Graph Node Scoring for the Analysis and Visualisation of Mobility Networks and Data
Urban mobility and geographical systems benefit significantly from a graph-based topology. To identify the network’s crucial zones in terms of connectivity or movement across the network, we implemented several centrality metrics on a particular type of spatial network, i.e., a Region Adjacency grap...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Urban Science |
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| Online Access: | https://www.mdpi.com/2413-8851/8/4/155 |
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| author | Rafael Alejandro Martínez Márquez Giuseppe Patanè |
| author_facet | Rafael Alejandro Martínez Márquez Giuseppe Patanè |
| author_sort | Rafael Alejandro Martínez Márquez |
| collection | DOAJ |
| description | Urban mobility and geographical systems benefit significantly from a graph-based topology. To identify the network’s crucial zones in terms of connectivity or movement across the network, we implemented several centrality metrics on a particular type of spatial network, i.e., a Region Adjacency graph, using three geographical regions of different sizes to exhibit the scalability of conventional metrics. To boost the topological analysis of a network with geographical data, we discuss the eigendata centrality and implement it for the largest of our Region Adjacency graphs using available geographical information. For flow prediction data-driven models, we discuss the Deep Gravity model and utilise either its geographical input data or predicted flow values to implement an additional node score through the Perron vector of the transition probability matrix. The results show that the topological analysis of a spatial network can be significantly enhanced by including regional and mobility data for graphs of different scales, connectivity, and orientation properties. |
| format | Article |
| id | doaj-art-1f38e855e842499da0ef080c6f8b0e22 |
| institution | Kabale University |
| issn | 2413-8851 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Urban Science |
| spelling | doaj-art-1f38e855e842499da0ef080c6f8b0e222024-12-27T14:57:21ZengMDPI AGUrban Science2413-88512024-09-018415510.3390/urbansci8040155Graph Node Scoring for the Analysis and Visualisation of Mobility Networks and DataRafael Alejandro Martínez Márquez0Giuseppe Patanè1Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes”, Consiglio Nazionale delle Ricerche, 16149 Genova, ItalyIstituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes”, Consiglio Nazionale delle Ricerche, 16149 Genova, ItalyUrban mobility and geographical systems benefit significantly from a graph-based topology. To identify the network’s crucial zones in terms of connectivity or movement across the network, we implemented several centrality metrics on a particular type of spatial network, i.e., a Region Adjacency graph, using three geographical regions of different sizes to exhibit the scalability of conventional metrics. To boost the topological analysis of a network with geographical data, we discuss the eigendata centrality and implement it for the largest of our Region Adjacency graphs using available geographical information. For flow prediction data-driven models, we discuss the Deep Gravity model and utilise either its geographical input data or predicted flow values to implement an additional node score through the Perron vector of the transition probability matrix. The results show that the topological analysis of a spatial network can be significantly enhanced by including regional and mobility data for graphs of different scales, connectivity, and orientation properties.https://www.mdpi.com/2413-8851/8/4/155spatial networksnode centrality metricsLaplacian matricesPerron vectormobility flowsgeovisualisation |
| spellingShingle | Rafael Alejandro Martínez Márquez Giuseppe Patanè Graph Node Scoring for the Analysis and Visualisation of Mobility Networks and Data Urban Science spatial networks node centrality metrics Laplacian matrices Perron vector mobility flows geovisualisation |
| title | Graph Node Scoring for the Analysis and Visualisation of Mobility Networks and Data |
| title_full | Graph Node Scoring for the Analysis and Visualisation of Mobility Networks and Data |
| title_fullStr | Graph Node Scoring for the Analysis and Visualisation of Mobility Networks and Data |
| title_full_unstemmed | Graph Node Scoring for the Analysis and Visualisation of Mobility Networks and Data |
| title_short | Graph Node Scoring for the Analysis and Visualisation of Mobility Networks and Data |
| title_sort | graph node scoring for the analysis and visualisation of mobility networks and data |
| topic | spatial networks node centrality metrics Laplacian matrices Perron vector mobility flows geovisualisation |
| url | https://www.mdpi.com/2413-8851/8/4/155 |
| work_keys_str_mv | AT rafaelalejandromartinezmarquez graphnodescoringfortheanalysisandvisualisationofmobilitynetworksanddata AT giuseppepatane graphnodescoringfortheanalysisandvisualisationofmobilitynetworksanddata |