地理学报 ›› 2017, Vol. 72 ›› Issue (8): 1432-1443.doi: 10.11821/dlxb201708008

• 城市研究 • 上一篇    下一篇

武汉市城市空间集聚要素的分布特征与模式

焦利民1,2(), 李泽慧1,2, 许刚1,2, 张博恩1,2, 董婷1,2, 谷岩岩3   

  1. 1. 武汉大学资源与环境科学学院,武汉 430079
    2. 武汉大学地理信息系统教育部重点实验室,武汉 430079
    3. 武汉大学测绘遥感信息工程国家重点实验室,武汉 430079
  • 收稿日期:2016-08-30 修回日期:2017-03-27 出版日期:2017-08-20 发布日期:2017-08-23
  • 作者简介:

    作者简介:焦利民(1977-), 男, 河南安阳人, 博士, 教授, 博士生导师, 主要从事空间数据分析与建模、土地利用/覆盖变化与城市扩张方面的研究。E-mail:lmjiao027@163.com

  • 基金资助:
    国家自然科学基金项目(41571385)

The characteristics and patterns of spatially aggregated elements in urban areas of Wuhan

Limin JIAO1,2(), Zehui LI1,2, Gang XU1,2, Boen ZHANG1,2, Ting DONG1,2, Yanyan GU3   

  1. 1. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
    2. Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China
    3. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Received:2016-08-30 Revised:2017-03-27 Online:2017-08-20 Published:2017-08-23
  • Supported by:
    National Natural Science Foundation of China, No.41571385

摘要:

不同城市要素的集聚现象具有不同的空间模式,定量化研究城市要素集聚模式的差异和联系对于理解城市发展机理、合理制定城市规划具有重要意义。本文以武汉市为例,获取不透水表面、商业服务业网点(POI)、人口、容积率、城市道路等城市要素数据;采用核密度估计法识别城市主次中心,从城市要素的分布形态、集聚程度、集聚模式等来研究城市要素的空间分布格局特征。研究发现,武汉市呈现“一主七副”的多中心结构,各城市要素从城市中心向外呈反S型的圈层递减。采用集聚度指数衡量城市要素的集聚水平,结果显示商业POI、人口密度的集聚程度最大,其次是容积率、道路密度、不透水表面占比。高度集聚的商业POI、人口密度呈现出点状模式与带状模式相结合的分布模式,中度集聚的道路密度、容积率呈现点状模式、环状模式与轴状模式相结合的分布模式,低度集聚的不透水表面密度主要表现为环状模式。城市中心的吸引、立体空间开发等促进了城市要素的向心集聚,交通干线、稀缺景观资源的廊道效应等重塑了城市要素的空间分布形态。

关键词: 城市要素, 空间集聚, 核密度估计, 梯度分析, 反S方程, 武汉市

Abstract:

Different urban elements may exhibit various aggregation patterns. It is of great significance to quantitatively investigate the disparity and connection among various aggregation patterns of urban elements for understanding the mechanism of urban development and supporting urban planning. Taking Wuhan city, Central China, as a case, we collected five types of urban elements, namely, impervious surface, population density, Point of Interest (POI), plot ratio, and road network, to explore their spatial distribution and characteristics of aggregation patterns. We first used Kernel Density Estimation (KDE) method to identify city centers and we found that there is one major center and seven sub-centers in Wuhan. Then we partitioned the study area by gradient analysis, and calculated the densities of urban elements. The density of urban element decreases outward from the centers, which is fitted well using "Inverse S-shaped" function. We used Concentration Degree Index (CDI) to reflect the aggregation degree of urban elements. The results indicate that the degrees of the aggregation of urban elements are: Commercial POI > population > plot ratio > road. Commercial POI and population are highly aggregated in the urban core area, while plot ratio and road are moderately aggregated in the urban core area. The spatial patterns of highly aggregated commercial POI and population are the combination of point pattern and zonal pattern, while the spatial patterns of moderately aggregated road network and plot ratio are the combination of point pattern, ring pattern and axial pattern. As for the lowly aggregated impervious surface, it shows a ring pattern. The attraction effect of city centers and vertical development promote the center-oriented agglomeration of urban elements. At the same time, the transportation lines and corridor effects of the scarce landscape resources reshape the spatial distribution pattern of urban elements.

Key words: urban elements, spatial agglomeration, kernel density estimation, gradient analysis, inverse S-shaped function, Wuhan city