Abstract
In this paper a methodology for identifying and delineating spatial technology clusters based on patenting concentration is developed. The methodology involves the automated geocoding of patent inventor addresses, the application of a home bias correction factor and a sensitivity analysis to determine the optimal parameters of the kernel density estimation interpolation distance and the minimum concentration threshold to identify clusters. The methodology’s performance is compared to a number of other cluster identification methods and it is validated across 18 individual sectors, including mature broad-based high-technology sectors and emerging niche sustainable energy technology sectors. The results suggest that the performance of the methodology exceed that of alternative cluster identification methods, although there is some variation in performance between different sectors. This demonstrates that the methodology provides researchers, practitioners and policy makers with a useful tool to gain insight into the spatial distribution of sectoral innovation activity at a global scale and sub-national regional level and to monitor changes over time, thereby supplementing more readily available global statistical data which is available at the national level.
Highlights
The innovation literature attaches significant importance to the sub-national regional scale, as well as global connections and competition between clusters (Fujita et al 2001; Gertler and Wolfe 2006; Porter 2000; Simmie 2004)
This paper describes a new ‘organic’ (Alcácer and Zhao 2016) cluster identification methodology that uses heat maps to identify ‘hot spots’ of innovation activity which are detected as cluster once they exceed a particular threshold
This paper demonstrates that using heat maps is an effective way of identifying technology clusters and that the methodology’s performance exceeds that of alternative approaches
Summary
The innovation literature attaches significant importance to the sub-national regional scale, as well as global connections and competition between clusters (Fujita et al 2001; Gertler and Wolfe 2006; Porter 2000; Simmie 2004). Patent data offers an opportunity to overcome the limitations of statistical data because patent data is global in scale and patents often contain geographical information such as an inventor address, which allows for the identification of a city or other sub-national spatial unit (Alcácer and Zhao 2016; Bergquist et al 2017).
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