An algorithm to compute data diversity index in spatial networks

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Title: An algorithm to compute data diversity index in spatial networks
Authors: Agryzkov, Taras | Tortosa, Leandro | Vicent, Jose F.
Research Group/s: Análisis y Visualización de Datos en Redes (ANVIDA)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Diversity index | Spatial networks | Urban networks | Spatial statistics | Gini–Simpson index
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 15-Nov-2018
Publisher: Elsevier
Citation: Applied Mathematics and Computation. 2018, 337: 63-75. doi:10.1016/j.amc.2018.04.068
Abstract: 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.
Sponsor: Partially supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P.
ISSN: 0096-3003 (Print) | 1873-5649 (Online)
DOI: 10.1016/j.amc.2018.04.068
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Elsevier Inc.
Peer Review: si
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Appears in Collections:INV - ANVIDA - Artículos de Revistas

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