Abstract
Cities function as complex systems, and the superlinear scaling laws of patents and incomes are well documented. Technology transfer is a bridge between knowledge and economic progress. Despite its importance, the urban scaling laws of technology transfer remain poorly understood, with limited exploration of their historical network evolution. Here we construct 166-year technology transfer networks for US cities. We find that technology transfers exhibit superlinear scaling with the scaling exponents ranked as intracity transfer > intercity transfer-in > intercity transfer-out. The evolution of the technology transfer network includes a nationwide space-filling process, a hierarchical structure among cities and functional polycentricity within cities. These dynamics have evolved in tandem, each tightly linked to the growth of scaling exponents. Our findings complement insights into the relationship between scaling laws and network evolution, validate the mechanisms of local and nonlocal knowledge interactions and provide new support for the evolutionary theory of complex urban systems.
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Fig. 1: Scaling law of technology transfer.
Fig. 2: Temporal evolution and space-filling process of US technology transfer, 1858–2023.
Fig. 3: Core–periphery structure of technology transfer.
Fig. 4: Technology transfer within top hubs.
Fig. 5: Temporal variation in polycentricity.
Data availability
Patent data from 1790–1975 were sourced from HistPat and are available online (https://histpat.shinyapps.io/HistPat). Patent data from 1976–2023 were sourced from PatentsView and can be accessed via a website (https://patentsview.org/download/data-download-tables). Population data from 1790–2020 were sourced from NHGIS and can be accessed via a website (https://www.nhgis.org/). The data that support the findings of this study are available at https://github.com/Qixiang-L/Technology-Transfer-US. All map boundary shapefiles were downloaded from the US Census Bureau website (https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html). ArcGIS Pro was used for map rendering and visualization.
Code availability
The code that supports the findings of this study is available at https://github.com/Qixiang-L/Technology-Transfer-US.
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Acknowledgements
This work was supported by the Major Program of the National Social Science Foundation of China under grant no. 23&ZD330 (Q.L., D.D. and Y.Y.).
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School of Geographic Sciences, East China Normal University, Shanghai, China
Qixiang Li, Debin Du & Yingjie Yu
Institute for Global Innovation Studies, East China Normal University, Shanghai, China
Qixiang Li, Debin Du & Yingjie Yu
Center for World Geography and Geostrategic Studies, East China Normal University, Shanghai, China
Qixiang Li, Debin Du & Yingjie Yu
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Qixiang Li
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Contributions
Q.L. and D.D. designed the research; Q.L. performed the research; Q.L., D.D. and Y.Y. analyzed data; Q.L. led software development; D.D. led funding acquisition; Q.L. led data curation; D.D. led resources and supervision; Q.L. and Y.Y. handled visualization; Q.L., D.D. and Y.Y. drafted the paper.
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Correspondence to Debin Du.
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1, ‘The association of scaling with network evolution’; 2, ‘The association of scaling with network evolution’; and 3, ‘Regression analysis’.
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Li, Q., Du, D. & Yu, Y. Scaling and network evolution of technology transfer in US cities. Nat Cities (2025). https://doi.org/10.1038/s44284-025-00209-x
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Received:06 July 2024
Accepted:29 January 2025
Published:07 March 2025
DOI:https://doi.org/10.1038/s44284-025-00209-x
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