Analyzing spatiotemporal land use change using an urban growth model based on multilevel logistic regression and future land demand scenarios

Lee, Changyeon; Lee, Sugie

doi:10.1016/j.apgeog.2023.103099

Cited by 2 publications

(1 citation statement)

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“…Indicator-based methods establish quantitative models linking land use demand to key influencers, setting fixed indicators for each land type based on historical data analysis [40], such as residential land per capita, industrial land per unit of GDP, and transportation land density [41]. Regression analysis is an effective method for forecasting future land demand by modeling the relationships between land use and various economic, demographic, and environmental factors [42][43][44][45]. Moreover, as research into land use change intensifies, advanced algorithmic models such as system dynamics, decision trees, neural networks, support vector machines, and random forests are increasingly being applied to forecast land demand [46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Towards a Comprehensive Framework for Regional Transportation Land Demand Forecasting: Empirical Study from Yangtze River Economic Belt, China

Wang,

Zhang

2024

Land

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China is currently experiencing rapid expansion in its transportation land. To promote sustainable land use, accurately estimating transportation land demand is crucial. This study aims to develop a comprehensive framework for urban transportation land forecasting within the Yangtze River Economic Belt (YREB), providing support for optimizing regional land allocation. Employing methods such as meta-analysis, statistical analysis, and BP neural network analysis, this study forecasts the transportation land demand of 127 cities in the YREB. The study findings indicate that cities with high transportation land demand are mainly distributed in the middle and upper reaches of the Yangtze River. Moreover, the growth rate of transportation land in the upper reaches significantly outstrips that in the middle and lower reaches, suggesting a focus shift in transportation infrastructure construction toward the upper regions. Additionally, some cities within the YREB face a mismatch between the supply and demand of transportation land, necessitating proactive adjustments to their land supply plans to achieve a balance between supply and demand. The main contribution of this study is the development of a comprehensive and adaptable framework that guides the development of future strategies for optimal land allocation by forecasting transportation land demand at a regional level.

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