Optimizing Distance-Based Methods for Big Data Analysis

Tobias Scholl and Thomas Brenner ()
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Tobias Scholl: House of Logistics and Mobility (HOLM), Frankfurt and Economic Geography and Location Research, Philipps-University, Marburg

No 2013-09, Working Papers on Innovation and Space from Philipps University Marburg, Department of Geography

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.

Keywords: Spatial concentration; Duranton-Overman index; big-data analysis; MAUP; distance-based measures (search for similar items in EconPapers)
JEL-codes: C40 M13 R12 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2013-10-06
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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