Urban expansion simulation and development-oriented zoning of rapidly urbanising areas: A case study of Hangzhou.

Sustainable urban development is the key to regional urban development policy-making. Therefore, the comprehensive spatial zoning of rapidly urbanising areas is important. In this study, a novel spatial zoning framework was established based on the future urban spatiotemporal pattern and multidimensional dynamic index system at the township scale. First, the urban expansion of Hangzhou in 2025 was simulated based on a new method in which the hybrid bat algorithm and deep belief network (DBN) are coupled with the cellular automata (CA) model (MDBN-CA). Second, an urban development-oriented evaluation system was established at the township scale based on urban expansion simulations and indicators, including the speed and intensity, morphology, socioeconomic and ecological benefits. Finally, Hangzhou was zoned by using the K-means method. The results show that: (1) The MDBN-CA model effectively overcomes the limitations of traditional neural networks, yielding an increase in the simulation accuracy and spatial pattern similarity of 3.70% and 10.11%, respectively; (2) Hangzhou can be divided into six zones according to the 2025 urban expansion, that is, the highly urbanised, key urbanised, radiation, potential, optimised, and ecological priority zones; (3) Based on the current development trends, urban expansion in Hangzhou will have relatively large benefits by 2025. However, problems with respect to the unbalanced development of land urbanisation and population urbanisation, as well as the low efficiency of land use, were identified. Based on the results of this study, suggestions are provided with respect to spatial pattern reconstruction, urban function transformation, efficient land use, and green and healthy development; (4) Based on the combination of the MDBN-CA and K-means models with the zoning framework of the comprehensive benefits evaluation system of urban expansion, future urban expansion can be predicted and analysed.