Logistic Cellular Automata Model Research Articles

Firefly algorithm-based cellular automata for reproducing urban growth and predicting future scenarios

• A new urban-growth CA model using the firefly algorithm can enhance the simulation accuracy. • The model was applied to simulate the past and future urban patterns of Xi-xian. • The land transition probability indicates that the traffic facilities have a significant impact. • The FOM in the calibration and validation are respectively increased by 4.84% and 8.10%. • The model can improve CA-based simulation methods and enhance our understanding. Optimization of cellular automata (CA) using swarm intelligence is an effective approach to establish optimal models for urban growth modeling. This study builds a new CA model (CA FFA ) using the firefly algorithm through optimizing transition rules, aiming to enhance the simulation accuracy. We applied CA FFA to reproduce historical urban growth (2009-2014 and 2014-2019) and simulate future scenarios (2024 and 2029) of the Xi-xian metropolitan area in 2009, 2014 and 2019. The retrieved CA FFA parameters and land transition probability maps indicate that the traffic facilities have more significant impacts on urban growth in Xi-xian than the socio-economic and other proximity factors. The overall accuracy of CA FFA is 90.2% in 2014 and 94.6% in 2019, both better than the logistic CA model (88.9% and 92.2%). The figure-of-merits of CA FFA in calibration and validation are respectively increased by 4.84% and 8.10% compared with the logistic CA model. With the increasing distance to the city center, new urban areas will first increase and then decrease, and be mainly distributed in the northern part, which are consistent with the local development planning and have strong practical significance. The proposed model can improve CA-based modeling methods and enhance our understanding of Chinese western cities.

Identification of conflict between wildlife living spaces and human activity spaces and adjustments in/around protected areas under climate change：A case study in the Three-River Source Region

Overlap between wildlife living spaces and human activity spaces represents one of the main causes of human-wildlife conflict. It is therefore necessary to identify and adjust the spatial distribution of conflicts because climate change will modify the ranges and locations of species. The MaxEnt model was used to simulate the distribution of wildlife living spaces under current and future climatic conditions (RCP4.5 scenario) based on species distribution data and environmental data from the Three-River Source Region. Furthermore, the logistic-cellular automata model was used to simulate the changes in human activity spaces (construction land and arable land) by 2050. Areas of overlap between wildlife living spaces and human activity spaces were determined by overlay analysis. Then, the areas of construction and arable land that would conflict with wildlife were redistributed outside highly suitable wildlife living spaces. In addition, ecological corridors connecting current and future living spaces were planned for the migration of certain species in response to climate change based on the minimum cumulative resistance model. The results showed that the areas of highly suitable wildlife living spaces will decrease under the influence of climate change if global warming occurs. In the future, the area of overlap between highly suitable wildlife living spaces and construction land will be 125 km2, and that between highly suitable wildlife living spaces and arable land will be 340 km2. The redistribution of these areas will cause human activity spaces to become more concentrated. Moreover, 110 ecological corridors should be constructed across roads in the Three-River Source Region.

Modeling urban expansion by using variable weights logistic cellular automata: a case study of Nanjing, China

ABSTRACTSimulation models based on cellular automata (CA) are widely used for understanding and simulating complex urban expansion process. Among these models, logistic CA (LCA) is commonly adopted. However, the performance of LCA models is often limited because the fixed coefficients obtained from binary logistic regression do not reflect the spatiotemporal heterogeneity of transition rules. Therefore, we propose a variable weights LCA (VW-LCA) model with dynamic transition rules. The regression coefficients in this VW-LCA model are based on VW by incorporating a genetic algorithm in a conventional LCA. The VW-LCA model and the conventional LCA model were both used to simulate urban expansion in Nanjing, China. The models were calibrated with data for the period 2000–2007 and validated for the period 2007–2013. The results showed that the VW-LCA model performed better than the LCA model in terms of both visual inspection and key indicators. For example, kappa, accuracy of urban land and figure of merit for the simulation results of 2013 increased by 3.26%, 2.96% and 4.44%, respectively. The VW-LCA model performs relatively better compared with other improved LCA models that are suggested in literature.

Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images

Cellular automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical phenomena. Traditional methods generally pose problems in determining the structure and parameters of CA for a large, complex region or a long-term simulation. This study presents a self-adaptive CA model integrated with an artificial immune system to discover dynamic transition rules automatically. The model’s parameters are allowed to be self-modified with the application of multi-temporal remote sensing images: that is, the CA can adapt itself to the changed and complex environment. Therefore, urban dynamic evolution rules over time can be efficiently retrieved by using this integrated model. The proposed AIS-based CA model was then used to simulate the rural-urban land conversion of Guangzhou city, located in the core of China’s Pearl River Delta. The initial urban land was directly classified from TM satellite image in the year 1990. Urban land in the years 1995, 2000, 2005, 2009 and 2012 was correspondingly used as the observed data to calibrate the model’s parameters. With the quantitative index figure of merit (FoM) and pattern similarity, the comparison was further performed between the AIS-based model and a Logistic CA model. The results indicate that the AIS-based CA model can perform better and with higher precision in simulating urban evolution, and the simulated spatial pattern is closer to the actual development situation.