Fine‐scale simulation of spatial population distribution by coupling GA–ABM and big data: A case study of Dongguan, China

Abstract

AbstractCapturing spatial population distribution can offer useful information for urban planning to promote reasonable population distribution and allocate urban resource. Agent‐based model (ABM) based on the modeling idea of “bottom‐up” can offer the ability to simulate the complex individual behaviors that generate spatial population distribution. Previous ABMs were unable to be extended for simulation of spatial population distribution at a fine scale due to the shortage of fine characterization of the urban environment and the calibration of agents' behavior. This study filled these gaps by proposing a genetic algorithm‐ABM (GA–ABM) for fine‐scale simulation of spatial population distribution in a manufacturing metropolis. In this model, the employment and residential choice behaviors of agents were defined by the labor economic theory and discrete selection model. Multisource geospatial big data such as enterprise points‐of‐interest big data and building footprints data were used to finely characterize the labor market and urban environment to reflect the impact of agents' employment choices on their residential decision. Furthermore, the grid‐scale population investigation big data were combined with the GA to calibrate the agents' residential decision behaviors. The proposed model was used in Dongguan, the typical manufacturing metropolis in China. As a comparison, the expert‐experience‐based method‐ABM (EEBM–ABM) was also conducted by using the same data set. Through the comparison of the results produced by these two models, it was demonstrated that the model coefficient calibrated by GA could effectively reflect the agents' residential decisions. The calibrated GA–ABM is more capable than EEBM–ABM in simulating spatial population distribution in a manufacturing metropolis. Hence, the proposed model can be used to simulate spatial population distribution in a manufacturing metropolis which helps the urban planner to conduct scientific urban planning.