地理科学进展 ›› 2015, Vol. 34 ›› Issue (10): 1259-1265.doi: 10.18306/dlkxjz.2015.10.006

• 城市与区域 • 上一篇    下一篇

基于邻域扩展量化法的城市边界识别

谭兴业, 陈彦光*()   

  1. 北京大学城市与环境学院,北京 100871
  • 收稿日期:2015-02-01 接受日期:2015-06-01 出版日期:2015-10-20 发布日期:2015-10-20
  • 通讯作者: 陈彦光 E-mail:chenyg@pku.edu.cn
  • 作者简介:

    作者简介:谭兴业(1989-),男,辽宁沈阳人,硕士生,主要从事GIS的城市空间分析研究,E-mail: tanxingy@mail2.sysu.edu.cn

  • 基金资助:
    国家自然科学基金项目(41171129)

Urban boundary identification based on neighborhood dilation

Xingye TAN, Yanguang CHEN*()   

  1. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
  • Received:2015-02-01 Accepted:2015-06-01 Online:2015-10-20 Published:2015-10-20
  • Contact: Yanguang CHEN E-mail:chenyg@pku.edu.cn

摘要:

城市空间分析的基本条件是可靠的测度,而城市的基本测度是规模。客观定义城市边界是有效确定城市规模的技术前提。近年来国内外学者提出几种城市边界识别的方法,其中能够定量反映城市内部实体空间组织关系的多采用矢量图像。但这些矢量数据的获取十分困难,且实时性差。因此,本文借鉴前人的研究成果,基于邻域扩展量化和标度思想,提出一种应用于遥感栅格图像上的城市边界识别方法。该方法的本质是一种空间邻域融合法,通过改变像元邻域的作用范围,可以得到不同的空间集群数目;借助搜索范围与集群数目的标度关系确定一个客观的半径,据此可以利用GIS技术确定城市边界。将该方法应用于北京地区多个年份的遥感图像,发现了像元的有效邻域作用范围。此方法以栅格图像为基础,数据实时性好并且获取容易,计算过程简便,在未来的城市边界研究过程中,可望与现有的方法相互补充。

关键词: 城市边界识别, 城市形态, 城市集群, 邻域扩展量化, 分形, 标度, 北京

Abstract:

Urban spatial analysis should be based on reliable measurements, and the most basic measurement of a city is its size. Defining urban boundaries objectively is fundamental for determining effective city size. In recent years, a number of Chinese and international scholars have developed improved methods of urban boundary identification. Among these, the majority apply vector data that can reflect the spatial organization relationships of entities internal of cities. However, access to these vector data is often limited. In this study, based on existing research a new method of urban boundary identification with remote sensing data as input and using neighborhood dilation and quantification is put forward. Our method takes a spatial neighboring merging approach. By changing the neighboring range of pixels, different numbers of spatial clusters are obtained. An optimal radius can be determined according to the scaling relationships between the neighboring range of pixels and the numbers of spatial clusters. GIS technology is then adopted to define urban boundaries. By applying this method to analyze remote sensing images of the Beijing area, we found the effective range of pixels. Remote sensing data used by this method are characterized by real-time acquisition and easy access. Also, the calculation procedure is straightforward. Thus, in future efforts of urban boundary identification, our new method may provide a complement to existing methods.

Key words: urban boundary identification, urban patterns, city clustering, neighborhood dilation and quantification, fractals, scaling, Beijing