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		<doi>10.1007/s41109-019-0211-7</doi>
		<label>lattes: 0654596992211296 7 LimaSantosCaSeCoMaQuMo:2019:HoUrMo</label>
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		<citationkey>SantosCaSeCoMaQuMo:2019:HoUrMo</citationkey>
		<author>Santos, Leonardo Bacelar Lima,</author>
		<author>Carvalho, Luiz Max,</author>
		<author>Seron, Wilson,</author>
		<author>Coelho, Flávio C.,</author>
		<author>Macau, Elbert Einstein Nehrer,</author>
		<author>Quiles, Marcos G.,</author>
		<author>Monteiro, Antônio Miguel Vieira,</author>
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		<group>DIDPI-CGOBT-INPE-MCTIC-GOV-BR</group>
		<affiliation>Centro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN)</affiliation>
		<affiliation>Fundação Oswaldo Cruz (FIOCRUZ)</affiliation>
		<affiliation>Universidade Federal de São Paulo (UNIFESP)</affiliation>
		<affiliation>Fundação Getúlio Vargas (FGV)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Universidade Federal de São Paulo (UNIFESP)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>20santoslbl@gmail.com</electronicmailaddress>
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		<electronicmailaddress>elbert.macau@inpe.br</electronicmailaddress>
		<electronicmailaddress></electronicmailaddress>
		<electronicmailaddress>miguel.monteiro@inpe.br</electronicmailaddress>
		<title>How do urban mobility (geo)graph's topological properties fill a map?</title>
		<journal>Applied Network Science</journal>
		<year>2019</year>
		<volume>4</volume>
		<number>1</number>
		<keywords>Complex networks, Geographical information systems (GIS), (geo)graphs, Traffic-topology correlation.</keywords>
		<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.</abstract>
		<pages>1-10</pages>
		<language>en</language>
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