The analysis and delimitation of Central Business District using network kernel density estimation
The analysis and delimitation of Central Business District using network kernel density estimation
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DOI：
10.1016/j.jtrangeo.2015.04.008
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发表时间：
2015-05
影响因子：
6.1
通讯作者：
Wenhao Yu;T. Ai;Shiwei Shao
中科院分区：
文献类型：
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作者：
Wenhao Yu;T. Ai;Shiwei Shao
中央商务区（CBD）是城市规划和决策管理的核心区域。CBD的制图定义与表现对于研究城市发展及其功能具有重要意义。为了促进这些过程，核密度估计（KDE）是一个非常有效的工具，因为它考虑了服务的衰减影响，并允许将信息从非常简单的输入散点图丰富到平滑的输出密度表面。然而，大多数现有的密度分析方法在欧几里得空间表示下考虑均匀和各向同性空间中的地理事件。考虑到城市环境中的物理运动通常受到街道网络的约束，我们提出了一种不同的基于网络配置的CBD划分方法。首先，从中心活动的位置出发，提出了一个浓度指数，通过密度面来可视化功能城市环境，密度面是用网络距离而不是欧几里得距离来细化的。然后结合网络距离计算问题的特点，提出了一种基于流扩展仿真的有效方法。
Central Business District (CBD) is the core area of urban planning and decision management. The cartographic definition and representation of CBD is of great significance in studying the urban development and its functions. In order to facilitate these processes, the Kernel Density Estimation (KDE) is a very efficient tool as it considers the decay impact of services and allows the enrichment of the information from a very simple input scatter plot to a smooth output density surface. However, most existing methods of density analysis consider geographic events in a homogeneous and isotropic space under Euclidean space representation. Considering the case that the physical movement in the urban environment is usually constrained by a street network, we propose a different method for the delimitation of CBD with network configurations. First, starting from the locations of central activities, a concentration index is presented to visualize the functional urban environment by means of a density surface, which is refined with network distances rather than Euclidean ones. Then considering the specialties of network distance computation problem, an efficient way supported by flow extension simulation is proposed. Taking Shenzhen and Guangzhou, two quite developed cities in China as two case studies, we demonstrate the easy implementation and practicability of our method in delineating CBD.