Does the scaling relationship between population sizes of cities with urban metrics like economic output and infrastructure (transversal scaling) mirror the evolution of individual cities in time (longitudinal scaling)? The answer to this question has important policy implications, but the lack of suitable data has so far hindered rigorous empirical tests. In this paper, we advance the debate by looking at the evolution of two urban variables, GDP and water network length, for over 5500 cities in Brazil. We find that longitudinal scaling exponents are city-specific. However, they are distributed around an average value that approaches the transversal scaling exponent provided that the data is decomposed to eliminate external factors, and only for cities with a sufficiently high growth rate. We also introduce a mathematical framework that connects the microscopic level to global behaviour, finding good agreement between theoretical predictions and empirical evidence in all analyzed cases. Our results add complexity to the idea that the longitudinal dynamics is a micro-scaling version of the transversal dynamics of the entire urban system. The longitudinal analysis can reveal differences in scaling behavior related to population size and nature of urban variables. Our approach also makes room for the role of external factors such as public policies and development, and opens up new possibilities in the research of the effects of scaling and contextual factors.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7236989 | PMC |
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Healthcare (Basel)
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Department of Psychological and Brain Sciences, Boston University, 900 Commonwealth Ave., 2nd Floor, Boston, MA 02215, USA.
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Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
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J Family Med Prim Care
December 2024
Department of Medical Parasitology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
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Sex Med
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Department of Urology, Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan, Chengdu 610072, China.
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Health Data Sci
January 2024
Department of Global Health, School of Public Health, Peking University, Beijing, China.
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