Spatio-Temporal Characteristics and Driving Mechanisms of Urban Expansion in the Central Yunnan Urban Agglomeration

Abstract

Accurately identifying the expansion characteristics and driving mechanisms at different development stages of urban agglomerations is crucial for their coordinated development. Using the Central Yunnan Urban Agglomeration as a case study, we employ a data fusion approach to fuse nighttime light data with LandScan data and utilize the U-net neural network to systematically analyze the expansion characteristics and driving mechanisms of the urban agglomeration. The results indicate that, from 2008 to 2013, the Central Yunnan Urban Agglomeration was in an initial expansion stage, primarily driven by economic development levels and population size. From 2013 to 2018, the agglomeration entered an accelerated expansion stage, driven mainly by industrial structure transformation and the population agglomeration effect. From 2018 to 2023, the agglomeration experienced a steady expansion stage, with industrial structure upgrading and government support as the primary driving forces. Furthermore, we found that, over time, the influence of economic development levels and population size as driving forces gradually weakened, while the impact of industrial structure and government support significantly increased. Through the fusion of multi-source data and analysis of driving mechanisms at different developmental stages, we comprehensively revealed the development trajectory of the Central Yunnan Urban Agglomeration and provided valuable insights for future urban agglomeration development planning and policymaking.

Keywords

• urban agglomeration expansion
• built-up areas
• data fusion
• spatiotemporal characteristics
• driving mechanisms