In cellular automata (CA) modeling, spatial heterogeneity can be delineated by
geographical area partitioning. The dual constrained space clustering method is a prevalent …

A key component of cellular automata (CA) models is the transition rules that determine the
transformation of cells at each iteration. However, most previous studies use a single set of …

The impact of urban expansion on achieving sustainable development goals (SDGs) has
become a significant research topic in the field of geographic information science. In this …

This work aims to utilize Internet of Things (IoT) technology and the Artificial Neural Network-
Cellular Automaton (ANN-CA) model to analyze the construction of indicators for territorial …

Urban studies require a rich set of information sources and techniques that enable a
comprehensive depiction of urban environments. Remote sensing captures physical …

Urban simulation research originated between the 1980s and 1990s. Today urban
simulation has become a new paradigm of urban research, which is an important outcome of …

Cellular automata (CA) based models are practical tools to simulate the spatiotemporal
landscape evolution induced by the land use/-cover change (LUCC). Existing models are …

This paper is an in‐depth study of international trade quantification models based on deep
neural networks. Based on an in‐depth analysis of global trade characteristics, a summary …

Simulating spatiotemporal land use and land cover change (LUCC) data precisely under
future climate scenarios is an important basis for revealing the carbon cycle response of …

As the demand for reliable, up-to-date information about natural and manmade
environments has risen, so has attention to this problem. Due to urbanization, it must …