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Tools to Characterize Point Patterns: dbmss for R

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  • Marcon, Eric
  • Traissac, Stéphane
  • Puech, Florence
  • Lang, Gabriel

Abstract

The dbmss package for R provides an easy-to-use toolbox to characterize the spatial structure of point patterns. Our contribution presents the state of the art of distance-based methods employed in economic geography and which are also used in ecology. Topographic functions such as Ripley's K, absolute functions such as Duranton and Overman's Kd and relative functions such as Marcon and Puech's M are implemented. Their confidence envelopes (including global ones) and tests against counterfactuals are included in the package.

Suggested Citation

  • Marcon, Eric & Traissac, Stéphane & Puech, Florence & Lang, Gabriel, 2015. "Tools to Characterize Point Patterns: dbmss for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(c03).
    • Handle: RePEc:jss:jstsof:v:067:c03
    DOI: http://hdl.handle.net/10.18637/jss.v067.c03
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    7. Glenn Ellison & Edward L. Glaeser & William R. Kerr, 2010. "What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns," American Economic Review, American Economic Association, vol. 100(3), pages 1195-1213, June.
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    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. 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.
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    Cited by:

    1. Eva Coll‐Martínez & Ana‐Isabel Moreno‐Monroy & Josep‐Maria Arauzo‐Carod, 2019. "Agglomeration of creative industries: An intra‐metropolitan analysis for Barcelona," Papers in Regional Science, Wiley Blackwell, vol. 98(1), pages 409-431, February.
    2. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani & Maria Michela Dickson, 2017. "Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(2-3), pages 326-346, July.
    3. Brad Humphreys & Josh Matti, 2018. "The Spatial Distribution of Urban Consumer Service Firms: Evidence from Yelp Reviews," Working Papers 18-03, Department of Economics, West Virginia University.
    4. Rudkin , Simon & He, Ming & Chen, Yang, 2020. "Attraction or Repulsion? Testing Coagglomeration of Innovation between Firm and University," ADB Economics Working Paper Series 608, Asian Development Bank.
    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. Xiaoxiang Zhang & Jing Yao & Katarzyna Sila-Nowicka & Chonghui Song, 2021. "Geographic concentration of industries in Jiangsu, China: a spatial point pattern analysis using micro-geographic data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(2), pages 439-461, April.
    8. Eric Marcon & Florence Puech, 2016. "A typology of distance-based measures of spatial concentration," Post-Print halshs-00679993, HAL.

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