地理学报 ›› 2017, Vol. 72 ›› Issue (2): 213-223.doi: 10.11821/dlxb201702003

• 城市与区域发展 • 上一篇    下一篇

城市范围界定与标度律

董磊1(), 王浩2,3, 赵红蕊2,3()   

  1. 1. 清华大学建筑学院,北京 100084
    2. 清华大学土木工程系地球空间信息研究所,北京 100084
    3. 清华大学3S中心,北京 100084
  • 收稿日期:2016-04-25 修回日期:2016-10-18 出版日期:2017-02-15 发布日期:2017-04-28
  • 作者简介:

    作者简介:董磊(1988-), 男, 博士生, 主要从事城市数据与空间分析研究。E-mail: arch.dongl@gmail.com

  • 基金资助:
    国家自然科学基金项目(41571414);清华大学自主科研项目(2015THZ01)

The definition of city boundary and scaling law

Lei DONG1(), Hao WANG2,3, Hongrui ZHAO2,3()   

  1. 1. School of Architecture, Tsinghua University, Beijing 100084, China
    2. Institute of Geomatics,Department of Civil Engineering, Tsinghua University, Beijing 100084, China
    3. 3S Center, Tsinghua University, Beijing 100084, China
  • Received:2016-04-25 Revised:2016-10-18 Online:2017-02-15 Published:2017-04-28
  • Supported by:
    National Natural Science Foundation of China, No.41571414;Tsinghua University Initiative Scientific Research Program, No.2015THZ01

摘要:

标度律作为城市发展的重要规律之一,反映了城市经济活动产值、基础设施数量等要素随城市人口规模的变动情况,在城市研究领域引起了广泛讨论。但由于不同国家的城市统计数据对应的空间范围各不相同,导致标度律系数受城市边界范围选取影响很大。本文通过比较中、美两国统计数据对应的空间范围,并结合普查、城市统计年鉴和遥感数据,计算了不同空间范围对应的标度律系数。结果表明:① 不同空间尺度和数据源得到的标度律系数有较大差异。就空间尺度而言,市辖区比市域范围的数据更符合标度律模型,因为中国城市市域范围内还存在大量的非城市化地区,并不符合标度律模型的适用条件;就数据源而言,遥感数据比城市统计年鉴数据有更好的拟合优度;② 与美国城市相比,中国城市人口集聚带来的经济增长率更高(标度律系数更高),市辖区人口每增加一倍,经济规模可增加122%,这一数字在美国是111%;而在家庭能源消耗(用水、用电)和土地利用方面,中国城市的效率更低;③ 从中国城市内部对比来看,大城市与中小城市在经济规模、土地利用方面的标度律(集聚效率)明显不同,人口集聚效应带来的大城市经济增长率、工资收入要远高于中小城市;能源消耗方面,中小城市比大城市更有效率。最后,本文还从建立更加有效的统计单元、传统统计数据与大数据结合、模型机制探索3个方面阐述了城市标度律未来可能的研究方向。

关键词: 城市范围, 标度律, 异速增长, 集聚效率

Abstract:

Scaling laws are powerful reflectors of the variations of the output of urban economic activities and the number of infrastructures with urban population. However, the difference in spatial definition of cities and data sources by countries leads to different statistical results of scaling law. We aim to analyse the difference in this paper by calculating regression coefficients of scaling law at different spatial scales, combined with census data, urban statistical yearbook data and remote sensing data of China. The conclusions are shown as follows: (1) Scaling coefficients change with both spatial scales and data sources. For spatial scales, scaling law is more agreeable with the data of urban municipal districts than with those of the whole city area in China. As there is a large number of non-urbanized areas within cities; these regions do not meet the assumptions of scaling law model. For data source, remote sensing data have a better fitting result than urban statistical yearbook data. (2) Comparatively speaking, urban population agglomeration contributes more to economic growth in China than it does in the US, but China has lower energy consumption and land-use efficiency. For example, the Gross Regional Product (GRP) scaling indicator of China is 1.22, while it is 1.11 in the United States. (3) Population agglomeration contributes more to the economic growth in large cities than in small cities. This may explain the emerging trends of urban immigrants in large cities of China. However, for energy consumption, small and medium-sized cities are more efficient than large cities. In addition, this paper discusses the potential direction for urban scaling research from three aspects: establishing more effective statistical units, combining traditional survey with big data analysis, and exploring mechanics behind scaling models.

Key words: city boundary, scaling law, allometric growth, agglomeration effect