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Spatial point pattern analysis and industry concentration

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  • Reinhold Kosfeld
  • Hans-Friedrich Eckey
  • Jørgen Lauridsen

Abstract

Traditional measures of spatial industry concentration are restricted to given areal units. They do not make allowance for the fact that concentration may be differently pronounced at various geographical levels. Methods of spatial point pattern analysis allow to measure industry concentration at a continuum of spatial scales. While common distancebased methods are well applicable for sub-national study areas, they become inefficient in measuring concentration at various levels within industrial countries. This particularly applies in testing for conditional concentration where overall manufacturing is used as a reference population. Using Ripley’s K function approach to second-order analysis, we propose a subsample similarity test as a feasible testing approach for establishing conditional clustering or dispersion at different spatial scales. For measuring the extent of clustering and dispersion, we introduce a concentration index of the style of Besag’s (1977) L function. By contrast to Besag’s L function, the new index can be employed to measure deviations of observed from general spatial point patterns. The K function approach is illustratively applied to measuring and testing industry concentration in Germany.
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  • Reinhold Kosfeld & Hans-Friedrich Eckey & Jørgen Lauridsen, 2011. "Spatial point pattern analysis and industry concentration," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 47(2), pages 311-328, October.
  • Handle: RePEc:spr:anresc:v:47:y:2011:i:2:p:311-328
    DOI: 10.1007/s00168-010-0385-5
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    Cited by:

    1. Tobias Scholl & Thomas Brenner, 2013. "Detecting Spatial Clustering Using a Firm-Level Index," Working Papers on Innovation and Space 2012-02, Philipps University Marburg, Department of Geography.
    2. Tobias Scholl & Thomas Brenner & Martin Wendel, 2016. "Evolving localization patterns of company foundationsEvidence from the German MST-industry," Journal of Evolutionary Economics, Springer, vol. 26(5), pages 1067-1087, December.
    3. Reinhold Kosfeld, 2012. "Identifying Clusters within R&D Intensive Industries Using Local Spatial Methods," ERSA conference papers ersa12p232, European Regional Science Association.
    4. Reinhold Kosfeld & Mirko Titze, 2014. "Benchmark Value Added Chains and Regional Clusters in German R&D Intensive Industries," ERSA conference papers ersa14p1396, European Regional Science Association.
    5. Gibbons, Steve & Overman, Henry G. & Patacchini, Eleonora, 2015. "Spatial Methods," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 115-168, Elsevier.
    6. Ying Ge & Yingxia Pu & Mengdi Sun, 2021. "Alternative measure of border effects across regions: Ripley's K‐function method," Papers in Regional Science, Wiley Blackwell, vol. 100(1), pages 287-302, February.
    7. HAEDO, Christian & MOUCHART, Michel, 2012. "A stochastic independence approach for different measures of concentration and specialization," LIDAM Discussion Papers CORE 2012025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Jean Dubé & Cédric Brunelle, 2014. "Dots to dots: a general methodology to build local indicators using spatial micro-data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 245-272, August.
    9. Tobias Scholl & Thomas Brenner, 2013. "Optimizing Distance-Based Methods for Big Data Analysis," Working Papers on Innovation and Space 2013-09, Philipps University Marburg, Department of Geography.
    10. Marcon, Eric & Puech, Florence, 2017. "A typology of distance-based measures of spatial concentration," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 56-67.
    11. Hans-Friedrich Eckey & Reinhold Kosfeld & Alexander Werner, 2012. "Bivariate K functions as instruments to analyze inter-industrial concentration processes," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 32(2), pages 133-157, September.
    12. Baixu Zhou & Xinyue Qi & Xinru Hou & Zhili Chen & Jinzhuo Wu, 2023. "Identification and Spatial Correlation of Imported Timber Landing Processing Industrial Clusters in Heilongjiang Province of China," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    13. 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.
    14. Tobias Scholl & Thomas Brenner, 2015. "Optimizing distance-based methods for large data sets," Journal of Geographical Systems, Springer, vol. 17(4), pages 333-351, October.
    15. Udo Broll & Antonio Roldán-Ponce & Jack Wahl, 2013. "Regional investment under uncertain costs of location," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 51(3), pages 645-657, December.
    16. Maddah, Lina & Arauzo Carod, Josep Maria & López, Fernando A., 2021. "Clusters of Cultural and Creative Industries: Empirical Evidence for Catalonia," Working Papers 2072/534911, Universitat Rovira i Virgili, Department of Economics.
    17. Eric Marcon & Florence Puech, 2012. "A typology of distance-based measures of spatial concentration," Working Papers halshs-00679993, HAL.

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    More about this item

    Keywords

    C46; L60; L70; R12;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • L70 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - General
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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