  Vector-based cellular automata (CA) based on real land-parcel has become an
important trend in current urban development simulation studies. Compared with
raster-based and parcel-based CA models, vector CA models are difficult to be
widely used because of their complex data structures and technical
difficulties. The UrbanVCA, a brand-new vector CA-based urban development
simulation framework was proposed in this study, which supports multiple
machine-learning models. To measure the simulation accuracy better, this study
also first proposes a vector-based landscape index (VecLI) model based on the
real land-parcels. Using Shunde, Guangdong as the study area, the UrbanVCA
simulates multiple types of urban land-use changes at the land-parcel level
have achieved a high accuracy (FoM=0.243) and the landscape index similarity
reaches 87.3%. The simulation results in 2030 show that the eco-protection
scenario can promote urban agglomeration and reduce ecological aggression and
loss of arable land by at least 60%. Besides, we have developed and released
UrbanVCA software for urban planners and researchers.
