地理科学 ›› 2015, Vol. 35 ›› Issue (5): 637-643.doi: 10.13249/j.cnki.sgs.2015.05.637

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基于局部聚类的网络Voronoi图生成方法研究

佘冰1(), 叶信岳2, 房会会3, 吴玲4, 朱欣焰1, 程叶青5   

  1. 1.武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079
    2.肯特州立大学地理系, 肯特 俄亥俄州 44242,美国
    3.河南大学黄河文明与可持续发展研究中心, 河南 开封 475003
    4. 中南财经政法大学刑事司法学院,湖北 武汉 430073
    5.海南师范大学地理与旅游学院,海南 海口 571158
  • 收稿日期:2014-01-29 修回日期:2014-04-11 出版日期:2015-05-20 发布日期:2015-05-20
  • 作者简介:

    作者简介:佘 冰(1987-),男,湖北黄石人,博士研究生,主要从事空间数据挖掘与分析研究。E-mail:coolnanjizhou@163.com

  • 基金资助:
    国家自然科学基金(41271401)、国家科技支撑计划项目(2012BAH35B03)、中央高校基本科研业务费项目(2722013JC030)、中南财经政法大学2012年引进人才项目(31541210702)项目资助

A Method for Integrating Network Voronoi and Spatial Clustering

Bing SHE1(), Xin-yue YE2, Hui-hui FANG3, Ling WU4, Xin-yan ZHU1, Ye-qing CHENG5   

  1. 1. State Key Lab of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China
    2. Computational Social Science Lab, Department of Geography, Kent State University, Kent 44242, United States
    3. Center for Yellow River Civilization and Sustainable Development,Henan University, Kaifeng, Henan 475003, China
    4. College of Criminal Justice, Zhongnan University of Economics and Law, Wuhan, Hubei 430073, China
    5. College of Geography and Tourism, Hainan Normal University, Haikou, Hainan 571158, China
  • Received:2014-01-29 Revised:2014-04-11 Online:2015-05-20 Published:2015-05-20

摘要:

提出一种将网络约束下的Voronoi和空间聚类相结合的方法,通过构造局部的聚类分析方法对网络边进行加权,根据实际的点过程性质可以把权重定义为加权或者乘权,进行标准化后与道路段本身长度融合进行计算,依此生成网络Voronoi图,以期理解城市街道的空间特性。以武汉市江汉区为例,对城市网格管理系统产生的城市事件进行算法验证,结果表明,该方法提供了一种灵活的网络约束下的服务区域划分工具,可用于基于网络空间点过程影响下的服务区划分,也可用于系统性地定量刻画城市管理的动态特性。

关键词: 网络Voronoi图, 局部Moran′s I 统计量, 加权边, 局部聚类

Abstract:

The planar space assumption of spatial cluster detection is invalid in the real world. The network space has opened a new gate to finer-scale spatial analysis, and provides a perspective for human dynamics. The urban street network is shaped by social and economic forces over time and also reflects the influences of governmental policies and cultural values. In the real world, any phenomenon whose location is represented through a street address system is inherently constrained by the street network. Hence, both events and their movements are constrained by the street network in the urban area. For example, the street-side business will cause more traffic, which lead to both management and commuting costs. Since the spatial point process is inherently probabilistic, it’s hard to set a fixed set of criteria, which would otherwise be dealt with as a spatial optimization problem. The weight of a given street segment will vary across space and over time when the activities of street-side business on this focal segment and nearby ones are considered. It is crucial to incorporate this information into urban management and urban studies, because equally-weighted street segments do not exist in the real world. The extension of Voronoi diagrams to the network space provides a useful tool in estimating service area in cities. Weighted Voronoi diagrams have been widely adopted to describe the capacity constraints. This proposed method develops a network Voronoi diagram with weighted links based on spatial cluster analysis. It borrows the strength from two large and growing literatures: Voronoi diagram and spatial cluster analysis. The weight is a central component in the construction of weighted Voronoi in urban street network. The weights are generated using local Moran’s I statistic. The weights, either additive or multiplicative, are normalized and transformed into the link length for constructing network Voronoi diagrams. The Monte Carlo simulation process is adopted to illustrate the statistical significance of detected clusters, and only links with significant p values are chosen to be weighted. The normalization interval determines how the clustering level influences the weights. The additive weight reflects the added constraint of the link attribute, while the multiplicative weight demonstrates the degree of influences imposed on the links. The conceptual foundations and technical details of this approach are elaborated in the case study of Wuhan City, China. The results show that the method is effective in incorporating the clustering criteria into the Voronoi construction, and provides an alternative tool for service area division. The constructed network Voronoi diagrams explicitly take into account the characteristics of underlying event distribution instead of a fixed set of criteria. This method sheds new light on micro-level spatial analysis, providing a perspective for observing how socio-economic urban activities, represented as network-constrained point distribution, shape the spatial structure and patterns in the metropolitan area. These constraints are often modelled on the generator points according to a set of predefined criteria. However, human activities are highly dynamic and constantly evolving. The outcomes of these activities are often represented as spatial point processes, which are also constrained by the network space. By means of clustering, the influences of the event points can be modelled as weights posed on the network links. Such weights can represent the congestions caused or times consumed in handling the events. Accounting these weighted links in the network Voronoi diagram will effectively capture the probabilistic nature of underlying point processes, and therefore more faithfully approximate the partition of street space. In a follow-up study, sensitivity analysis needs to be carried to test how the results might vary based on the street segment size to select.

Key words: Network Voronoi diagram, Local Moran’s I, weighted links, local clustering

中图分类号: 

  • P91