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Detecting Spatial Clustering Using a Firm-Level Cluster Index

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  • Tobias Scholl
  • Thomas Brenner

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.

Suggested Citation

  • Tobias Scholl & Thomas Brenner, 2016. "Detecting Spatial Clustering Using a Firm-Level Cluster Index," Regional Studies, Taylor & Francis Journals, vol. 50(6), pages 1054-1068, June.
    • Handle: RePEc:taf:regstd:v:50:y:2016:i:6:p:1054-1068
    DOI: 10.1080/00343404.2014.958456
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    Cited by:

    1. Zidong Yu & Jinyan Zu & Yang Xu & Yimin Chen & Xintao Liu, 2022. "Spatial and functional organizations of industrial agglomerations in China’s Greater Bay Area," Environment and Planning B, , vol. 49(7), pages 1995-2010, September.
    2. Matias Nehuen Iglesias, 2021. "The Overlooked Insights from Correlation Structures in Economic Geography," Papers in Evolutionary Economic Geography (PEEG) 2105, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Jan 2021.
    3. Nils Grashof, 2020. "Sinking or swimming in the cluster labour pool? A firm-specific analysis of the effect of specialized labour," Jena Economics Research Papers 2020-006, Friedrich-Schiller-University Jena.
    4. Grashof, Nils, 2020. "Putting the watering can away Towards a targeted (problem-oriented) cluster policy framework," Papers in Innovation Studies 2020/4, Lund University, CIRCLE - Centre for Innovation Research.
    5. Ismaëlh Cissé & Jean Dubé & Cédric Brunelle, 2020. "New business location: how local characteristics influence individual location decision?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(1), pages 185-214, February.
    6. Thomas Brenner, 2017. "Identification of Clusters - An Actor based Approach," Working Papers on Innovation and Space 2017-02, Philipps University Marburg, Department of Geography.
    7. Giulio Cainelli & Roberto Ganau & Yuting Jiang, 2020. "Detecting space–time agglomeration processes over the Great Recession using firm-level micro-geographic data," Journal of Geographical Systems, Springer, vol. 22(4), pages 419-445, October.
    8. Pauline Debanes, 2018. "Modes of Insertion into Global Value Chains as a Source of Firms' Heterogeneity?," Working Papers halshs-01849224, HAL.
    9. Nils Grashof & Alexander Kopka, 2023. "Widening or closing the gap? The relationship between artificial intelligence, firm-level productivity and regional clusters," Bremen Papers on Economics & Innovation 2304, University of Bremen, Faculty of Business Studies and Economics.
    10. Nils Grashof, 2020. "Spill over or Spill out? - A multilevel analysis of the cluster and firm performance relationship," Papers in Evolutionary Economic Geography (PEEG) 2013, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Mar 2020.
    11. Wang, Yahui & Li, Xiubin & Lu, Dan & Yan, Jianzhong, 2020. "Evaluating the impact of land fragmentation on the cost of agricultural operation in the southwest mountainous areas of China," Land Use Policy, Elsevier, vol. 99(C).

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