Spatial heterogeneity is an essential feature of land use change. It is important for cellular
automata (CA)-based urban expansion modeling to consider the spatial heterogeneity of …

Spatiotemporal modeling has long been a major concern of geographic information science.
Even though previous research has shown the importance of temporal and spatial …

As an important component of the cellular automata (CA) model, the neighborhood defines
the geographical interaction domain (GID) of cells and their interaction mechanisms …

Urban systems are featured by spatial autocorrelation, which may produce clustering of
model residuals when simulating urban expansion using cellular automata (CA). Accurate …

Simulation and quantitative analysis of urban land use change are effective ways to
investigate urban form evolution. Cellular Automata (CA) has been used as a convenient …

Urban cellular automata (CA) models are broadly used in quantitative analyses and
predictions of urban land-use dynamics. However, most urban CA developed with …

Cellular automata (CA) models have been widely employed to simulate urban growth and
land use change. In order to represent urban space more realistically, new approaches to …

Many methods can be used to construct geographical cellular automata (CA) models of
urban land use, but most do not adequately capture spatial heterogeneity in urban …

The stochastic perturbation of urban cellular automata (CA) model is difficult to fine-tune and
does not take the constraint of known factors into account when using a stochastic variable …

The neighborhood effect, a pivotal element within the realm of Geographic Cellular
Automata (GCA) modeling, has garnered significant attention in research. However, no …