Simulation of urban expansion patterns by integrating auto-logistic regression, Markov chain and cellular automata models

Liu, Yaobin; Dai, Lu; Xiong, Hao

doi:10.1080/09640568.2014.916612

Cited by 39 publications

(25 citation statements)

References 55 publications

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“…Spatial factors commonly include the proximity of a cell to the urban center, district centers, main roads and other urban facilities. These parameters were derived using logistic regression given that this modeling approach has been proven as reliable in capturing CA parameters [17,28,53]. Pn i,t in Equation (2) represents the conversion probability being affected by the state of other cells within its neighborhood.…”

Section: Ca Model Calibrated By Logistic Regressionmentioning

confidence: 99%

“…The parameters of transition rules of the LogCA model were calculated using a logistic regression method [13,17,18,28,52]. In a tessellated space, CA determines the state of a non-urban cell i at time t + 1 as the integrated effects of itself and its neighboring cells at time t according to land transition rules.…”

Section: Ca Model Calibrated By Logistic Regressionmentioning

confidence: 99%

“…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%

“…Liu and Feng [17] extended the LogCA model by applying continuous cell states to represent the progressive rural-to-urban land transition, with a case study of Gold Coast, Australia. Reference [28] incorporated a Markov chain into the LogCA model to build a hybrid CA model for modeling dynamic land change in the Poyang Lake region of China. These studies show that the structure of the LogCA model allows for flexibility in incorporating changes into development regulations; it also allows for detailed spatial interaction settings to model urban growth drivers and processes.…”

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: Ca Model Calibrated By Logistic Regressionmentioning

confidence: 99%

Section: Ca Model Calibrated By Logistic Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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Performance of habitat offsets for species conservation in dynamic human‐modified landscapes

et al. 2023

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Biodiversity offsets are a popular policy tool for mitigating the impact of development on biodiversity, but the ecological success of offsets arise from complex interactions among socio‐economic, ecological and policy processes, making outcomes challenging to assess. Many offset policies use habitat surrogates to determine offset requirements, rather than using direct measures of impacted biota, and this can lead to poor outcomes for species. One potential solution to this is for offsets to be delivered by a public agency (agency‐led) rather than by developers (developer‐led). This is because agencies may be able to strategically choose offset sites that maximise outcomes for species (e.g. abundance), while there may be little reason for developers to act strategically in this way when offset requirements are based purely on habitat surrogates. Yet, the success of a strategic agency‐led approach is likely to depend on patterns of development and offset site availability. To examine this, we developed a novel integrated spatially explicit model of land‐use change, habitat, species abundance and offset regulation. We apply the model to the Queensland Government's Environmental Offsets Policy for koalas Phascolarctos cinereus in South East Queensland, Australia, and test how patterns of development and offset site availability influence the performance of agency‐led versus developer‐led offsets. When potential offset sites were plentiful, agency‐led offsets tended to outperform developer‐led offset delivery for maximising koala abundance while achieving similar or better outcomes for habitat area. Yet, when potential offset sites were rare, the relative performance of agency‐led offset was often poor, and offset requirements for habitat area were less likely to be met. Different spatial patterns of development had little effect on the relative performance of agency‐led versus developer‐led offsets. Our analysis shows that agency‐led offsets with strategic choices of offset sites can improve species' outcomes for habitat‐based offsets but can also risk failing to meet habitat area requirements when the availability of offset sites is low. Importantly, our integrated spatial model provides a holistic approach to assessing policy options for biodiversity offsets in dynamic human‐modified landscapes. Read the free Plain Language Summary for this article on the Journal blog.

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Modelling Urban Growth Under Contemporary China’s Transferable Development Rights Programme: A Case Study from Ezhou, China

Cheng

2023

Urban Sustainability

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Simulation of urban expansion patterns by integrating auto-logistic regression, Markov chain and cellular automata models

Cited by 39 publications

References 55 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

Performance of habitat offsets for species conservation in dynamic human‐modified landscapes

Modelling Urban Growth Under Contemporary China’s Transferable Development Rights Programme: A Case Study from Ezhou, China

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