Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Abstract

Although traditional cellular automata (CA)-based models can effectively simulate urban land-use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell-based simulation strategies. This research proposes a new patch-based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a patterncalibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000-2006-2012.The results from this model were compared with two common models: cell-based CA and logistic-patch CA. The proposed model yields a degree of patch-level agreement that is 23.4 and 7.5% higher than those of these pre-existing models, respectively. This reveals that the patch-based CA model simulates actual development patterns much better than the two other 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).…”

Cellular Automata Modelling Approach for Urban Growth

“…Herold et al (2003) described that RS can provide synoptic view which is detail in space and time, while social data are often restricted to limited stakeholders and they are poor in temporal accuracy and consistency. Both RS and GIS are introduced in some models due to their compatibility (e.g., Fan et al, 2008;Moghadam et al, 2017). Amongst them, iCity (Stevens et al, 2007;Stevens and Dragi evi , 2007) is one of the most major GIS-CA collaborated modelling tools.…”

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

“…In a classical CA model, space is represented as a regular unit as for raster data. Since raster‐based CA do not match the shape and size of real‐world objects such as city blocks and cadastral parcels, irregular CA models have also been developed (Abolhasani, Taleai, Karimi, & Rezaee Node, ; Alaei Moghadam, Karimi, & Habibi, ; Barreira‐González, Gómez‐Delgado, & Aguilera‐Benavente, ; Dahal & Chow, ; Yao et al, ). Among the various methods of representing the spatial structure of irregular CA models, it seems that the most appropriate ones for simulating urban land‐use interaction at a high level of detail are those using the cadastral parcel, since these are more realistic and more impactful in urban land‐use planning (Abolhasani et al, ; Jjumba & Dragićević, ; Maleki, Hakimpour, & Masoumi, ; Stevens, Dragicevic, & Rothley, ).…”