Validating Spatial Patterns of Urban Growth from a Cellular Automata Model

For urban growth modeling, assessment metrics derived from cell-by-cell comparisons are mainly related to the size of the study area and the urban growth rate. Non-urban areas always occupy an important part of the city to which cellular automata (CA) models do not contribute much, so the simulation accuracy is often exaggerated when this part is included. To enable comparing simulation results across models, regions, and time, we developed an improved equivalent area-based assessment (EQASS) method using cell-by-cell comparison metrics. As against existing assessment methods, EQASS is computed by including the same area of urban and suburban areas (i.e., equivalent areas). EQASS was tested in three Chinese coastal cities using a heuristic CA model and two spatial statistical CA models to simulate urban growth. The results show that EQASS can exclude correct rejections that are not attributable to CA models; these correct rejections have a significant impact on the model assessment. The improved assessment can better evaluate the performance of CA models across regions and over time than the conventional assessment method that accounts for the full study area. This study extends the simulation assessment method and provides a good solution for selecting the best CA model from many candidate models.

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

Assessing cellular automata model behaviour using a sensitivity analysis approach

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

Time is a fundamental dimension in urban dynamics, but the effect of various definitions of time on urban growth models has rarely been evaluated. In urban growth models such as cellular automata (CA), time has typically been defined as a sequence of discrete time steps. However, most urban growth processes such as land‐use changes are asynchronous. The aim of this study is to examine the effect of various temporal dynamics scenarios on urban growth simulation, in terms of urban land‐use planning, and to introduce an asynchronous parcel‐based cellular automata (AParCA) model. In this study, eight different scenarios were generated to investigate the impact of temporal dynamics on CA‐based urban growth models, and their outputs were evaluated using various urban planning indicators. The obtained results show that different degrees of temporal dynamics lead to various patterns appearing in urban growth CA models, and the application of asynchronous (event‐driven) CA models achieves better simulation results than synchronous models.

Globally, rapid and haphazard urban growth has induced land use land cover (LULC) transformations in cities and their surroundings. The cities located in the Himalayan foothills have experienced tremendous urban growth in recent years. In this context, urban growth modeling integrated with remote sensing and geoinformatics assists to predict the future urban growth pattern. Therefore, the urban growth pattern of Jammu Urban Agglomeration (UA) from 1991 to 2021 is assessed in this paper. Shannon’s entropy index assesses the trend of built-up expansion in Jammu UA. Further, the upcoming urban growth for the year 2031 was predicted by integrating artificial neural network-multi-layer perceptron (ANN-MLP) and cellular automata (CA) model. The results revealed a substantial rise in built-up land cover while fallow land, vegetation, agriculture, and water body land cover decreased during 1991–2021. The occurrence of dispersed growth in Jammu UA was specified by the entropy index. The predicted urban growth pattern for the year 2031 showcased a further escalation in the built-up land cover while other land cover categories continued declining trend. Overall, such an urban growth pattern is unsustainable for Jammu UA, and there is an urgent requirement of urban containment measures.KeywordsUrban growthUrban growth modelingArtificial neural network-multi-layer perceptronCellular automata

A new cellular automata framework of urban growth modeling by incorporating land use policies and economic development zone planning

Cellular Automata -Simplicity Behind Complexity 88 huge mega-cities This last point reflects how, with a deeper understanding of urban phenomena, scientists have begun to recognise that cities are not uniform or a single type of phenomenon. Instead they are increasingly being recognised as complex systems through which non-linear processes, emergence and self-organisation occur These processes shape the spatial growth of the city over time with structured and ordered patterns emerging (Torrens 2000a). More recent urban models, catalysed by the availability of mainframe and desktop computers, have focused on modelling the spatial structure of urban growth processes and the notion of self-organisation. These models include large-scale urban models of land usetransportation developed using a range of analytical methods from other disciplines including linear programming, physics, human ecology, mathematics, operations research, regional science and economics Many of these models have incorporated cellular automata (CA), whether to model land parcels or the actions of firms or organisations. This chapter will demonstrate the potential of CA as a tool for urban planning and development using two CA models and case studies, one from Saudi Arabia and the other from the Republic of Korea. In Riyadh, Saudi Arabia, the government are planning new suburban towns to absorb future population growth. The exact location of these towns is subject to several environmental constraints. CA have been used as a planning tool to evaluate several sites and visualise the likely future growth of these towns. The impact of increased population growth is also one of the main drivers for the construction of a new city in the Republic of Korea. This chapter illustrates how CA have been used for the evaluation of different potential sites and to visualise how the city could grow over time based on various scenarios. A review of previous work in this area is first provided to place these modelling exercises in context. This is followed by a description of the case studies, the results of the model scenarios and a comparison of the two models. The strengths and weaknesses of the models are discussed including areas for further development.

A segment derived patch-based logistic cellular automata for urban growth modeling with heuristic rules

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

Compared with megacity, the medium-sized city is playing a more and more important part in the rapid urbanization process in China. Due to the expanding scale of the medium-sized city, urban growth is sprawl and this way leads to ecological, social and environment problems that are unsustainable. To measure the urban growth of the medium sized city, cellular automata (CA) model is employed. CA has proven to be a popular and effective modeling approach to investigate dynamic urban growth systems and for evaluating the impacts of possible policy options. We use the SLEUTH model, which is a well known CA model, to simulate the future urban growth of Huaian, Jiangsu province of China. The results confirm the value of SLEUTH which provides a rich exploratory of knowledge for evaluating the effects of possible decision-making in local government.

