Constrained Cellular Automata Model Research Articles

Bringing conservation priorities into urban growth simulation: An integrated model and applied case study of Hangzhou, China

A green infrastructure assessment (GIA)-constrained cellular automata (CCA) model was developed to integrate habitat preservation with spatial-temporal simulation and the corresponding establishment of an urban growth boundary (UGB). A case study of the Hangzhou Metropolitan Area successfully demonstrated the application of the GIA-CCA model. Multitemporal land use maps interpreted from remotely sensed images taken in 2000, 2005, 2010 and 2015 were utilized for validation and UGB prediction for 2020. The GI scenario incorporating the ecological network and a baseline scenario (BS) involving a current growth trend of no environmental constraint were designed to further investigate the unique advantages of the GIA-CCA model over the CCA model. Comparative analysis with groups of landscape metrics indicated that the GI scenario improved the connectivity among ecological elements, reducing urban fragmentation and promoting growth compactness, yet many irregular forms of edge sprawl and main city expansion were observed under the BS. Based on the GI scenario, the final UGBs were established, covering the central city, six new cities and nine towns. In contrast to the proposed UGBs, the planned UGBs generated by economic policies might play a negative role in preserving natural resources and responding to development uncertainties and could be improved by considering the forecasted results based on the GIA-CCA model as a helpful decision-making tool. The model could become an increasingly important part of growth management, particularly for developing countries and regions that are exposed to environmental deterioration due to rapid urbanization.

Simulating Sustainable Urban Development by Incorporating Social-ecological Risks into a Constrained CA Model

A key solution to urban and global sustainability is effective planning of sustainable urban development, for which geo-techniques especially cellular automata (CA) models can be very informative. However, existing CA models for simulating sustainable urban development, though increasingly refined in modeling urban growth, capture mostly the environmental aspect of sustainability. In this study, an adaptable risk-constrained CA model was developed by incorporating the social-ecological risks of urban development. A three-dimensional risk assessment framework was proposed that explicitly considers the environmental constraints on, system resilience to, and potential impacts of urban development. The risk-constrained model was then applied to a case study of Sheyang County, Jiangsu Province in the eastern China. Comparative simulations of urban development in four contrasting scenarios were conducted, namely, the environmental suitability constrained scenario, the ecological risk constrained scenario, the social risk constrained scenario, and the integrated social-ecological risk constrained scenario. The simulations suggested that considering only environmental suitability in the CA simulation of urban development overestimated the potential of sustainable urban growth, and that the urbanization mode changed from city expansion that was more constrained by social risks to town growth that was more constrained by ecological risks. Our risk-constrained CA model can better simulate sustainable urban development; additionally, we provide suggestions on the sustainable urban development in Sheyang and on future model development.

The dynamics of shifting cultivation captured in an extended Constrained Cellular Automata land use model

This paper presents an extension to the Constrained Cellular Automata (CCA) land use model of White et al. [White, R., Engelen, G., Uljee, I., 1997. The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environment and Planning B: Planning and Design 24(3), 323–343] to make it better suited for modelling the dynamics of shifting cultivation. In the extended model the time passed since the last land use transition of a location is a factor of its land use potential. The model can now account for the gradual decrease in soil fertility after an area of forest has been cleared for cultivation and also capture the process of regeneration once the plot is fallowed. The model is applied for the Ruhunupura area of Sri Lanka where chena, a particular practice of shifting cultivation, is a common land use that dominates the landscape dynamics. The model is calibrated for the period 1985–2001 and the results are assessed in terms of location to location overlap as well as structural similarity at multiple scales. These results give confidence in the representation of land use dynamics for the main land use classes. On the basis of a long term scenario run for the period 2001–2030, it is verified that the model captures stylized facts related to chena dynamics, in particular shortening fallow periods and increasingly long cultivation periods of chena, as a result of increasing land use pressure. We conclude that the model extension is crucial for regions with substantial areas of shifting cultivation. The extension affects not only the land use class shifting cultivation, but also through spatio-temporal interactions that are already present in the original CCA model the whole land use system is better represented.

An Artificial-Neural-Network-based, Constrained CA Model for Simulating Urban Growth

Insufficient research has been done on integrating artificial-neural-network-based cellular automata (CA) models and constrained CA models, even though both types have been studied for several years. In this paper, a constrained CA model based on an artificial neural network (ANN) was developed to simulate and forecast urban growth. Neural networks can learn from available urban land-use geospatial data and thus deal with redundancy, inaccuracy, and noise during the CA parameter calibration. In the ANN-Urban-CA model we used, a two-layer Back-Propagation (BP) neural network has been integrated into a CA model to seek suitable parameter values that match the historical data. Each cell's probability of urban transformation is determined by the neural network during simulation. A macro-scale socio-economic model was run together with the CA model to estimate demand for urban space in each period in the future. The total number of new urban cells generated by the CA model was constrained, taking such exogenous demands as population forecasts into account. Beijing urban growth between 1980 and 2000 was simulated using this model, and long-term (2001–2015) growth was forecast based on multiple socio-economic scenarios. The ANN-Urban-CA model was found capable of simulating and forecasting the complex and non-linear spatial-temporal process of urban growth in a reasonably short time, with less subjective uncertainty.

A Constrained CA Model for the Simulation and Planning of Sustainable Urban Forms by Using GIS

A constrained cellular automata (CA) model based on ‘grey cells’ for the simulation of different types of urban forms and developments is developed within a raster GIS. Remote sensing and GIS are used to supply information on environmental constraints and locations of growth centers for the simulation. The model is able to consider different criteria, such as urban forms, environmental suitability, and land consumption for the planning of sustainable cities. Seven different types of urban forms and developments that range from compact–monocentric, compact–polycentric, compact–monocentric–environmental, compact–polycentric–environmental, dispersed, highly dispersed, to very highly dispersed developments were simulated by using the model. The model can generate urban forms and developments with fractal structures that are close to real cities. The simulated patterns were evaluated by using different cost indicators related to sustainable development principles.