EconPapers    
Economics at your fingertips  
 

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 ().

 
Page updated 2025-03-20
Handle: RePEc:taf:regstd:v:50:y:2016:i:6:p:1054-1068