An Extended Minimum Spanning Tree method for characterizing local urban patterns
An Extended Minimum Spanning Tree method for characterizing local urban patterns
复制标题
用于表征当地城市模式的扩展最小生成树方法
DOI：
10.1080/13658816.2017.1384830
复制
发表时间：
2018-01-01
影响因子：
5.7
通讯作者：
Wu, Jianping
中科院分区：
文献类型：
--
作者：
Wu, Bin;Yu, Bailang;Wu, Jianping
详细而精确的城市建筑形态信息对于城市设计、景观评价、社会分析和城市环境研究至关重要。虽然已经进行了广泛的研究，城市建筑模式的提取，很少有研究同时考虑了空间邻近关系和形态属性的建筑单元水平。在这项研究中，我们提出了一个简单而新颖的图论方法，扩展的最小生成树（埃姆斯特），描述和表征当地的建筑模式，在建筑单元水平的大城市地区。首先从建筑物足迹数据和高分辨率光探测和测距数据中描绘和导出具有丰富的二维和三维建筑特征的建筑物对象。然后，我们提出了埃姆斯特方法来表示和描述空间邻近关系和建筑特征。此外，埃姆斯特组的建筑物对象到不同的局部连接的子集，通过应用基于完形理论的图划分方法。在此基础上，我们的埃姆斯特方法通过空间自相关分析和同质性指数来评估每个建筑物的特征，以发现局部模式。我们将所提出的方法应用于纽约市的史泰登岛，并成功地提取和区分各种当地的建筑模式在研究区域。结果表明，埃姆斯特是一个有效的数据结构，从地理和感知的角度来理解当地的建筑模式。我们的方法具有很大的潜力，确定当地的城市模式，并提供全面和必要的信息，城市规划和管理。
Detailed and precise information on urban building patterns is essential for urban design, landscape evaluation, social analyses and urban environmental studies. Although a broad range of studies on the extraction of urban building patterns has been conducted, few studies simultaneously considered the spatial proximity relations and morphological properties at a building-unit level. In this study, we present a simple and novel graph-theoretic approach, Extended Minimum Spanning Tree (EMST), to describe and characterize local building patterns at building-unit level for large urban areas. Building objects with abundant two-dimensional and three-dimensional building characteristics are first delineated and derived from building footprint data and high-resolution Light Detection and Ranging data. Then, we propose the EMST approach to represent and describe both the spatial proximity relations and building characteristics. Furthermore, the EMST groups the building objects into different locally connected subsets by applying the Gestalt theory-based graph partition method. Based on the graph partition results, our EMST method then assesses the characteristics of each building to discover local patterns by employing the spatial autocorrelation analysis and homogeneity index. We apply the proposed method to the Staten Island in New York City and successfully extracted and differentiated various local building patterns in the study area. The results demonstrate that the EMST is an effective data structure for understanding local building patterns from both geographic and perceptual perspectives. Our method holds great potential for identifying local urban patterns and provides comprehensive and essential information for urban planning and management.