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Analysis of spatial patterns of technological innovation capability based on patent data in Jiangsu province, China

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Listed:
  • Yuxuan Ma

    (Nanjing Normal University)

  • Lei Wang

    (Nanjing Normal University)

  • Di Hu

    (Nanjing Normal University
    Nanjing Normal University
    Jiangsu Centre of Collaborative Innovation in Geographical Information Resource Development and Application)

  • Yaoqing Ge

    (Nanjing Normal University)

  • Junzhu Zuo

    (Nanjing Normal University)

  • Tian Lan

    (University College London)

Abstract

Innovation is the main driver of regional economic development. Exploring the spatial patterns of regional innovation can elucidate the regional differences in innovation development. Nearly all the researches on spatial patterns of innovation take the administrative divisions of provinces, cities and counties as spatial units, and portray the spatial patterns of regional innovation from the macroscopic overall, but lack the spatial patterns within the regions. This paper employed patents of Jiangsu province, China in 2019 as a sample, divided the spatial units based on geographic coordinates of patent data, calculated the technological innovation capability index of within the spatial units, overcoming the drawbacks of the research method that fails to reveal the internal pattern of cities by using provinces, cities and counties as the research scale, and analysed the spatial patterns of technological innovation capability in Jiangsu province by using spatial autocorrelation analysis and standard deviation ellipse. The results show that: (1) In terms of distribution, the spatial pattern of technological innovation capability in Jiangsu province is obviously “one core and one belt” in southern Jiangsu, with Nanjing as the core and Suzhou, Wuxi and Changzhou as a southeast-northwest belt of high innovation capacity, while multi-point scattered in northern Jiangsu. (2) From the perspective of aggregation mode, each city has the largest number of H-H agglomeration units, relatively presenting a zonal distribution in the Suzhou-Wuxi-Changzhou area and Nanjing, and less distribution in other places. (3) Industrially speaking, the development of technological innovation capability of the secondary industry in many cities is better and centralized, while secondary and tertiary industries move hand in hand around the city centre in Nanjing, and the tertiary industry dominates comparably in Suzhou and Wuxi.

Suggested Citation

  • Yuxuan Ma & Lei Wang & Di Hu & Yaoqing Ge & Junzhu Zuo & Tian Lan, 2023. "Analysis of spatial patterns of technological innovation capability based on patent data in Jiangsu province, China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02428-w
    DOI: 10.1057/s41599-023-02428-w
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    References listed on IDEAS

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    1. Rasmus Lema & Michiko Iizuka & Rainer Walz, 2015. "Introduction to low-carbon innovation and development: insights and future challenges for research," Innovation and Development, Taylor & Francis Journals, vol. 5(2), pages 173-187, October.
    2. Jianshi Wang & Chengxin Wang & Shangkun Yu & Mengcheng Li & Yu Cheng, 2022. "Coupling Coordination and Spatiotemporal Evolution between Carbon Emissions, Industrial Structure, and Regional Innovation of Counties in Shandong Province," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    3. Rosina Moreno & Raffaele Paci & Stefano Usai, 2005. "Spatial Spillovers and Innovation Activity in European Regions," Environment and Planning A, , vol. 37(10), pages 1793-1812, October.
    4. Riccardo Crescenzi & Andrés Rodriguez-Pose & Michael Storper, 2007. "The territorial dynamics of innovation: a Europe-United States comparative analysis," Journal of Economic Geography, Oxford University Press, vol. 7(6), pages 673-709, November.
    5. Eduardo Goncalves & Eduardo Almeida, 2009. "Innovation and Spatial Knowledge Spillovers: Evidence from Brazilian Patent Data," Regional Studies, Taylor & Francis Journals, vol. 43(4), pages 513-528.
    6. Jörn Block & Christian Fisch & Kenta Ikeuchi & Masatoshi Kato, 2022. "Trademarks as an indicator of regional innovation: evidence from Japanese prefectures," Regional Studies, Taylor & Francis Journals, vol. 56(2), pages 190-209, February.
    7. Roberta Capello & Camilla Lenzi, 2015. "Knowledge, Innovation and Productivity Gains across European Regions," Regional Studies, Taylor & Francis Journals, vol. 49(11), pages 1788-1804, November.
    8. Zoltan J. Acs & David B. Audretsch, 1989. "Patents as a Measure of Innovative Activity," Kyklos, Wiley Blackwell, vol. 42(2), pages 171-180, August.
    9. Jens Horbach & Christian Rammer, 2022. "Skills shortage and innovation," Industry and Innovation, Taylor & Francis Journals, vol. 29(6), pages 734-759, July.
    10. Roberta Capello & Camilla Lenzi, 2019. "Regional innovation evolution and economic performance," Regional Studies, Taylor & Francis Journals, vol. 53(9), pages 1240-1251, September.
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