%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2020/09.02.14.54
%2 sid.inpe.br/mtc-m21c/2020/09.02.14.54.54
%@doi 10.1038/s41467-020-17634-2
%@issn 2041-1723
%T Spatiotemporal data analysis with chronological networks
%D 2020
%8 Dec.
%9 journal article
%A Ferreira, Leonardo N.,
%A Vega-Oliveros, Didier A.,
%A Cotacallapa, Frank Moshé,
%A Cardoso, Manoel Ferreira,
%A Quile, Marcos G.,
%A Zhao, Liang,
%A Macau, Elbert Einstein Nehrer,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Indiana University
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidade Federal de São Paulo (UNIFESP)
%@affiliation Universidade de São Paulo (USP)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress ferreira@leonardonascimento.com
%@electronicmailaddress
%@electronicmailaddress frank.moshe@inpe.br
%@electronicmailaddress manoel.cardoso@inpe.br
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress elbert.macau@inpe.br
%B Nature Communications
%V 11
%N 1
%P e4036
%X 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.
%@language en
%3 ferreira_spatiotemporal.pdf