Assessing the effect of temporal dynamics on urban growth simulation: Towards an asynchronous cellular automata

Abstract

Time is a fundamental dimension in urban dynamics, but the effect of various definitions of time on urban growth models has rarely been evaluated. In urban growth models such as cellular automata (CA), time has typically been defined as a sequence of discrete time steps. However, most urban growth processes such as land‐use changes are asynchronous. The aim of this study is to examine the effect of various temporal dynamics scenarios on urban growth simulation, in terms of urban land‐use planning, and to introduce an asynchronous parcel‐based cellular automata (AParCA) model. In this study, eight different scenarios were generated to investigate the impact of temporal dynamics on CA‐based urban growth models, and their outputs were evaluated using various urban planning indicators. The obtained results show that different degrees of temporal dynamics lead to various patterns appearing in urban growth CA models, and the application of asynchronous (event‐driven) CA models achieves better simulation results than synchronous models.

Modeling urban regions: Comparing random forest and support vector machines for cellular automata

“…As such, the gridded two‐dimensional (2D) character of a CA environment makes it conducive to the simulation of urban sprawl. Several studies have utilized the simulation power of CA to investigate future patterns of urban sprawl with sophisticated calibration methods (Abolhasani & Taleai, 2020; Alaei Moghadam, Karimi, & Habibi, 2018; Charif, Omrani, Abdallah, & Pijanowski, 2017; Kantakumar, Kumar, & Schneider, 2016; Lin & Li, 2016; Momeni & Antipova, 2020; Zhang, Wang, He, & Xia, 2020). The bottom‐up approach of CA does not provide insight into how urban patterns were formed or which processes of the coupled human–environment system in metropolitan regions underpin such patterns (Batty, 2005; Benenson & Torrens, 2004; Wu & Silva, 2010).…”

Behind the Land Use Mix: Measuring the Functional Compatibility in Urban and Sub-Urban Areas of China

“…Firstly, incorporating the latest research of geography, topology, and other disciplines into the index could be one path to refine it, particularly to improve the compatibility measurement. Introducing some simulation methods such as CA models represents a potential direction for exploring the inner mechanism and improving the measurement of functional compatibility [53][54][55][56][57]. Secondly, adopting mechanisms, such as a group decision making approach that could involve more local planning practitioners and create a more convincing compatibility relation matrix, would be beneficial.…”

A Novel Method of Modeling Grassland Wildfire Dynamics Based on Cellular Automata: A Case Study in Inner Mongolia, China

“…The proposed method for modeling grassland wildfire dynamics is based on the CA model. CA has been widely used as a simple model of computation but is capable of simulating complex behaviors [60,61], such as the simulation of snow crystal growth processes [62][63][64]; urban dynamics [65][66][67][68]; self-reproduction, which is a major factor in the development of a system [69]; image processing [70,71]; and epidemic propagation [67,72]. The CA model has been popular for wildfire simulations over the last few decades [48,[73][74][75][76][77][78][79][80][81][82].…”