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Measuring and testing spatial mass concentration with micro-geographic data

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  • Thomas-Agnan, Christine
  • Bonneu, Florent
Abstract
We address the question of measuring and testing industrial spatial concentration based on micro-geographic data with distance based methods. We discuss the basic requirements for such measures and we propose four additional requirements. We also discuss the null assumptions classically used for testing aggregation of a particular sector and propose an alternative point of view. Our general index measure involves a cumulative and a non-cumulative version. This allows us to propose an alternative version of the Duranton Overman index with a proper baseline as well as a cumulative version of this same index. We illustrate the approach with some simulated data.

Suggested Citation

  • Thomas-Agnan, Christine & Bonneu, Florent, 2014. "Measuring and testing spatial mass concentration with micro-geographic data," TSE Working Papers 14-474, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:27959
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    References listed on IDEAS

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    9. A. J. Baddeley & J. Møller & R. Waagepetersen, 2000. "Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 54(3), pages 329-350, November.
    10. Giuseppe Espa & Diego Giuliani & Giuseppe Arbia, 2010. "Weighting Ripley�s K-function to account for the firm dimension in the analysis of spatial concentration," Department of Economics Working Papers 1012, Department of Economics, University of Trento, Italia.
    11. Eric Marcon & Florence Puech, 2010. "Measures of the geographic concentration of industries: improving distance-based methods," Journal of Economic Geography, Oxford University Press, vol. 10(5), pages 745-762, September.
    12. Arbia, G. & Espa, G. & Giuliani, D. & Mazzitelli, A., 2012. "Clusters of firms in an inhomogeneous space: The high-tech industries in Milan," Economic Modelling, Elsevier, vol. 29(1), pages 3-11.
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    Cited by:

    1. A. Tidu & S. Usai & Frederick Guy, 2021. "Agglomeration in manufacturing and services: an experimental application of a distance-based measure to Sardinia," Working Paper CRENoS 202110, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    2. Daniel A. Griffith & Yongwan Chun & Jan Hauke, 2022. "A Moran eigenvector spatial filtering specification of entropy measures," Papers in Regional Science, Wiley Blackwell, vol. 101(1), pages 259-279, February.
    3. Rawaa Laajimi & Julie Le Gallo & Saloua Benammou, 2020. "What Geographical Concentration of Industries in the Tunisian Sahel? Empirical Evidence Using Distance‐Based Measures," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 111(5), pages 738-757, December.
    4. Franz-Josef Bade & Eckhardt Bode & Eleonora Cutrini, 2015. "Spatial fragmentation of industries by functions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(1), pages 215-250, January.
    5. 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.
    6. Gabriel Lang & Eric Marcon & Florence Puech, 2020. "Distance-based measures of spatial concentration: introducing a relative density function," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 243-265, April.
    7. Eric Marcon & Florence Puech, 2016. "A typology of distance-based measures of spatial concentration," Post-Print halshs-00679993, HAL.
    8. Gabriel Lang & Eric Marcon & Florence Puech, 2020. "Distance-based measures of spatial concentration: Introducing a relative density function," Post-Print hal-01082178, HAL.
    9. S. Usai & Frederick Guy & A. Tidu, 2022. "Measuring spatial dispersion: an experimental test on the M-index," Working Paper CRENoS 202206, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

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    Keywords

    Spatial concentration; marked point processes; agglomeration; spatial clusters;
    All these keywords.

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