The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review

Abstract

In recent years, several types of simulation and prediction models have been used within a GIS environment to determine a realistic future for urban growth patterns. These models include quantitative and spatio-temporal techniques that are implemented to monitor urban growth. The results derived through these techniques are used to create future policies that take into account sustainable development and the demands of future generations. The aim of this paper is to provide a basis for a literature review of urban Cellular Automata (CA) models to find the most suitable approach for a realistic simulation of land use changes. The general characteristics of simulation models of urban growth and urban CA models are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various models are identified based on the analysis and discussion of the characteristics of these models. The results of the review confirm that the CA model is one of the strongest models for simulating urban growth patterns owing to its structure, simplicity, and possibility of evolution. Limitations of the CA model, namely weaknesses in the quantitative aspect, and the inability to include the driving forces of urban growth in the simulation process, may be minimized by integrating it with other quantitative models, such as via the Analytic Hierarchy Process (AHP), Markov Chain and frequency ratio models. Realistic simulation can be achieved when socioeconomic factors and spatial and temporal dimensions are integrated in the simulation process.

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

“…Both the research findings in this study and related previous studies tried to show the usefulness of the application of the CA-MC model to figure out factors and trends of urban expansion and its impact on the land-use changes of a particular area [4,24,31,55]. Indeed, the model identified the role of biophysical and socioeconomic variables in predicting future urbanization processes.…”

“…Measuring and, above all, modeling urban expansion and its impacts requires the exploitation of both (historical) remote sensing data and data on actual socioeconomic attributes [2,8,13]. Urban modeling thus enables policymakers and officials to understand and foresee the future expansion and helps in designing LUC policies better geared towards sustainable development [18,31,40].…”

“…Both CA and MC models have their limitations, i.e., the inability to empirically describe the LUCs (CA) and the geographical lack of specificity when describing changes (MC), respectively [24,52]. An integrated CA-MC, therefore, increases both models' capability by capitalizing on their respective qualities [4,31]: it combines the neighborhood effects of the CA and the historical transition probability effects of the MC, thus giving a more comprehensive prediction of the LUCs [14,17,51]. Unlike other models, this has the advantages of incorporating both time and spatial variability, while at the same time recognizing some of the major biophysical and socio-economic drivers underlying LUC [8,14].…”

Comparative Review of Methods Supporting Decision-Making in Urban Development and Land Management

“…The literature review indicates that cellular automata is one of the most useful models for simulating urban growth owing to its "structure, simplicity, and possibility of evolution" [38]. On the other hand, there can be several limitations identified, such as weaknesses in the quantitative aspect or the inability to include the driving forces of urban growth in the simulation process; however, these can be minimized by integrating cellular automata with other quantitative models [38].…”

Developing Efficient Discrete Simulations on Multicore and GPU Architectures

“…[6][7][8]. It is currently being very used, in particular, for simulations in geography (especially in urban development planning [9], future development of cities [10], and land use [11]) pedestrian or vehicular traffic [12,13], and bio-medicine (applied to physiological modeling; for example, for cancer [14], or epidemic modeling [15]).…”