While great emphasis has been placed on the role of social interactions as driver of innovation growth, very few empirical studies have explicitly investigated the impact of social network structures on the innovation performance of cities. Past research has mostly explored scaling laws of socio-economic outputs of cities as determined by, for example, the single predictor of population. Here, by drawing on a publicly available dataset of the startup ecosystem, we build the first Workforce Mobility Network among US metropolitan areas. We found that node centrality computed on this network accounts for most of the variability observed in cities' innovation performance and significantly outperforms other predictors such as population size or density, suggesting that policies and initiatives aiming at sustaining innovation processes might benefit from fostering professional networks alongside other economic or systemic incentives. As opposed to previous approaches powered by census data, our model can be updated in real-time upon open databases, opening up new opportunities both for researchers in a variety of disciplines to study urban economies in new ways, and for practitioners to design tools for monitoring such economies in real-time.
University of Michigan（密歇根大学）；University of Toulouse（图卢兹大学）；Utrecht University（乌得勒支大学）；Massachusetts Institute of Technology（麻省理工学院）；Chinese Academy of Sciences（中国科学院）
Complex Connections Lab, Network Science Institute, Northeastern University London（复杂连接实验室，网络科学研究所，伦敦东北大学）；Faculty of Business, Northeastern University London（商学院，伦敦东北大学）；MTA-ELTE Agglomeration, Networks, and Innovation Momentum Research Group, ELTE Centre for Economic and Regional Studies（MTA-ELTE集聚、网络与创新动力研究组，布达佩斯罗兰大学经济与区域研究中心）；ANETI Lab, Corvinus Institute for Advanced Studies, Corvinus University of Budapest（ANETI实验室，布达佩斯考文纽斯大学高级研究院，布达佩斯考文纽斯大学）；Institute for Data Analytics and Information Systems, Corvinus University of Budapest（数据分析与信息系统研究所，布达佩斯考文纽斯大学）；ISI Foundation（ISI基金会）
了解人口和移徙模式是一个巨大的挑战。数以百万计的个人决定，由经济，政治，人口，理性和/或情感的原因，构成了人口动态的高度复杂性。近年来，在定量地理解这种复杂性方面已经取得了重大进展，因为涉及城市增长的那些[Bettencourt LMA，Lobo J，Helbing D，Kuehnert C，West GB（2007）Growth，. Innovation，Scaling，and the Pace of Life in Cities，Proc Natl Acad Sci USA 104（17）7301-7306]，但许多基本问题仍然难以理解。在这里，我们提出了令人信服的经验证据，高度的规律性，在城市蔓延的时间和空间的相关性，解开模式的惯性在城市的增长和它们之间的相互作用。通过使用世界上现存最详尽的人口数据基础之一--西班牙政府研究所INE的数据，记录涵盖111年和（2011年）4500万人，分布在8000多个人口核心中--我们表明城市增长的惯性具有15年的特征时间，它与其他城市的增长之间的相互作用具有70公里的特征距离。距离被证明是纠缠两个城市的主要因素（占总相关性的60%）。我们提出了一个人口流动的数学模型，i）再现了所有这些假设，ii）可以用来预测城市的人口演变。因此，我们当前社会理论的力量得到了加强。
