Strategies for landscape ecology: An application using cellular automata models

The stochastic perturbation of urban cellular automata (CA) model is difficult to fine-tune and does not take the constraint of known factors into account when using a stochastic variable, and the simulation results can be quite different when using the Monte Carlo method, reducing the accuracy of the simulated results. Therefore, in this paper, we optimize the stochastic component of an urban CA model by the use of a maximum entropy model to differentially control the intensity of the stochastic perturbation in the spatial domain. We use the kappa coefficient, figure of merit, and landscape metrics to evaluate the accuracy of the simulated results. Through the experimental results obtained for Wuhan, China, the effectiveness of the optimization is proved. The results show that, after the optimization, the kappa coefficient and figure of merit of the simulated results are significantly improved when using the stochastic variable, slightly improved when using Monte Carlo methods. The landscape metrics for the simulated results and actual data are much closer when using the stochastic variable, and slightly closer when using the Monte Carlo method, but the difference between the simulated results is narrowed, reflecting the fact that the results are more reliable.

The urban growth boundary (UGB) plays an important role in the regulation of urban sprawl and the conservation of natural ecosystems. The delineation of UGBs is a common strategy in urban planning, especially in metropolitan areas undergoing fast expansion. However, reliable tools for the delineation of informed UGBs are still not widely available for planners. In this study, a patch-based cellular automaton (CA) model was applied to build UGBs, in which urban expansions were represented as organic and spontaneous patch growing processes. The proposed CA model enables the modeler to build various spatial and socio-economic scenarios for UGB delineation. Parameters that control the patch size and shape, along with the spatial compactness of an urban growth pattern, were optimized using a genetic algorithm. A random forest model was employed to estimate the probability of urban development. Six scenarios in terms of the demand and the spatial pattern of urban land allocation were constructed to generate UGB alternatives based on the simulated urban land maps from the CA model. Application of the proposed model in Ezhou, China from 2004 to 2030 reveals that the model proposed in this study can help urban planners make informed decisions on the delineation of UGBs under different scenarios.

Assessing cellular automata model behaviour using a sensitivity analysis approach

The diversity of human immunodeficiency virus (HIV) in vivo has been reported. In this article, we propose a cellular automata (CA) model describing the interactions between the immune system and HIV, and examine the effect of the diversity of these interactions. The novel aspects of our CA model are that it not only considers four states (HIV, virgin, dead, infect) but also the diversity exhibited by both HIV and T cells. We simulated maximum diversities for these states by simulating CA on a computer. The model revealed that increased diversity had the effect of increasing the HIV population and simulation steps. In addition, we observed that the CA model accurately reflects the occurrence of infection, incubation period, and the development of AIDS. The CA model demonstrated that the diversity of the virus is the major factor affecting the success rate of the escape of HIV from the immune response.

Modeling dynamic urban growth using cellular automata and particle swarm optimization rules

Land use and land cover change (LULCC) can influence giant panda distributions by altering landscape structure and configuration. However, the spatial impacts and potential time lag effects of landscape pattern changes on giant pandas remain underexplored. In this study, we applied a random forest classification method to analyze LULCC in 1990, 2000, and 2010, alongside calculating a set of landscape metrics to assess changes in landscape fragmentation, connectivity, and diversity. Random forest regression models were then used to evaluate the spatial relationships between landscape metrics and giant panda density, with the aim of identifying whether a time lag effect exists. The results revealed the following: (1) The random forest classification achieved high land use classification accuracy. Forests remained the dominant land cover, occupying approximately 97% of the study area throughout the period, with only minor fluctuations observed among other land use types. (2) Landscape metrics indicated increasing landscape fragmentation, connectivity, and diversity. While increased landscape fragmentation can negatively impact giant panda habitat, improvements in landscape connectivity and diversity could mitigate these effects by preserving movement corridors and enhancing habitat accessibility. (3) The strongest correlations between giant panda density and landscape metrics were observed when the time points aligned. Landscape metrics from 2010 showed the highest correlation with the 4th NGPS (around 2010), and landscape metrics from 2000 had the highest correlation with the 3rd NGPS (around 2000). The results revealed that giant panda density responded most strongly to contemporary landscape pattern changes, suggesting an immediate response. However, correlations with earlier landscape metrics also suggest that a relatively weak time lag effect may be present. All landscape metrics were derived from remote sensing data, enabling scalable and repeatable GIS-based analysis. These findings highlight the utility of spatial landscape indicators for monitoring species distribution patterns and underscore the importance of maintaining and enhancing habitat connectivity within giant panda conservation efforts.

As an integrated kind of railway signal-control pattern, the four-aspect fixed autoblock system has been used in many train control systems. This paper takes the four-aspect fixed autoblock system as the research object and proposes the cellular automata model of the fixed autoblock system based on the existing theoretical researches on different train systems and traffic systems by means of the analytical study of the classical cellular automata model. The CA (cellular automata) models combine complexity of passenger railway line with the theory of cellular automata and introduce some new CA models into the existing control systems. After analyzing the relevant simulation results, we study thoroughly and obtain efficiently the needed data for the variation of the section carrying capacity, the average train delay and the train speed which have been affected by redundant time on the operation of passenger trains with different speeds.

