“…Many statistical and geospatial‐based models, such as the cellular automata (CA) (Aburas et al., 2016; Mitsova et al., 2011), the agent‐based model (Mustafa et al., 2017; Tian et al., 2016), Markov chain analysis (Rimal et al., 2017), principal components analysis (Feng & Liu, 2013), artificial neural networks (Li & Yeh, 2002), linear regression (Arsanjani et al., 2013), fractal‐based models (Jat et al., 2017), and logistic regression (LR) (Mustafa et al., 2017), have been developed to evaluate and forecast urban growth. Among these established models, CA has become a popular and effective tool to describe the complex dynamics and ecological consequences of urban sprawl (Abolhasani & Taleai, 2019; Tong & Feng, 2019a; Tripathy & Kumar, 2019), which is widely used to investigate urbanization in Asia (Feng et al., 2016; Liu et al., 2018; Tian et al., 2016), Africa (Agyemang & Silva, 2019), Europe (Basse et al., 2016), Australia (Lu, Laffan, et al, 2019), and the North and South America (Guzman et al., 2020) because of its simple rules, low resource consumption, fast running speed, powerful complex calculation function, and strong capabilities in simulating the temporal and spatial dynamic evolution of complex spatial systems (Aburas et al., 2016).…”