论文标题
对城市增长进行建模并用空间熵进行形式
Modeling Urban Growth and Form with Spatial Entropy
论文作者
论文摘要
熵是分形尺寸定义的物理碱基之一,广义分形尺寸由Renyi熵定义。使用分形维度,我们可以描述城市的生长和形式,并表征空间复杂性。为城市研究提出了许多分形模型和测量。但是,分形维度应用的先决条件是在城市中找到比例关系。在没有缩放特性的情况下,我们可以利用熵功能和测量。本文致力于研究如何通过使用空间熵来描述城市增长。通过与城市的分形维度增长模型的类比,可以得出一对熵增加模型,并可以构建一组基于熵的测量值来描述城市生长过程和模式。首先,利用逻辑功能和Boltzmann方程来建模熵增加城市生长的曲线。其次,使用一系列基于空间熵的索引来表征城市形式。此外,多重尺寸光谱被推广到空间熵光谱。结论如下。熵和分形维度既有相交,也具有不同的城市研究应用领域。因此,对于给定的空间测量量表,为简单起见,通常可以用空间熵代替分形维度。这项工作中提出的模型和测量值对于将熵和分形维度整合到城市空间分析的相同框架和理解城市的空间复杂性的相同框架中至关重要。
Entropy is one of physical bases for fractal dimension definition, and the generalized fractal dimension was defined by Renyi entropy. Using fractal dimension, we can describe urban growth and form and characterize spatial complexity. A number of fractal models and measurements have been proposed for urban studies. However, the precondition for fractal dimension application is to find scaling relations in cities. In absence of scaling property, we can make use of entropy function and measurements. This paper is devoted to researching how to describe urban growth by using spatial entropy. By analogy with fractal dimension growth models of cities, a pair of entropy increase models can be derived and a set of entropy-based measurements can be constructed to describe urban growing process and patterns. First, logistic function and Boltzmann equation are utilized to model the entropy increase curves of urban growth. Second, a series of indexes based on spatial entropy are used to characterize urban form. Further, multifractal dimension spectrums are generalized to spatial entropy spectrums. Conclusions are drawn as follows. Entropy and fractal dimension have both intersection and different spheres of application to urban research. Thus, for a given spatial measurement scale, fractal dimension can often be replaced by spatial entropy for simplicity. The models and measurements presented in this work are significant for integrating entropy and fractal dimension into the same framework of urban spatial analysis and understanding spatial complexity of cities.