Gamma titanium aluminide (γ-TiAl) based alloys are very interesting materials for structural applications at elevated temperatures owing to the combination of high specific strength, good oxidation, creep and fatigue resistance. However, their relatively poor ductility and fracture toughness remain important obstacles for their industrial applications in engineering components. The mechanical properties of these alloys, namely the poor room temperature ductility, as well as the yield stress, creep and fracture resistance, can be significantly improved by controlling the microstructure. These alloys undergo solid state phase transformations upon cooling from the high temperature α phase, which results in formation of different types of microstructures depending on the cooling conditions and the alloy composition. Low and moderate cooling rates lead to the precipitation of the γ phase as parallel plates within the α matrix, followed by the α → α2 ordering reaction. As a result of this transformation, the characteristic lamellar structure of TiAl is produced. Rapid cooling leads to a massive transformation from α to γ. It has been shown that the massive microstructure can be used as a precursor to obtain a refined microstructure through a subsequent tempering in the α + γ phase field. However, obtaining a fully massive microstructure at all depths in a component can be difficult to achieve. Numerical simulation of microstructure formation can be used as a tool to anticipate the microstructure distribution in a component depending on the local chemistry and cooling conditions. The main objective of this work was to develop microstructure models describing the formation of the massive and lamellar microstructures. Two distinct modeling approaches have been used. The first approach is a deterministic model having for objective to predict the microstructure distribution in a cast and heat treated component. The model was designed to be integrated into a FEM or FDM heat flow solver in order to calculate microstructural quantities such as the proportion of phases and lamellar spacings at each nodal point of the mesh. The modeling approach is based on a combination of nucleation and growth laws which take into account the specific mechanisms of each phase transformation. Nucleation of massive and lamellar γ is described with classical nucleation theory, accounting for the fact that nuclei are formed predominantly at α/α grain boundaries. Growth of the massive γ grains is based on theory for interface-controlled reactions. A modified Zener model is used to calculate the thickening rate of the lamellar γ precipitates. The model incorporates the effect of particle impingement and coverage of the nucleation sites by the growing phases. The driving pressures of the phase transformations are obtained from Thermo-Calc based on the actual temperature and matrix composition. The deterministic approach was used to calculate CCT diagrams and lamellar spacings, which showed to be in good agreement with experimental data obtained from dedicated heat treatment experiments and from the literature. The model permitted investigating the influence of cooling rate, alloy chemistry and average α grain size upon the amount of massive γ and the average thickness and spacing of the lamellae. In particular, it indicated that the Al depletion of the α phase during lamellar precipitation seems to play an important role in the suppression of the massive transformation at moderate cooling rate and in the large lamellar spacings observed at low cooling rate. It was also found that Nb additions enhance the formation of massive γ by lowering the lamellar transformation temperature, increasing the T0 temperature and slowing down the lamellar growth due to the low diffusivity of Nb. The second approach was a cellular automaton (CA) model, which was used to describe the formation of the lamellar microstructure on the scale of a few microns. The objective of this approach was to make a direct description of the growth of a precipitate by an interfacial ledge mechanism, and then to evaluate whether the simplifications made in the deterministic model regarding this mechanism are appropriate. The modelling approach is based on the resolution of the diffusion equations in the α and γ phases. The ledge structure at the α/γ interface is described by introducing different types of cells in the CA grid. The CA model was used to describe the formation of the lamellar microstructure at isothermal conditions and at various cooling rates. The thickening kinetics of the lamellae, the lamellar spacings and the overall kinetics of the transformation were calculated with this approach and were compared with the results obtained with the deterministic model. It was found first that the thickening kinetics of the lamellae calculated with the deterministic model are slower than those calculated with the CA model. It was established that the method used in the deterministic model to account for the growth by a ledge mechanism always leads to a growth exponent of 0.5, which is characteristic of diffusion controlled reactions. In contrast, the CA approach yields growth exponents that are comprised between 0.5 and 1, as it is expected for growth in a mixed diffusion/interface controlled mode. The incorporation of a kinetic undercooling term into the deterministic model was found to be an appropriate correction. A good agreement was finally obtained in terms of lamellar spacings and overall kinetics. The underestimated thickening kinetics of the deterministic model was observed to be partly compensated by a higher nucleation rate at the beginning of the transformation. The overall kinetics of the lamellar transformation calculated with the deterministic model was found to be sufficiently accurate to correctly predict the competition between the lamellar and massive microstructures.

CELLULAR AUTOMATA MODELLING APPROACH FOR URBAN GROWTH

There has been a growing interest from academia and industry in developing circuits and systems for edge computing and quality control tasks in food production lines, where image-processing is frequently required. This paper outlines the required considerations for designing a fruit classification system based on image-processing using Cellular Automata (CA) models and integrating it into reconfigurable hardware (HW) such as Field Programmable Gate Arrays (FPGAs). Parallel processing in CA requires numerous processing elements to be implemented and mapping CA models to HW generally comes with limitations. Homogeneous CA arrays are easier to design and implement in HW but can be resource-demanding. To fill this gap, this study explores different alternatives for the HW implementation of CA models, particularly trading computational-parallelism for a more optimized use of the available HW resources. We conducted experimental tests of the designed HW system using the Digilent Nexys development board, and the operation was validated against software-based benchmarks for image-processing, particularly concerning edge-detection. The presented study provides a broader range of design solutions for the HW implementation of two-dimensional CA models and a better understanding of their advantages and disadvantages. The results show that solutions focusing on instruction-parallelism add some complexity to the conception and require more design effort, compared to homogeneous CA models composed of identical cells. However, the instruction-parallel design solutions can significantly improve the HW resource utilization, especially when implementing computationally intensive CA rules in FPGAs.

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.