An algorithm to compute data diversity index in spatial networks
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http://hdl.handle.net/10045/76216
Título: | An algorithm to compute data diversity index in spatial networks |
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Autor/es: | Agryzkov, Taras | Tortosa, Leandro | Vicent, Jose F. |
Grupo/s de investigación o GITE: | Análisis y Visualización de Datos en Redes (ANVIDA) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | Diversity index | Spatial networks | Urban networks | Spatial statistics | Gini–Simpson index |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | 15-nov-2018 |
Editor: | Elsevier |
Cita bibliográfica: | Applied Mathematics and Computation. 2018, 337: 63-75. doi:10.1016/j.amc.2018.04.068 |
Resumen: | Diversity is an important measure that according to the context, can describe different concepts of general interest: competition, evolutionary process, immigration, emigration and production among others. It has been extensively studied in different areas, as ecology, political science, economy, sociology and others. The quality of spatial context of the city can be gauged through this measure. The spatial context with its corresponding dataset can be modelled using spatial networks. Consequently, this allows us to study the diversity of data present in this specific type of networks. In this paper we propose an algorithm to measure diversity in spatial networks based on the topology and the data associated to the network. In the experiments developed with networks of different sizes, it is observed that the proposed index is independent of the size of the network, but depends on its topology. |
Patrocinador/es: | Partially supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P. |
URI: | http://hdl.handle.net/10045/76216 |
ISSN: | 0096-3003 (Print) | 1873-5649 (Online) |
DOI: | 10.1016/j.amc.2018.04.068 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2018 Elsevier Inc. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.amc.2018.04.068 |
Aparece en las colecciones: | INV - ANVIDA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2018_Agryzkov_etal_ApplMathComp_final.pdf | Versión final (acceso restringido) | 3,25 MB | Adobe PDF | Abrir Solicitar una copia |
2018_Agryzkov_etal_ApplMathComp_preprint.pdf | Preprint (acceso abierto) | 7,63 MB | Adobe PDF | Abrir Vista previa |
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