized RBCs based on their development level in 2013, findings suggest that this classification may no longer accurately reflect the current development status of RBCs in the context of urbanization. (3) Utilizing spectral clustering, we categorized RBCs into five types, identified 10 RBCs undergoing shrinkage and 30 cities trending towards stabilization post-shrinkage. This research offers a refined evaluative method for urbanization, providing insights beneficial for policy-making concerning RBCs’ sustainable growth..