Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata

This article aims to construct a Land-use Simulation and Decision-Support system (LandSDS) to comprehensively explore the interference and impact of natural environment, individual choice, and national macro-policies on land use and cover change. LandSDS takes full advantage of system dynamics (SD) model, agent-based model (ABM), cellular automata (CA) model, and Geographic Information System (GIS): (1) On the one hand, SD model controls land-use area change on macro level. On the other hand, by passing the parameters values from SD model to CA model, the iterations of CA model are controlled macroscopically, and at the same time, the transition rules in the CA model are affected microscopically by SD model; (2) By simulating the complex selection behaviors of different agents, ABM enforces or participates in the cells' transformation behaviors in CA model on the micro evolution scale; (3) Taking CA model as a breakthrough point, the micro-cell unit is used to realize the data exchange and communication between SD-ABM-CA by integrating CA with SD and CA with ABM; and (4) Using GIS's powerful spatial computing and data processing capabilities, the simulation results are further analyzed and processed to obtain more effective information. LandSDS can solve the top-down data analysis and graphic visualization problems caused by target data or policy mutations, and can also handle the data and graphic visualization problems caused by the temporal and spatial changes of cell units. The system solves the analysis methods and technical problems about the top-down and bottom-up seamless integration of the spatial and temporal models. Beijing urban expansion was taken as an example to demonstrate the availability of the system.