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@Article{SantosCaSeCoMaQuMo:2019:HoUrMo,
               author = "Santos, Leonardo Bacelar Lima and Carvalho, Luiz Max and Seron, 
                         Wilson and Coelho, Fl{\'a}vio C. and Macau, Elbert Einstein 
                         Nehrer and Quiles, Marcos G. and Monteiro, Ant{\^o}nio Miguel 
                         Vieira",
          affiliation = "{Centro Nacional de Monitoramento e Alertas de Desastres Naturais 
                         (CEMADEN)} and {Funda{\c{c}}{\~a}o Oswaldo Cruz (FIOCRUZ)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and 
                         {Funda{\c{c}}{\~a}o Get{\'u}lio Vargas (FGV)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de S{\~a}o Paulo (UNIFESP)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "How do urban mobility (geo)graph's topological properties fill a 
                         map?",
              journal = "Applied Network Science",
                 year = "2019",
               volume = "4",
               number = "1",
                pages = "1--10",
             keywords = "Complex networks, Geographical information systems (GIS), 
                         (geo)graphs, Traffic-topology correlation.",
             abstract = "Urban mobility data are important to areas ranging from traffic 
                         engineering to the analysis of outbreaks and disasters. In this 
                         paper, we study mobility data from a major Brazilian city from a 
                         geographical viewpoint using a Complex Network approach. The case 
                         study is based on intra-urban mobility data from the Metropolitan 
                         area of Rio de Janeiro (Brazil), presenting more than 480 spatial 
                         network nodes. While for the mobility flow data a log-normal 
                         distribution outperformed the power law, we also found moderate 
                         evidence for scale-free and small word effects in the flow 
                         networks degree distribution. We employ a novel open-source GIS 
                         tool to display (geo)graphs topological properties in maps and 
                         observe a strong traffic-topology association and also a fine 
                         adjustment for hubs location for different flow threshold 
                         networks. In the central commercial area for lower thresholds and 
                         in high population residential areas for higher thresholds. This 
                         set of results, including statistical, topological and 
                         geographical analysis may represent an important tool for 
                         policymakers and stakeholders in the urban planning area, 
                         especially by the identification of zones with few but strong 
                         links in a real data-driven mobility networ.",
                  doi = "10.1007/s41109-019-0211-7",
                  url = "http://dx.doi.org/10.1007/s41109-019-0211-7",
                label = "lattes: 0654596992211296 7 LimaSantosCaSeCoMaQuMo:2019:HoUrMo",
             language = "en",
           targetfile = "santos_how.pdf",
        urlaccessdate = "2019, Dec. 10"
}


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