The dynamics of urban growth are the direct consequence of the actions of individuals, and public and private organisations, which act to change the urban landscape simultaneously over space and time. Since previous urban form has a strong influence on the present, a prime concern of urban planners, spatial scientists and government authorities is to under‐ stand how a city has grown in the past in order to predict the growth of the city in the fu‐ ture. This requires flexible tools that allow planners to examine the impacts and potential consequences of applying different development policies, strategies and future plans [1]. However, traditional linear, static and top-down models are unable to adequately capture the processes underlying urban change. The non-linearity of spatial and temporal relation‐ ships and irregular, uncoordinated and uncontrolled local decision-making gives rise to seemingly coordinated global patterns that define the size and shape of cities in familiar ways [2-7]. Cities are now increasingly recognized as complex systems and display many of the characteristic traits of complexity, i.e. non-linearity, self-organization and emergence. Cellular Automata (CA) offer a modeling framework and a set of techniques for modelling the dynamic processes and outcomes of self-organizing systems [8-13]. Since the late 1980s they have demonstrated significant potential benefits for urban modelling through their simplicity, flexibility and transparency [8, 14-17]. CA are capable of generating complex pat‐ terns in aggregate form by using relatively simple local transition rules, i.e. by recursive de‐ velopment decisions being made at individual cells or sites [2, 15, 18]. However, cities are also influenced by global factors representing government polices (such as broader social, economic and technological factors). This has led to a number of hybrid-type urban growth models, which take into consideration local, regional and global factors [19-22]. When inte‐ grated with other technologies such as GIS and remote sensing, the potential of CA for geo‐

A species’ distribution across the landscape is not random, but it is affected by distribution, size, abundance and connectivity of landscape patches. This spatial configuration of the landscape shapes ecological processes, for example the location of home ranges, migration routes and migration ability. Landscape metrics describe the configuration of a landscape quantitatively. While traditional approaches in habitat modelling only consider environmental attributes at a specific location, the integration of landscape metrics adds more functional information. In this paper we evaluated a method of directly incorporating a set of landscape metrics as covariates into a Maxent habitat model. Specifically, we used hexagons as statistical units for the calculation of landscape metrics. With this method also landscape metrics calculated with vector data sets can be used for SDM. We tested this approach for the smooth snake (Coronella austriaca) in the Austrian Alps. The experimental designs resulted in an improvement of the habitat models. Moreover, the results demonstrated the benefits of landscape metrics for the model outcomes at different scales.

The checkerboard update Glauber model for the one-dimensional Ising model is mapped onto a cellular automata model. This cellular automata model is found to be a special case of the well known (two-state) triangular cellular automata model. The author shows, therefore, that there exists a subspace of the full parameter space of the triangular cellular automata that obeys detailed balance. Using this result the shows that there are no phase transitions within the interior of the cubical parameter space of the triangular cellular automata model. He identifies a new line of critical points for the triangular cellular automata. This critical line is associated with the zero-temperature critical line of the one-dimensional Ising antiferromagnet in an external field. The critical properties of the checkerboard Glauber model are studied and are found to be identical to those of the single-spin update model. In addition, the correlations in the checkerboard Glauber model are found to exhibit a null-cone structure, similar to that found in relativity theory. This null-cone, which originates in the constraints imposed by the checkerboard update procedure, is found to be a general property of the triangular cellular automata model.

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

Urban sprawl scenario simulations based on cellular automata and ordered weighted averaging ecological constraints

Convolution-Generated Motion as a Link between Cellular Automata and Continuum Pattern Dynamics

Under the background of the ecological civilization era, rapidly obtaining coal mining information, timely assessing the ecological environmental impacts, and drafting different management and protection measures in advance to enhance the capacity of green mine construction have become the urgent technical problems to be solved at present. Simulating and analyzing mining subsidence is the foundation for a land reclamation plan. The Cellular Automata (CA) model provides a new tool for simulating the evolution of mining subsidence. This paper takes a mine in East China as a research area, analyses the methods and measures for developing a model of mining subsidence based on the theories of CA and mining technology, then discusses the results of simulation from different aspects. Through comparative analysis, it can be found that the predicted result is well consonant with the observation data. The CA model can simulate complex systems. The system of mining subsidence evolution CA is developed with the support of ArcGIS and Python, which can help to realize data management, visualization, and spatial analysis. The dynamic evolution of subsidence provides a basis for constructing a reclamation program. The research results show that the research methods and techniques adopted in this paper are feasible for the dynamic mining subsidence, and the work will continue to do in the future to help the construction of ecological civilization in mining areas.

Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR