城市与地区间的交叉排序：人口与收入
Cross Ranking of Cities and Regions: Population vs. Income
AI总结：
本文通过官方数据的排名分析，探讨社区内部经济结构与人口分布的关系，发现人口与财富的排名相关性存在双降幂律特征，并揭示了区域间的差异。
摘要：
本文通过官方数据的排名分析，探讨社区内部经济结构与人口分布的关系，发现人口与财富的排名相关性存在双降幂律特征，并揭示了区域间的差异。
英文摘要：
This paper explores the relationship between the inner economical structure of communities and their population distribution through a rank-rank analysis of official data, along statistical physics ideas within two techniques. The data is taken on Italian cities. The analysis is performed both at a global (national) and at a more local (regional) level in order to distinguish "macro" and "micro" aspects. First, the rank-size rule is found not to be a standard power law, as in many other studies, but a doubly decreasing power law. Next, the Kendall and the Spearman rank correlation coefficients which measure pair concordance and the correlation between fluctuations in two rankings, respectively, - as a correlation function does in thermodynamics, are calculated for finding rank correlation (if any) between demography and wealth. Results show non only global disparities for the whole (country) set, but also (regional) disparities, when comparing the number of cities in regions, the number of inhabitants in cities and that in regions, as well as when comparing the aggregated tax income of the cities and that of regions. Different outliers are pointed out and justified. Interestingly, two classes of cities in the country and two classes of regions in the country are found. "Common sense" social, political, and economic considerations sustain the findings. More importantly, the methods show that they allow to distinguish communities, very clearly, when specific criteria are numerically sound. A specific modeling for the findings is presented, i.e. for the doubly decreasing power law and the two phase system, based on statistics theory, e.g., urn filling. The model ideas can be expected to hold when similar rank relationship features are observed in fields. It is emphasized that the analysis makes more sense than one through a Pearson value-value correlation analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
