A centrality measure for urban networks based on the eigenvector centrality concept
| Autor: | Taras Agryzkov, Leandro Tortosa, Jose F. Vicent, Richard C. Wilson |
|---|---|
| Přispěvatelé: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial, Análisis y Visualización de Datos en Redes (ANVIDA) |
| Rok vydání: | 2017 |
| Předmět: |
Geography
Planning and Development 0211 other engineering and technologies 0507 social and economic geography Network science 02 engineering and technology Network theory Management Monitoring Policy and Law computer.software_genre Network graphs Betweenness centrality Architecture Katz centrality Economic geography Network centrality Nature and Landscape Conservation Street networks 05 social sciences Spatial analysis Ciencia de la Computación e Inteligencia Artificial 021107 urban & regional planning Degree distribution Random walk closeness centrality Network controllability Eigenvector centrality Urban Studies Geography Data mining Centrality 050703 geography computer |
| Zdroj: | RUA. Repositorio Institucional de la Universidad de Alicante Universidad de Alicante (UA) |
| ISSN: | 2399-8091 2399-8083 |
| Popis: | A massive amount of information as geo-referenced data is now emerging from the digitization of contemporary cities. Urban streets networks are characterized by a fairly uniform degree distribution and a low degree range. Therefore, the analysis of the graph constructed from the topology of the urban layout does not provide significant information when studying topology-based centrality. On the other hand, we have collected geo-located data about the use of various buildings and facilities within the city. This does provide a rich source of information about the importance of various areas. Despite this, we still need to consider the influence of topology, as this determines the interaction between different areas. In this paper, we propose a new model of centrality for urban networks based on the concept of Eigenvector Centrality for urban street networks which incorporates information from both topology and data residing on the nodes. So, the centrality proposed is able to measure the influence of two factors, the topology of the network and the geo-referenced data extracted from the network and associated to the nodes. We detail how to compute the centrality measure and provide the rational behind it. Some numerical examples with small networks are performed to analyse the characteristics of the model. Finally, a detailed example of a real urban street network is discussed, taking a real set of data obtained from a fieldwork, regarding the commercial activity developed in the city. |
| Databáze: | OpenAIRE |
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