IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Optimizing Distance-Based Methods for Big Data Analysis

  • Tobias Scholl

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

  • Thomas Brenner

    (Philipps-Universität Marburg)

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: ftp://137.248.191.199/RePEc/pum/wpaper/wp0913.pdf
File Function: Full text
Download Restriction: no

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

as
in new window

Length: 15 pages
Date of creation: 06 Oct 2013
Date of revision:
Handle: RePEc:pum:wpaper:2013-09
Contact details of provider: Postal: Deutschhausstrasse 10, 35032 Marburg
Phone: 064212824257
Fax: 064212828950
Web page: http://www.uni-marburg.de/fb19/
Email:


More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Reinhold Kosfeld & Hans-Friedrich Eckey & Jørgen Lauridsen, 2011. "Spatial point pattern analysis and industry concentration," The Annals of Regional Science, Springer, vol. 47(2), pages 311-328, October.
  2. Eric Marcon & Florence Puech, 2003. "Evaluating the Geographic Concentration of Industries Using Distance-Based Methods," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00372646, HAL.
  3. 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.
  4. Stefania Vitali & Mauro Napoletano & Giorgio Fagiolo, 2009. "Spatial Localization in Manufacturing: A Cross-Country Analysis," Sciences Po publications 2009/04, Sciences Po.
  5. Thomas Klier & Daniel P. McMillen, 2008. "Evolving Agglomeration In The U.S. Auto Supplier Industry," Journal of Regional Science, Wiley Blackwell, vol. 48(1), pages 245-267.
  6. 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.
  7. 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.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:pum:wpaper:2013-09. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christoph Mengs)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.