Laws of Population Growth
Hernan D. Rozenfeld, Diego Rybski, Jose S. Andrade Jr., Michael Batty,, H. Eugene Stanley, and Hernan A. Makse

TL;DR
This paper introduces the City Clustering Algorithm (CCA) to define metropolitan areas based on population distribution, revealing deviations from Gibrat's law and uncovering spatial correlations affecting city growth dynamics.
Contribution
The paper presents the CCA method for defining cities beyond administrative boundaries and analyzes its impact on understanding city growth laws.
Findings
Mean growth rate deviates from Gibrat's law using CCA.
Standard deviation of growth rate follows a power-law decay.
Long-range spatial correlations influence city growth patterns.
Abstract
An important issue in the study of cities is defining a metropolitan area, as different definitions affect the statistical distribution of urban activity. A commonly employed method of defining a metropolitan area is the Metropolitan Statistical Areas (MSA), based on rules attempting to capture the notion of city as a functional economic region, and is constructed using experience. The MSA is time-consuming and is typically constructed only for a subset (few hundreds) of the most highly populated cities. Here, we introduce a new method to designate metropolitan areas, denoted the "City Clustering Algorithm" (CCA). The CCA is based on spatial distributions of the population at a fine geographic scale, defining a city beyond the scope of its administrative boundaries. We use the CCA to examine Gibrat's law of proportional growth, postulating that the mean and standard deviation of the…
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