Spatiotemporal data analysis with chronological networks

Leonardo N. Ferreira (), Didier A. Vega-Oliveros, Moshé Cotacallapa, Manoel F. Cardoso, Marcos G. Quiles, Liang Zhao and Elbert E. N. Macau
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Leonardo N. Ferreira: Associated Laboratory for Computing and Applied Mathematics
Didier A. Vega-Oliveros: University of Campinas
Moshé Cotacallapa: Associated Laboratory for Computing and Applied Mathematics
Manoel F. Cardoso: Center for Earth System Science
Marcos G. Quiles: Institute of Science and Technology
Liang Zhao: University of São Paulo
Elbert E. N. Macau: Associated Laboratory for Computing and Applied Mathematics

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-17634-2

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