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A New Technique Based on Voronoi Tessellation to Assess the Space-Dependence of Categorical Variables

ETS Ingenieros de Telecomunicación, Information Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid, 28040 Madrid, Spain
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Entropy 2019, 21(8), 774; https://doi.org/10.3390/e21080774
Received: 2 May 2019 / Revised: 2 August 2019 / Accepted: 2 August 2019 / Published: 8 August 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
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Abstract

Based on a sample of geolocated elements, each of them labeled with a (not necessarily ordered) categorical feature, several indexes for assessing the relationship between the geolocation variables (latitude and longitude) and the categorical variable are evaluated. Among these indexes, a new one based on a Voronoi tessellation presents several advantages since it does not require a variable transformation or a previous discretization; in addition, simulations show that this index is considerably robust when compared with the previously known ones. Finally, the use of the presented indexes is also illustrated by analyzing the geolocation of communities in some communication networks derived from Call Detail Records. View Full-Text
Keywords: spatial correlation; independence indices; Voronoi tessellation; entropy spatial correlation; independence indices; Voronoi tessellation; entropy
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Zufiria, P.J.; Hernández-Medina, M.Á. A New Technique Based on Voronoi Tessellation to Assess the Space-Dependence of Categorical Variables. Entropy 2019, 21, 774.

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