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A New Technique Based on Voronoi Tessellation to Assess the Space-Dependence of Categorical Variables.
Zufiria, Pedro J; Hernández-Medina, Miguel Á.
Afiliación
  • Zufiria PJ; ETS Ingenieros de Telecomunicación, Information Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid, 28040 Madrid, Spain.
  • Hernández-Medina MÁ; ETS Ingenieros de Telecomunicación, Information Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid, 28040 Madrid, Spain.
Entropy (Basel) ; 21(8)2019 Aug 08.
Article en En | MEDLINE | ID: mdl-33267486
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Entropy (Basel) Año: 2019 Tipo del documento: Article País de afiliación: España Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Entropy (Basel) Año: 2019 Tipo del documento: Article País de afiliación: España Pais de publicación: Suiza