Scaling of urban economic outputs: insights both from urban population size and population mobility
Abstract
Urban scaling laws assume that the performance of a city largely relies on its urban population size. However, two cities with the same population size may have vastly different economic outputs, which reveals that factors apart from urban size (a measurement of intra-urban interactions) determine their economic outputs. Economic production is essentially the product of social interactions. Urban population size and interurban interactions reflected by population mobility were both considered to evaluate the scaling of urban economic outputs in this paper. We quantified the scaling relationship between urban economic outputs and interurban interactions, and compared it with the paradigm derived from urban size. Results showed that urban economic outputs scale with urban interactions across cities and present the same super-linear scaling regimes but with a greater scaling exponent. A deeper looking showed that interurban interactions definitely bring a more obvious super-linearity than population size but the scaling relationship between urban size and economic outputs is more robust. Urban population size has a greater impact on Gross Domestic Product (GDP), secondary and tertiary industries output products, total retail sales of consumer goods and total wage with 60– 65% relative contribution. For exports, interurban interactions and urban population size are almost equally important. We also proved that interactions between cities are significantly positively correlated with urban extra growth. These findings provide convictive evidence that in addition to population size, interurban interaction is also crucial for exploring the scaling of urban growth. Our results are enlightening to the study of mechanisms and evolutions of urban scaling that interurban interaction besides urban population size should both be a vital consideration to urban economic outputs.
Keywords
Urban scaling
Population mobility
Urban economic outputs
Social interactions
Agglomeration effects
1. Introduction
Cities are concentrations not only of population but rather of socio-economic interactions (Jacobs, 1969). As one of the cornerstones to explore the new science of cities (Rybski, Arcaute, amp; Batty, 2018), urban scaling shows how urban attributes vary with size and the relationship between them (Batty, 2013b; West, 2017). Numerous urban indicators referring to material resource or measure of social activity, Y(t), scale with urban size N(t) across cities, obeying the power law form Y(t) = Y0N(t)beta;, where Y0 is a normalized constant and beta; is the scaling exponent (L. M. Bettencourt, Lobo, Helbing, Kuhnert, amp; West, 2007). This paradigm emphasizes the basic function of urban population size in the growth of socio-economic activities and infrastructure (Joseacute; Lobo, Bettencourt, Strumsky, amp; West, 2013). It also expresses that urban economic outputs brought by urban population size is nonlinear, which also determines the agglomeration effects or economies of scale characteristics of urban indicators in the process of urban development (L. M. Bettencourt, Lobo, Strumsky, amp; West, 2010; Florida, 2005). Socio-economic related properties of a city grow faster than population, called super-linear scaling regimes, characterized by a scaling exponent greater than one, while infrastructure presents sub-linear scaling regimes because of economies of scale (L. M. Bettencourt et al., 2007).
Recent theoretical models have been proposed to explain the positive agglomeration effects shown by super-linear scaling regimes generated by human social networks embedded in space (Arbesman, Kleinberg, amp; Strogatz, 2009; L. M. A. Bettencourt, 2013; L. M. A. Bettencourt et al., 2020; Jose Lobo, Bettencourt, Smith, amp; Ortman, 2019; Pan, Ghoshal, Krumme, Cebrian, amp; Pentland, 2013;
关键词:城市规模化;人口流动;城市经济产出;社交互动;集聚效应
我们通过复杂的网络分析方法探索城市人口流动的网络特征。选取度中心性、接近中心性和中介中心性,分别反映网络中节点的直接通信、可达服务和控制能力,进行中心性分析。
度中心性应该反映直接连接到该点的节点数。中心度越大,城市越中心化。度中心性可以通过以下方式测量:
同样,加权度中心度C W是基于C D,使用交互强度作为边缘权重,来衡量城市中心度:
接近中心性衡量节点之间的距离,可以通过从一个节点到其他节点的最短路径之和的倒数来表示。城市的接近中心度越大,越难被其他节点城市控制。节点城市的接近中心度C c ( c i ) 定义为:
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