Integration of logistic regression,Markov chain and cellular automata models to simulate urban expansion

This research analyses the suburban expansion in the metropolitan area of Tehran, Iran. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Environmental and socio-economic variables dealing with urban sprawl were operationalised to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026. For validation, the model was evaluated by means of relative operating characteristic values for different sets of variables. The approach was calibrated for 2006 by cross comparing of actual and simulated land use maps. The achieved outcomes represent a match of 89% between simulated and actual maps of 2006, which was satisfactory to approve the calibration process. Thereafter, the calibrated hybrid approach was implemented for forthcoming years. Finally, future land use maps for 2016 and 2026 were predicted by means of this hybrid approach. The simulated maps illustrate a new wave of suburban development in the vicinity of Tehran at the western border of the metropolis during the next decades.     

Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics

Empirical models designed to simulate and predict urban land‐use change in real situations are generally based on the utilization of statistical techniques to compute the land‐use change probabilities. In contrast to these methods, artificial neural networks arise as an alternative to assess such probabilities by means of non‐parametric approaches. This work introduces a simulation experiment on intra‐urban land‐use change in which a supervised back‐propagation neural network has been employed in the parameterization of several biophysical and infrastructure variables considered in the simulation model. The spatial land‐use transition probabilities estimated thereof feed a cellular automaton (CA) simulation model, based on stochastic transition rules. The model has been tested in a medium‐sized town in the Midwest of São Paulo State, Piracicaba. A series of simulation outputs for the case study town in the period 1985–1999 were generated, and statistical validation tests were then conducted for the best results, based on fuzzy similarity measures.     

Fuzzy inference guided cellular automata urban‐growth modelling using multi‐temporal satellite images

This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on urban development is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. A defuzzification process converts the development potential to the required neighbourhood development level, which is taken by cellular automata as initial approximation for its transition rules. Such approximations are updated through spatial calibration over townships and temporal calibration with multi‐temporal satellite images. Assessment of the modelling results is based on three evaluation measures: fitness and Type I and Type II errors. The approach is applied to model the growth of the city of Indianapolis, Indiana over a period of 30 years from 1973 to 2003. A fitness level of 100 ±20% with 30% average errors can be achieved for 80% of the townships in urban‐growth prediction.     

A review of assessment methods for cellular automata models of land-use change and urban growth

ABSTRACT

Cellular automata (CA) models are in growing use for land-use change simulation and future scenario prediction. It is necessary to conduct model assessment that reports the quality of simulation results and how well the models reproduce reliable spatial patterns. Here, we review 347 CA articles published during 1999–2018 identified by a Scholar Google search using ‘cellular automata’, ‘land’ and ‘urban’ as keywords. Our review demonstrates that, during the past two decades, 89% of the publications include model assessment related to dataset, procedure and result using more than ten different methods. Among all methods, cell-by-cell comparison and landscape analysis were most frequently applied in the CA model assessment; specifically, overall accuracy and standard Kappa coefficient respectively rank first and second among all metrics. The end-state assessment is often criticized by modelers because it cannot adequately reflect the modeling ability of CA models. We provide five suggestions to the method selection, aiming to offer a background framework for future method choices as well as urging to focus on the assessment of input data and error propagation, procedure, quantitative and spatial change, and the impact of driving factors.     

基于扩展CA的文化传播时空模拟研究

文化传播影响因素复杂，具有空间上的相邻、不相邻、选择、随机等扩散形式和时间上的遗传特征，其时空模拟必须借助复杂系统研究方法。元胞自动机模型（CA）与GIS集成在地理过程模拟方面具有很大的优势。文中在分析元胞空间关系和元胞邻居描述的基础上，提出元胞邻居存在几何间上的邻接形式和空间上不邻接但属性上相关的邻居形式。据此，通过对文化革新扩散的实质、空间形式、影响因素等分析，对立了基于邻居扩展CA的文化传播系统模型，并利用该模型对文化传播现象进行了实验研究，取得了比较满意的实验结果。     

A Moore's cellular automaton model to get probabilistic seismic hazard maps for different magnitude releases: A case study for Greece

Cellular automata are simple mathematical idealizations of natural systems and they supply useful models for many investigations in natural science. Examples include sandpile models, forest fire models, and slider block models used in seismology. In the present paper, they have been used for establishing temporal relations between the energy releases of the seismic events that occurred in neighboring parts of the crust. The catalogue is divided into time intervals, and the region is divided into cells which are declared active or inactive by means of a threshold energy release criterion. Thus, a pattern of active and inactive cells which evolves over time is determined. A stochastic cellular automaton is constructed starting with these patterns, in order to simulate their spatio-temporal evolution, by supposing a Moore's neighborhood interaction between the cells. The best model is chosen by maximizing the mutual information between the past and the future states. Finally, a Probabilistic Seismic Hazard Map is given for the different energy releases considered. The method has been applied to the Greece catalogue from 1900 to 1999. The Probabilistic Seismic Hazard Maps for energies corresponding to m = 4 and m = 5 are close to the real seismicity after the data in that area, and they correspond to a background seismicity in the whole area. This background seismicity seems to cover the whole area in periods of around 25–50 years. The optimum cell size is in agreement with other studies; for m > 6 the optimum area increases according to the threshold of clear spatial resolution, and the active cells are not so clustered. The results are coherent with other hazard studies in the zone and with the seismicity recorded after the data set, as well as provide an interaction model which points out the large scale nature of the earthquake occurrence.     

Cellular modelling of river catchments and reaches: Advantages, limitations and prospects

The last decade has witnessed the development of a series of cellular models that simulate the processes operating within river channels and drive their geomorphic evolution. Their proliferation can be partly attributed to the relative simplicity of cellular models and their ability to address some of the shortcomings of other numerical models. By using relaxed interpretations of the equations determining fluid flow, cellular models allow rapid solutions of water depths and velocities. These can then be used to drive (usually) conventional sediment transport relations to determine erosion and deposition and alter the channel form. The key advance of using these physically based yet simplified approaches is that they allow us to apply models to a range of spatial scales (1–100 km²) and time periods (1–100 years) that are especially relevant to contemporary management and fluvial studies.However, these approaches are not without their limitations and technical problems. This paper reviews the findings of nearly 10 years of research into modelling fluvial systems with cellular techniques, principally focusing on improvements in routing water and how fluvial erosion and deposition (including lateral erosion) are represented. These ideas are illustrated using sample simulations of the River Teifi, Wales. A detailed case study is then presented, demonstrating how cellular models can explore the interactions between vegetation and the morphological dynamics of the braided Waitaki River, New Zealand. Finally, difficulties associated with model validation and the problems, prospects and future issues important to the further development and application of these cellular fluvial models are outlined.     

Geomorphological limits to self-organization of alpine forest-tundra ecotone vegetation

Feedback in the establishment of vegetation has been shown to produce spatial patterns that differ from the geomorphological basis for resources. The dynamics of these spatial patterns have been characterized as self-organization because local processes produce them at landscape scales. Geomorphic patterns could, however, enhance or disrupt the processes that lead to patterns and the interpretation of self-organization. A simulation model that showed such indication of self-organization at alpine forest-tundra ecotones is modified to incorporate a geomorphic feature commonly seen in this environment – solifluction steps – as an exogenous condition in the model. Analyses linking spatial patterns and rates of advance of vegetation indicate that such geomorphic patterns do not alter the dynamics of vegetation until the size of the patterns is about double that of the dimension within which endogenous dynamics operate. The sizes of some geomorphic patterns incorporated in the model are probably larger than any realistic solifluction feature at such ecotones in western North America.