TY  - JOUR
TI  - Identifying geographic clusters: A network analytic approach
N1  - Available online May 14, 2015
KW  - Innovation clusters
KW  -  Network analysis
KW  -  Bio-pharmaceutical industry
AV  - public
EP  - 1762
ID  - eprints2680
SP  - 1749
UR  - http://www.sciencedirect.com/science/article/pii/S004873331500013X
SN  - 0048-7333
VL  - 44
PB  - Elsevier
Y1  - 2015/11//
IS  - 9
N2  - 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.
A1  - Catini, Roberto
A1  - Karamshuk, Dmytro
A1  - Penner, Orion
A1  - Riccaboni, Massimo
JF  - Research Policy
ER  -