地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (1): 62-68.doi: 10.3724/SP.J.1047.2015.00062

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山西省城镇空间分布特征Voronoi图建模分析

林慧(), 郑新奇*()   

  1. 中国地质大学(北京)信息工程学院,北京 100083
  • 收稿日期:2013-10-21 修回日期:2013-12-11 出版日期:2015-01-10 发布日期:2015-01-05
  • 通讯作者: 郑新奇 E-mail:linhui@cugb.edu.cn;zhengxq@cugb.edu.cn
  • 作者简介:

    作者简介:林 慧(1990-),女,硕士生,主要从事GIS开发与应用、空间数据挖掘等研究。E-mail:linhui@cugb.edu.cn

  • 基金资助:
    国土资源部公益性行业科研专项经费项目(201011018)

Data Mining and GIS Modeling of Urban Spatial Distribution Characteristics Based on Voronoi Diagram: Taking Shanxi Province as an Example

LIN Hui(), ZHENG Xinqi*()   

  1. School Information Engineering, China University of Geosciences, Beijing 100083, China
  • Received:2013-10-21 Revised:2013-12-11 Online:2015-01-10 Published:2015-01-05
  • Contact: ZHENG Xinqi E-mail:linhui@cugb.edu.cn;zhengxq@cugb.edu.cn
  • About author:

    *The author: CHEN Nan, E-mail:fjcn99@163.com

摘要:

随着我国新型城镇化规划的实施,如何识别城镇发展的不均衡,以及产生这些差异的原因已成为城镇化建设亟待解决的问题。本文利用Voronoi图的空间剖分特性,将城市中心性强度作为权重引入模型,建立山西省地级市加权Voronoi图,分析其空间影响范围,以判断山西省地级市发展的合理性和局限性;利用Delaunay图发现城市“空洞”,结合道路河流等矢量信息,通过叠加分析识别出待优先发展城镇;通过常规Voronoi图和变异系数Cv值判断本文模型的合理性和可行性。研究发现,山西省太原市的空间影响范围较大,导致周边地级市东西部发展较为不均衡;繁峙县、灵石县、新绛县条件较好,可以优先发展;通过常规Voronoi图和Cv值验证表明,本文所得结论与实际检验相符合。

关键词: Voronoi图, 空间数据挖掘, 配位数, 城市“空洞”, 变异系数

Abstract:

Strengthening urbanization has now become the main task of China’s modernization. Urban spatial distribution patterns are characterized by complexity and diversity. Through these characteristics, it is easy to notice problems such as the uneven distributions of urban areas within provinces, the excessive implemented levels in administrative divisions, and huge gaps existed between cities and towns in terms of their urbanization levels. Therefore, it is important to find out how to display the spatial distribution of urban area rationally and explore useful information. As one of the spatial data mining tools, Voronoi diagram can be used to implement spatial subdivision and reveal the scope of spatial influence generated by urban area. In this article, a Voronoi diagram of Shanxi Province, which is weighted by the city centricity intensity, is generated using the Weighted Voronoi Diagram Extension for ArcGIS 9.x. It is also used to analyze the spatial influence of cities in Shanxi and to evaluate the rationalities and limitations in developing these cities. Undeveloped areas in these cities are obtained by combining Voronoi diagram with Delaunay triangulation and overlay analysis. Counties to be developed are obtained by implementing overlay analysis on county, street, and river data. Finally, the rationality and feasibility are evaluated by integrating general Voronoi diagram with Cv values. Through the analysis, conclusions could be made as follows: Taiyuan city has a larger spatial influence area than the other cities, and it has a negative impact on the development of other cities; western and eastern areas in Shanxi are developed unevenly; the cities of Fanzhi, Lingshi, and Xinjiang have the advantages and potentials to be developed in priority. Through the integration of general Voronoi Diagram and Cv values, the results correspond well to the actual situations.

Key words: Voronoi diagram, spatial data mining, coordination number, city “void”, Cv