A distance-driven urban simulation model (DISUSIM): Accounting for urban morphology at multiple landscape levels. (March 2023) - Yang, Jianxin; Yang, Shengbing; Li, Jingjing; Gong, Jian; Yuan, Man; Li, Jingye; Dai, Yunzhe; Ye, Jing
Abstract: Data and skill gaps are common in exploring future urban growth scenarios, especially in developing countries. This study demonstrates how to use distance-related laws regarding process and pattern of urban development to bypass the heavy data and skills required in traditional urban expansion models that rely on various driving factors and machine-learning algorithms. We proposed an urban simulation model that uses three distance-driven components alone to simulate urban expansion by regulating urban morphology at multiple levels. The landscape-level component discriminates the spatiotemporal distribution of urban demand based on rules linked to distance to city centers. The class-level component applies an exponential model with the distance to pre-urbanized patches to control where new urban development may expand. The patch-level component creates urban patches of given sizes with shapes controlled by their distances to the initial patch seeds. Application of the model in Wuhan, China confirms the efficacy of the model and its distance-driven components, as supported by both cell-level agreement and pattern-level similarity. The overarching contribution of DisUSIM lies in providing a distance-driven framework for convenient exploration of urban dynamics in situations where datasets and skills on processing and analyzing driving factors and mechanisms of urban expansion are not readily available. Highlights: A distance-driven CA model was proposed for simulation of urban growth. The model uses three distance-driven components to regulate urban morphology at the landscape-, class- and patch-level. The model enables modelers to explore future urban growth scenarios of various spatial morphologies. The model is convenient for rapid urban simulation in situations where information on driving factors is unknown in advance.