Cellular Automata for Urban Growth Modelling:

Abstract

Abstract:Urban growth modelling has attracted considerable attention over the past two decades. This article reviews the driving factors that have been identified and studied in cellular automata (CA); one of the popular methods in urban growth modelling. Over a hundred articles published between 1993 and 2012 were selected and reviewed. We extracted the driving factors from CA transition rules and arranged them in a list. The list contributes to early spatial research for the selection of factors in CA models. Our analyses show that studies between 1993 and 2000 mainly focused on using earth's physical factors in predicting urban growths, while recent studies combined them with socioeconomic factors, resulting with models with a greater number of inputs. Nevertheless, the human-behaviour factors impacting urban growth were generally under-represented. Geographically, more applications of the CA urban growth models have been seen in the developed countries compared with those in the developing countries, suggesting substantial work is needed to address issues in understanding and modelling rapid urban growth processes in developing countries.

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

“…Previous research has, inter alia, applied various econometric models and spatial-based measurement frameworks for examining urban complexity and its multifaceted expressions [41,42]. Spatiotemporal models such as CA, MC, and SLEUTH (slope, land use, exclusion, transportation, and hill shade) techniques were applied in most of the previous studies for monitoring urban expansion [43,44]. In the present paper, we used an integrated CA-MC model that eliminates the shortcomings of individual (CA and MC) models [4].…”

Modeling urban sprinkling with cellular automata

“…Table A lists 19 scientific articles on urban expansion modeling from 2014 to 2020. Due to the presence of several recent reviews of prediction models (Aburas et al, 2016;Dang & Kawasaki, 2016;Poelmans & Van Rompaey, 2010;van Vliet et al, 2016;Wahyudi & Liu, 2016), articles published before 2014 have not been considered in this summary. The search was conducted on February 2020, in the Scopus website and was carried out considering one of these keywords: "Urban growth", "Model prevision", "Prediction", "Cellular Automata", "Logistic regression", "Scenario", "Urban expansion".…”

“…Land use models allow to project and simulate future urban patterns in order to act on the dynamics and mechanisms of urban expansion (Wahyudi & Liu, 2016). Urban land use change models are generally analyzed and applied to provide a decision support to policy makers in the implementation of new urbanization plans (Allen & Lu, 2003;Nasiri et al, 2019;Puertas et al, 2014).…”

Autómatas Celulares en Cascada para modelar la expansión urbana con áreas restringidas

“…Los autómatas celulares (AC) son, tal vez, el enfoque más utilizado desde hace varias décadas para representar cambios espacio-temporales en el territorio (Wahyudi y Liu, 2016). Las razones son diversas: son prácticos y robustos para procesar imágenes, reconocer y proyectar patrones en tiempo y espacio (Wongthanavasu y Tangvoraphonkchai, 2007); no requieren demasiados datos que son costosos de recolectar (lo que los hace mucho más operables que los modelos integrados urbanos: Lopes, Grangeiro y Van Wee, 2019); y facilitan simular numerosos procesos espaciotemporales: cambios de uso del suelo (Baxendale y Buzai, 2011;Pérez-Miranda et al, 2011;Padilla et al, 2015), difusión de contaminantes (Marín et al, 2000), deslizamientos de tierra (Lai y Dragicevic, 2011), propagación de incendios (Yassemi, Dragićević y Schmidt, 2008), entre otros.…”