Rank-size distribution or the rank-size rule (or law) describes the remarkable regularity in many phenomena including the distribution of city sizes around the world, sizes of businesses, particle sizes (such as sand), lengths of rivers, frequencies of word usage, wealth among individuals, etc. All are real-world observations that follow power laws such as those called Zipf's law, the Yule distribution, or the Pareto distribution. If one ranks the population size of cities in a given country or in the entire world and calculates the natural logarithm of the rank and of the city population, the resulting graph will show a remarkable log-linear pattern. This is the rank-size distribution. In the case of city populations, the resulting distribution in a country, region or the world will be characterized by a largest city, with other cities decreasing in size respective to it, initially at a rapid rate and then more slowly. This results in a few large cities, and a much larger number of cities orders of magnitude smaller. For example, a rank 3 city would have ⅓ the population of a country's largest city, a rank four city would have ¼ the population of the largest city, and so on. Why should simple rank be able to predict so easily such complex distributions? In short, why does the rank size rule “work?” One study has shown why this is so. The distributions mentioned above such as Zipf, Pareto, Yule, etc. , also called power laws, are all also related to the distribution known as the Fibonacci sequence and to that of the equiangular spiral. In the Fibonacci sequence, each term is approximately 1.618 times the preceding term. The same ratio is seen in the Lucas numbers consisting of these sequentially additive numbers 1, 3, 4, 7, 11, 18, 29, 47, 76, 123, 199,… When any log-linear factor is ranked, the ranks follow the Lucas numbers as above and each of the terms in the sequence can also be approximated by the successive values of powers of 1.618. For example, the third term in the sequence above, 4, is approximately 1.618 or 4.236 (which is approximately 4); the fourth term in the sequence, 7, is approximately 1.618 or 6.854 (which is approximately 7); the eight term in the series, 47, is approximately 1.618 or 46.979 (which is approximately 47). With higher and higher values, the figures converge. Thus it is shown that the rank size rule “works” because it is a “shadow” or coincidental measure of the true phenomenon. The true value of rank size is thus not as an accurate mathematical measure (since other power-law formulas are more accurate, especially at ranks lower than 10) but rather as a handy measure or “rule of thumb” to spot power laws. When presented with a ranking of data, is the third-ranked variable approximately ⅓ the value of the highest-ranked one? Or, conversely, is the highest-ranked variable approximately ten times the value of the tenth-ranked one? If so, the rank size rule
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