A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing

Feng, Yongjiu; Liu, Yan

doi:10.1080/13658816.2012.695377

Cited by 79 publications

(68 citation statements)

References 49 publications

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“…Over the past decades, CA has grown as a spatial simulation tool to investigate the dynamics of human-environment systems and their interactions [11][12][13][14][15]. Many studies on CA have concentrated on the definition of transition rules using different methods, which include logistic regression [16][17][18], particle swarm optimization [19], fuzzy set [20,21], simulated annealing algorithm [22], genetic algorithm [23,24], higher dimensional space [25,26], and Markov chain [27][28][29]. The CA models are usually applied in simulating and understanding the urban dynamics [30][31][32] and land use and cover change [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

“…It is necessary for policymakers to use science-and evidence-based scenario modeling to guide future urban development and be able to adequately assess the impact of different strategies, whether the focus is on economic growth or environmental protection [37]. Although CA-based urban and land use modeling in Chinese cities have been extensively documented [19,22,28,38,39], few are concerned with predicting possible future scenarios, especially at the current turning point in time concerning urbanization, population growth, economic prosperity, and environmental protection [40]. Ningbo is an important harbor city situated in the east coastal area of Zhejiang province, China.…”

Section: Introductionmentioning

confidence: 99%

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Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China

Liu

Feng

2016

Sustainability

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Coastal cities in China are challenged by multiple growth paths and strategies related to demands in the housing market, economic growth and eco-system protection. This paper examines the effects of conflicting strategies between economic growth and environmental protection on future urban scenarios in Ningbo, China, through logistic-regression-based cellular automata (termed LogCA) modeling. The LogCA model is calibrated based on the observed urban patterns in 1990 and 2015, and applied to simulate four future scenarios in 2040, including (a) the Norm-scenario, a baseline scenario that maintains the 1990-2015 growth rate; (b) the GDP-scenario, a GDP-oriented growth scenario emphasizing the development in city centers and along economic corridors; (c) the Slow-scenario, a slow-growth scenario considering the potential downward trend of the housing market in China; and (d) the Eco-scenario, a slow-growth scenario emphasizing natural conservation and ecosystem protections. The CA parameters of the Norm-and Slow-scenarios are the same as the calibrated parameters, while the parameters of proximities to economic corridors and natural scenery sites were increased by a factor of 3 for the GDP-and Eco-scenarios, respectively. The Norm-and GDP-scenarios predicted 1950 km 2 of new growth for the next 25 years, the Slow-scenario predicted 650 km 2 , and the Eco-scenario predicted less growth than the Slow-scenario. The locations where the newly built-up area will emerge are significantly different under the four scenarios and the Slow-and Eco-scenarios are preferable to achieve long-term sustainability. The scenarios are not only helpful for exploring sustainable urban development options in China, but also serve as a reference for adjusting the urban planning and land policies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China

Liu

Feng

2016

Sustainability

Self Cite

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“…One important issue in CA modeling is the quantification of the impacts of the factors that drive urban growth and land use change at both global and local scales. Many approaches have been developed to define CA transition rules and each is aimed at improving the overall accuracy and reducing errors of simulation [21][22][23][24]. These approaches vary widely in theoretical assumptions, underlying methodologies, and spatio-temporal resolutions and extents [25].…”

Section: Introductionmentioning

confidence: 99%

“…This model was used to simulate the multiple land use changes in a rapidly growing area of Guangdong Province, China. A heuristic CA model of urban land use change was proposed based on a simulated annealing (SA) algorithm and was successfully applied to simulate the urban growth in one of Shanghai's outer suburbs [22]. This model was built around a function that minimizes the difference (residual) between observed and simulated land use patterns, resulting in improved locational accuracy when compared to a logistic-regression-based CA model (named logistic-CA).…”

Section: Introductionmentioning

confidence: 99%

“…This model was built around a function that minimizes the difference (residual) between observed and simulated land use patterns, resulting in improved locational accuracy when compared to a logistic-regression-based CA model (named logistic-CA). Other heuristic optimization algorithms such as genetic algorithms (GA) and particle swarm optimization (PSO) have been used to optimize CA parameters from logistic regression and calibrate CA models [22,[26][27][28]. A landscape expansion index was incorporated into CA (LEI-CA) to simulate both the adjacent and outlying urban growth of Dongguan City in southern China [15].…”

Section: Introductionmentioning

confidence: 99%

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Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

Feng

Liu

Chen

et al. 2016

IJGI

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Abstract:We developed a geographic cellular automata (CA) model based on partial least squares (PLS) regression (termed PLS-CA) to simulate dynamic urban growth in a geographical information systems (GIS) environment. The PLS method extends multiple linear regression models that are used to define the unique factors driving urban growth by eliminating multicollinearity among the candidate drivers. The key factors (the spatial variables) extracted are uncorrelated, resulting in effective transition rules for urban growth modeling. The PLS-CA model was applied to simulate the rapid urban growth of Songjiang District, an outer suburb in the Shanghai Municipality of China from 1992 to 2008. Among the three components acquired by PLS, the first two explained more than 95% of the total variance. The results showed that the PLS-CA simulated pattern of urban growth matched the observed pattern with an overall accuracy of 85.8%, as compared with 83.5% of a logistic-regression-based CA model for the same area. The PLS-CA model is readily applicable to simulations of urban growth in other rapidly urbanizing areas to generate realistic land use patterns and project future scenarios.

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A Framework to Probe Uncertainties in Urban Cellular Automata Modelling Using a Novel Framework of Multilevel Density Approach: A Case Study for Wallonia Region, Belgium

Chakraborty

Mustafà

Omrani

et al. 2023

The Urban Book Series

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Urban expansion models are widely used to understand, analyze and predict any peculiar scenario based on input probabilities. Modelling and uncertainty are concomitant, and can occur due to reasons ranging from-discrepancies in input variables, unpredictable model parameters, spatio-temporal variability between observations, or malfunction in linking model variables under two different spatiotemporal scenarios. However, uncertainties often occur because of the interplay of model elements, structures, and the quality of data sources employed; as input parameters influence the behavior of cellular automaton (CA) models. Our study aims to address these uncertainties. While most studies consider neighborhood effects, timestep and spatial resolution, our study uniquely focuses on the susceptibility of multi density classes and varying cell size on uncertainty. Hence this chapter offers a theoretical elucidation of the concepts, sources, and strategies for managing uncertainty under various criteria as well as an algorithm for enumerating the model's accuracy for Wallonia, Belgium.

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A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing

Cited by 79 publications

References 49 publications

Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China

Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

A Framework to Probe Uncertainties in Urban Cellular Automata Modelling Using a Novel Framework of Multilevel Density Approach: A Case Study for Wallonia Region, Belgium

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