Modelling dynamic urban expansion processes incorporating a potential model with cellular automata

He, Chunyang; Okada, Norio; Zhang, Qiaofeng; Shi, Peijun; Li, Jinggang

doi:10.1016/j.landurbplan.2007.12.010

Cited by 224 publications

(112 citation statements)

References 48 publications

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“…A variety of approaches have been used to model the spatial process of urban expansion, such as Markov chains (Ló pez et al, 2001) and spatial logistic regression (Cheng and Masser, 2003). In recent years, Cellular Automata (CA) techniques have gained attention as modeling tools for urban expansion process simulation (AlAhmadi et al, 2009;Barredo et al, 2003;Batty and Xie, 1994;He et al, 2008;He et al, 2006;Li and Yeh, 2000;Liu et al, 2008;Stevens et al, 2007;White and Engelen, 1993;Wu, 1998). The application of CA in urban expansion modeling can give insights into a wide variety of urban phenomena (Li and Yeh, 2002).…”

Section: Cellular Automatamentioning

confidence: 99%

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Ecological benefits assessment and spatial modeling of urban ecosystem for controlling urban sprawl in Eastern Beijing, China

Yang

Wang

et al. 2011

Ecological Complexity

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Section: Cellular Automatamentioning

confidence: 99%

“…The random disturbance term cell (i, j) at time t can be defined as (Barredo et al, 2003;He et al, 2008;Li and Yeh, 2002):…”

Section: Integration Of Neural Network and Ca In Modeling Of Urban Exmentioning

confidence: 99%

Ecological benefits assessment and spatial modeling of urban ecosystem for controlling urban sprawl in Eastern Beijing, China

Yang

Wang

et al. 2011

Ecological Complexity

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“…The application of models has been growing in recent years to predict likely future changes (Lambin et al, 2000;Veldkamp and Lambin, 2001;Epstein et al, 2002;He et al, 2008) and its relationship with the underlying driving mechanisms (Verburg, 2006;Verburg et al, 2009). Numerous spatial models have been developed to identify and and forecast the possible dynamic urban features (Li and Yeh, 2002).…”

Section: Introductionmentioning

confidence: 99%

Modelling land use/cover change in Lake Mogan and surroundings using CA-Markov Chain Analysis

Durmusoglu¹,

Tanriover²

2017

JEB

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Aim : Methodology :Results : Interpretation :Lake Mogan, having high ecological and cultural significance, has been under intense pressure of urbanization and industrialization due to its location on the periphery of the capital Ankara. In this study, we analyzed data from satellite remote sensing, Geographic Information System and Cellular Automata Markov Chain modelling to predict land use/cover changes in Lake Mogan and surrounding areas.Three images recorded in 1975 and 1999 (air photos) and 2009 (Quickbird image) were used to detect the land use/cover changes over the time. An object-based supervised classification approach to simulate changes in the study area for the future was performed. Land use/cover changes for the year 2029 was modelled using Cellular Automata Markov Chain Analysis with the support of suitability analysis.The results indicated that most of the agricultural areas will be converted into settlements over the next 40 years. Modelling results for 2029 also showed that land degradation activities are likely to continue in the future.According to model on land use changes in Lake Mogan and surrounding areas, there is an immediate need to take long-term measures for sustainable land development based on the regional scales, preferably on the basin level.

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“…According to the 2014 Revision of World Urbanization Prospects [1], the urban population is projected to increase by 2.46 billion, an increase from 3.88 billion (54%) in 2014 to 6.34 billion (66%) in 2050. This projected increase in the urban population will create various demands, and the resources necessary for daily life and production in cities are believed to be an important driving force that will contribute to changes in urban land use [2][3][4]. Additional agricultural and rural lands will be converted for urban development, resulting in an expanded urban landscape [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Application of Environmental Change Efficiency to the Sustainability of Urban Development at the Neighborhood Level

Kuo

Tsou

2015

Sustainability

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This study describes a new assessment model framework, termed the driving forces-pressure-state-impact-policy and pattern (DPSIP) model, for environmental change efficiency in urban land development, based on urban sustainable development and the theory of economic efficiency evaluation. A spatial and measurable efficiency value is defined for environmental changes in urban land development, which provides a comprehensive evaluation index for the efficiency of urban development and its environmental impact. This type of urban interior sustainability is considered new within the context of global environmental changes. We identify nine important indicators to evaluate the relative efficiency of 233 neighborhoods in Tainan, Taiwan. The results indicate that the average environmental change efficiency is 89.44%, which shows clear spatial differentiation. The key indicators affecting the efficiency score are area, population density, location, mixed land uses, the floor area ratio, and the impervious ratio. In the future, urban design can reduce environmental impacts and enhance efficiency values.

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Modelling dynamic urban expansion processes incorporating a potential model with cellular automata

Cited by 224 publications

References 48 publications

Ecological benefits assessment and spatial modeling of urban ecosystem for controlling urban sprawl in Eastern Beijing, China

Ecological benefits assessment and spatial modeling of urban ecosystem for controlling urban sprawl in Eastern Beijing, China

Modelling land use/cover change in Lake Mogan and surroundings using CA-Markov Chain Analysis

Application of Environmental Change Efficiency to the Sustainability of Urban Development at the Neighborhood Level

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