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Optimizing Distance-Based Methods for Big Data Analysis

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Author Info

  • Tobias Scholl

    (House of Logistics and Mobility (HOLM), Frankfurt and Economic Geography and Location Research, Philipps-University, Marburg)

  • Thomas Brenner

    (Philipps-Universität Marburg)

Abstract

Distance-based methods for measuring spatial concentration such as the Duranton-Overman index undergo an increasing popularity in the spatial econometrics community. However, a limiting factor for their usage is their computational complexity since both their memory requirements and running-time are in O(n2). In this paper, we present an algorithm with constant memory requirements and an improved running time, enabling the Duranton-Overman index and related distance-based methods to run big data analysis. Furthermore, we discuss the index by Scholl and Brenner (2012) whose mathematical concept allows an even faster computation for large datasets than the improved algorithm does.

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File URL: ftp://137.248.191.199/RePEc/pum/wpaper/wp0913.pdf
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Bibliographic Info

Paper provided by Philipps University Marburg, Department of Geography in its series Working Papers on Innovation and Space with number 2013-09.

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Length: 15 pages
Date of creation: 06 Oct 2013
Date of revision:
Handle: RePEc:pum:wpaper:2013-09

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Related research

Keywords: Spatial concentration; Duranton-Overman index; big-data analysis; MAUP; distance-based measures;

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  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. William Kerr & Edward Glaeser & Glenn Ellison, 2007. "What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns," Working Papers 07-13, Center for Economic Studies, U.S. Census Bureau.
  3. 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.
  4. Stefania Vitali & Mauro Napoletano & Giorgio Fagiolo, 2009. "Spatial Localization in Manufacturing: A Cross-Country Analysis," Papers in Evolutionary Economic Geography (PEEG) 0906, Utrecht University, Section of Economic Geography, revised Jun 2009.
  5. Reinhold Kosfeld & Hans-Friedrich Eckey & Jørgen Lauridsen, 2009. "Spatial Point Pattern Analysis and Industry Concentration," MAGKS Papers on Economics 200916, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  6. Thomas H. Klier & Daniel McMillen, 2006. "Evolving agglomeration in the U.S. auto supplier industry," Working Paper Series WP-06-20, Federal Reserve Bank of Chicago.
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