Detecting Spatial Clustering Using a Firm-Level Cluster Index
Tobias Scholl and
Thomas Brenner ()
Regional Studies, 2016, vol. 50, issue 6, 1054-1068
Abstract:
S choll T. and B renner T. Detecting spatial clustering using a firm-level Cluster Index. Regional Studies . A new statistical method is presented that detects industrial clusters at a firm level. The proposed method does not divide space into subunits, whereby it is not affected by the modifiable areal unit problem (MAUP). Hence, it is the first method to identify clusters without predetermined borders. The metric differs in both its calculation and its interpretation from existing distance-based metrics and shows three central properties that enable its meaningful use for cluster analysis. The method fulfils all five criteria for a test of localization proposed by Duranton and Overman in 2005.
Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (11)
Downloads: (external link)
http://hdl.handle.net/10.1080/00343404.2014.958456 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:regstd:v:50:y:2016:i:6:p:1054-1068
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CRES20
DOI: 10.1080/00343404.2014.958456
Access Statistics for this article
Regional Studies is currently edited by Ivan Turok
More articles in Regional Studies from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().