Working Papers on Innovation and Space
نویسندگان
چکیده
Distance-based methods for measuring spatial concentration such as the DurantonOverman 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 & Brenner (2012) whose mathematical concept allows an even faster computation for large datasets than the improved algorithm does.
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