A novel approach for urbanization level evaluation based on information entropy principle: A case of Beijing

A novel approach for urbanization level evaluation based on information entropy principle: A case of Beijing
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基于信息熵原理的城镇化水平评价新方法——以北京市为例

DOI:
10.1016/j.physa.2015.02.039
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发表时间:
2015-07
期刊:
Physica A: Statistical Mechanics and its Applications
影响因子:
--
通讯作者:
Lihe Chai
Lihe Chai
中科院分区:
其他
文献类型:
--
作者:
Jingjing Zhao;Lihe Chai

文献摘要

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城市化水平评价是指导城市管理者决策的重要科学依据。通过引入信息熵来描述指标之间的相互作用关系,推导出指标网络的整体结构参数矩阵及其动力学方程,并采用自组织特征映射模拟技术来描述指标网络的结构演化。这样,一个新的ULE模型普遍提出。然后,利用该模型对北京市2005-2012年的城市化水平进行了评价。计算了以35个微观指标为节点的指标网络的结构参数λ值。结果表明,北京城市化水平不断提高。2008年和2012年城镇化水平的大幅上升表明这两年城镇化水平有了明显提高,而2010年城镇化发展出现了快速调整。城市建设、经济发展、社会发展、生态环境和城乡发展五个中观子系统对北京城市化水平有不同的影响。模型的雷达图显示,经济发展和城乡发展对北京城市化的贡献变化最大,但城乡发展协调性较差的现象普遍存在。通过两种分析方法对北京市城市生活质量的分析,进一步探讨了指标网络选择的客观性和灵活性。最后,在应用实例的基础上,讨论了新模型的通用性和优越性.
Urbanization level evaluation (ULE) is an important scientific basis for guiding urban managers to make decisions. By introducing information entropy to describe the interactions between all indicators, a holistic structural parameter ξ, its dynamic equation and self-organizing feature map simulation technique are derived to describe the structural evolution of the indicator network. In this way, a novel ULE model is universally proposed. Then, we use the model to assess the evolutionary urbanization level of Beijing during 2005–2012. We calculate structural parameter ξ values of the indicator network with 35 microscopic indicators as nodes. The results show Beijing’s urbanization level has ever kept increasing. Large increase of ξ values in 2008 and 2012 represented significant improvements of urbanization level in these two years, while a rapid adjustment of urbanization development occurred in 2010. Five meso-scopic subsystems as urban construction, economic development, social development, ecological environment and urban–rural development affected Beijing’s urbanization level in different ways. The radar chart of the model shows the contributions of economic development and urban–rural development to Beijing’s urbanization changed most, while poor coordination of urban–rural development largely existed. By showing Beijing’s ULE based on two analytical ways, we further discuss the objectivity and flexibility in choosing indicator network. Finally, beyond the application case, we discuss the universality and superiority of the new model.
使用自组织特征图神经网络模型聚类城市多功能景观
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