Xun Liang's repositories
Open-Space-Cellular_Automata
A spatio-temporal approach based on Cellular Automata (CA) for simulating the spatial dynamics of open spaces (include urban green spaces, parks, squares, trails, courtyards, and other natural spaces), by considering a set of spatial data that represents the infrastructural and socio-economic factors, namely the OS-CA (Open Space Cellular Automaton) model. The dynamic sub-model for OS is used to generate scenarios with different parameters (e.g. mean construction delays and mean area) for exploring the effects of planning policies on the future distribution of open space. The OS-CA considers the interactions and inter-attraction between open space and urban land in the simulation process. The proposed model can accurately predict the emergence of some open spaces.
Patch-generating_Land_Use_Simulation_Model
The PLUS model integrates a rule mining framework based on Land Expansion Analysis Strategy (LEAS) and a CA model based on multi-type Random Patch Seeds (CARS), which was used to understand the drivers of land expansion and project landscape dynamics.
Mixed_Cell_Cellullar_Automata
The Mixed-Cell Cellullar Automata (MCCA) provides a new approach to enable more dynamic mixed landuse modeling to move away from the analysis of static patterns. One of the biggest advantages of mixed-cell CA models is the capability of simulating the quantitative and continuous changes of multiple landuse components inside cells.
UrbanEmergenceCA
A CA model to simulate urban emergence
flusver1.0
Simulating land use change