@Article{FerreiraVeCoCaQuZhMa:2020:SpDaAn,
               author = "Ferreira, Leonardo N. and Vega-Oliveros, Didier A. and 
                         Cotacallapa, Frank Mosh{\'e} and Cardoso, Manoel Ferreira and 
                         Quile, Marcos G. and Zhao, Liang and Macau, Elbert Einstein 
                         Nehrer",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Indiana 
                         University} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and 
                         {Universidade de S{\~a}o Paulo (USP)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Spatiotemporal data analysis with chronological networks",
              journal = "Nature Communications",
                 year = "2020",
               volume = "11",
               number = "1",
                pages = "e4036",
                month = "Dec.",
             abstract = "The number of spatiotemporal data sets has increased rapidly in 
                         the last years, which demands robust and fast methods to extract 
                         information from this kind of data. Here, we propose a 
                         network-based model, called Chronnet, for spatiotemporal data 
                         analysis. The network construction process consists of dividing a 
                         geometric space into grid cells represented by nodes connected 
                         chronologically. Strong links in the network represent consecutive 
                         recurrent events between cells. The chronnet construction process 
                         is fast, making the model suitable to process large data sets. 
                         Using artificial and real data sets, we show how chronnets can 
                         capture data properties beyond simple statistics, like frequent 
                         patterns, spatial changes, outliers, and spatiotemporal clusters. 
                         Therefore, we conclude that chronnets represent a robust tool for 
                         the analysis of spatiotemporal data sets.",
                  doi = "10.1038/s41467-020-17634-2",
                  url = "http://dx.doi.org/10.1038/s41467-020-17634-2",
                 issn = "2041-1723",
             language = "en",
           targetfile = "ferreira_spatiotemporal.pdf",
        urlaccessdate = "27 abr. 2024"
}