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Demand prediction and regulation zoning of urban-industrial land: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China

机译:城镇工业用地需求预测与调控区划:以北京-天津-河北城市群为依据

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摘要

As a main type of urban construction land, urban-industrial land is used to provide the judging criteria for construction land scale in the planning period when urban population, industrial development, investment scale, and other conditions are uncertain in China; however, research on expected indicator such as urban-industrial land in overall land use plan mainly focuses on qualitative analysis; quantitative analysis research has not yet been carried out. Using MATLAB R2016a software modeling tools to establish GM (1, 1) model and RBF neural network model, respectively, this paper predicted the demand of urban-industrial land in Beijing-Tianjin-Hebei Urban Agglomeration. Comparing the predicated results with the actual value of urban-industrial land in Beijing, Tianjin, and 11 prefecture-level cities in Hebei Province, we determined the reasonable prediction model for urban-industrial land after testing the accuracy of the two prediction models. The results showed that the RBF neural network model was the more reasonable prediction model for urban-industrial land. Using the predicted results of the RBF neural network model, combining expected indicators of overall land use plan (2006-2020) in Beijing and Tianjin, as well as 11 prefecture-level cities in Hebei Province in the planning target year, determined remaining usable time of urban-industrial land. Finally, combined with the actual scale of urban-industrial land in 2015 and the predicated scale of urban-industrial land in 2020, the remaining usable time of each city's urban-industrial land was calculated in terms of the average annual growth rate of urban-industrial land from 2009 to 2015. According to the comparative relationship between the remaining usable time and the remaining time of the overall land use plan (5 years), urban-industrial lands were divided into three kinds of regulation zones: reasonable reduction zone, optimized adjustment zone, and core development zone. The policy implications for urban-industrial land in each regulation zone were also provided. This paper can provide reference for regulation zoning of urban-industrial land in developing countries and regions.
机译:在城市人口,工业发展,投资规模等不确定的条件下,城市工业用地作为城市建设用地的主要类型,为规划期内的建设用地规模提供了判断标准。但是,在土地利用总体规划中对城市工业用地等预期指标的研究主要集中在定性分析上。定量分析研究尚未进行。利用MATLAB R2016a软件建模工具分别建立了GM(1,1)模型和RBF神经网络模型,预测了京津冀城市群的城市工业用地需求。将预测结果与北京,天津和河北省11个地级市的城市工业用地的实际价值进行比较,通过测试这两种预测模型的准确性,确定了合理的城市工业用地预测模型。结果表明,RBF神经网络模型是较为理想的城市工业用地预测模型。利用RBF神经网络模型的预测结果,结合规划目标年度北京,天津以及河北省11个地级市总体土地利用规划(2006-2020年)的预期指标,确定剩余可用时间城市工业用地。最后,结合2015年城市工业用地的实际规模和2020年城市工业用地的预计规模,以城市-城市平均年增长率为基础,计算每个城市的城市工业用地剩余时间。 2009年至2015年工业用地。根据剩余使用时间与土地利用总体规划(5年)剩余时间之间的比较关系,将城市工业用地分为三种调节区:合理的减少区,优化的调整区和核心开发区。还提供了每个管制区内对城市工业用地的政策含义。本文可为发展中国家和地区城市工业用地调控区划提供参考。

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