The Improved Cellular Automata and Its Application in Delineation of Urban Spheres of Influence

Deng, Yu

doi:10.3390/su6128931

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Pn i,t in Equation (2) represents the conversion probability being affected by the state of other cells within its neighborhood. A square neighborhood within m × m cells is commonly used, and the probability of the conversion is given as [54]:…”

Section: Ca Model Calibrated By Logistic Regressionmentioning

confidence: 99%

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%

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

Liu

Feng

2016

Sustainability

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A Scene–Object–Economy Framework for Identifying and Validating Urban–Rural Fringe Using Multisource Geospatial Big Data

Yin,

Feng,

Jiang

et al. 2024

Applied Sciences

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Rapid urbanization has led to the emergence of urban–rural fringes, complex transitional zones that challenge traditional urban–rural dichotomies. While these areas play a crucial role in urban development, their precise identification remains a significant challenge. Existing methods often rely on single-dimensional metrics or administrative boundaries, failing to capture the multi-faceted nature of these zones. This study introduces a novel “Scene–Object–Economy” (SOE) framework to address these limitations and enhance the precision of urban–rural fringe identification. Our approach integrates multisource geospatial big data, including remote sensing imagery, nightlight data, buildings, and Points of Interest (POI), leveraging machine learning techniques. The SOE framework constructs feature from three dimensions: scene (image features), object (buildings), and economy (POIs). This multidimensional methodology allows for a more comprehensive and nuanced mapping of urban–rural fringes, overcoming the constraints of traditional methods. The study demonstrates the effectiveness of the SOE framework in accurately delineating urban–rural fringes through multidimensional validation. Our results reveal distinct spatial patterns and characteristics of these transitional zones, providing valuable insights for urban planners and policymakers. Furthermore, the integration of dynamic population data as a separate layer of analysis offers a unique perspective on population distribution patterns within the identified fringes. This research contributes to the field by offering a more robust and objective approach to urban–rural fringe identification, laying the groundwork for improved urban management and sustainable development strategies. The SOE framework presents a promising tool for future studies in urban spatial analysis and planning.

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The Improved Cellular Automata and Its Application in Delineation of Urban Spheres of Influence

Cited by 2 publications

References 18 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

A Scene–Object–Economy Framework for Identifying and Validating Urban–Rural Fringe Using Multisource Geospatial Big Data

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