eprintid: 2680
rev_number: 11
eprint_status: archive
userid: 69
dir: disk0/00/00/26/80
datestamp: 2015-05-18 15:45:57
lastmod: 2015-11-02 13:13:29
status_changed: 2015-05-18 15:45:57
type: article
metadata_visibility: show
creators_name: Catini, Roberto
creators_name: Karamshuk, Dmytro
creators_name: Penner, Orion
creators_name: Riccaboni, Massimo
creators_id: roberto.catini@imtlucca.it
creators_id: dmytro.karamshuk@imtlucca.it
creators_id: orion.penner@imtlucca.it
creators_id: massimo.riccaboni@imtlucca.it
title: Identifying geographic clusters: A network analytic approach
ispublished: pub
subjects: G1
subjects: QA75
divisions: EIC
full_text_status: public
keywords: Innovation clusters, Network analysis, Bio-pharmaceutical industry
note: Available online May 14, 2015
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.
date: 2015-11
date_type: published
publication: Research Policy
volume: 44
number: 9
publisher: Elsevier
pagerange: 1749-1762
id_number: 10.1016/j.respol.2015.01.011
refereed: TRUE
issn: 0048-7333
official_url: http://www.sciencedirect.com/science/article/pii/S004873331500013X
citation:   Catini, Roberto and Karamshuk, Dmytro and Penner, Orion and Riccaboni, Massimo  Identifying geographic clusters: A network analytic approach.  Research Policy, 44 (9).  pp. 1749-1762.  ISSN 0048-7333      (2015)  
document_url: http://eprints.imtlucca.it/2680/1/final-preprint.pdf