 Identifying geographic clusters: A network analytic approachRoberto Catini (), Dmytro Karamshuk, Orion Penner and Massimo Riccaboni () Research Policy, 2015, vol. 44, issue 9, 1749-1762 Abstract: In recent years there has been a growing interest in the role of networks and clusters in the global economy. Despite being a popular research topic in economics, sociology and urban studies, geographical clustering of human activity has often been studied by means of predetermined geographical units, such as administrative divisions and metropolitan areas. This approach is intrinsically time invariant and it does not allow one to differentiate between different activities. Our goal in this paper is to present a new methodology for identifying clusters, that can be applied to different empirical settings. We use a graph approach based on k-shell decomposition to analyze world biomedical research clusters based on PubMed scientific publications. We identify research institutions and locate their activities in geographical clusters. Leading areas of scientific production and their top performing research institutions are consistently identified at different geographic scales. Keywords: Innovation clusters; Network analysis; Bio-pharmaceutical industry (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:respol:v:44:y:2015:i:9:p:1749-1762 DOI: 10.1016/j.respol.2015.01.011 Access Statistics for this article Research Policy is currently edited by M. Bell, B. Martin, W.E. Steinmueller, A. Arora, M. Callon, M. Kenney, S. Kuhlmann, Keun Lee and F. Murray More articles in Research Policy from Elsevier |
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