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Detecting Spatial Clustering Using a Firm-Level Cluster Index

Scholl, Tobias

Scholl T. and Brenner 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.

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Detecting Spatial Clustering Using a Firm-Level Cluster Index

Scholl, Tobias

Scholl T. and Brenner 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.

Detecting Spatial Clustering Using a Firm-Level Cluster Index

Scholl, Tobias

Scholl T. and Brenner 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.

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Title:

Detecting Spatial Clustering Using a Firm-Level Cluster Index

Contributors:

Scholl, Tobias (author) / Brenner, Thomas

Published in:

Regional studies ; 50

Publication date:

2016

ISSN:

DOI:

https://doi.org/10.1080/00343404.2014.958456

Type of media:

Article (Journal)

Type of material:

Print

Language:

English

Keywords:

Regionalwissenschaft , Regionalwirtschaft , Zeitschrift , Regionalplanung , Raumordnung , Geografie , Regionalökonomik , Urbanistik , Regionalforschung , Welt , Theorie

Classification:

BKL: 74.71 Regionalplanung / 83.64 Regionalwirtschaft / 74.12 Stadtgeographie, Siedlungsgeographie
Local classification TIB: 275/6700

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