Rank-size distributions of Chinese cities: Macro and micro patterns

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


A large number of studies have been conducted to find a better fit for city rank-size distributions in different countries. Many theoretical curves have been proposed, but no consensus has been reached. This study argues for the importance of examining city rank-size distribution across different city size scales. In addition to focusing on macro patterns, this study examines the micro patterns of city rank-size distributions in China. A moving window method is developed to detect rank-size distributions of cities in different sizes incrementally. The results show that micro patterns of the actual city rank-size distributions in China are much more complex than those suggested by the three theoretical distributions examined (Pareto, quadratic, and q-exponential distributions). City size distributions present persistent discontinuities. Large cities are more evenly distributed than small cities and than that predicted by Zipf’s law. In addition, the trend is becoming more pronounced over time. Medium-sized cities became evenly distributed first and then unevenly distributed thereafter. The rank-size distributions of small cities are relatively consistent. While the three theoretical distributions examined in this study all have the ability to detect the overall dynamics of city rank-size distributions, the actual macro distribution may be composed of a combination of the three theoretical distributions.

Original languageEnglish (US)
Pages (from-to)577-588
Number of pages12
JournalChinese Geographical Science
Issue number5
StatePublished - Oct 1 2016
Externally publishedYes


  • China
  • Pareto’s law
  • city rank-size
  • moving window

ASJC Scopus subject areas

  • Geography, Planning and Development
  • General Earth and Planetary Sciences


Dive into the research topics of 'Rank-size distributions of Chinese cities: Macro and micro patterns'. Together they form a unique fingerprint.

Cite this