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  Spatiotemporal data analysis with chronological networks

Ferreira, L. N., Vega-Oliveros, D. A., Cotacallapa, M., Cardoso, M. F., Quiles, M. G., Zhao, L., Macau, E. E. N. (2020): Spatiotemporal data analysis with chronological networks. - Nature Communications, 11, 4036.
https://doi.org/10.1038/s41467-020-17634-2

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 Creators:
Ferreira, Leonardo N.1, Author              
Vega-Oliveros, Didier A.2, Author
Cotacallapa, Moshé2, Author
Cardoso, Manoel F.2, Author
Quiles, Marcos G.2, Author
Zhao, Liang2, Author
Macau, Elbert E. N.2, Author
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 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. Extracting central information from ever-growing data generated in our lives calls for new data mining methods. Ferreira et al. show a simple model, called chronnets, that can capture frequent patterns, spatial changes, outliers, and spatiotemporal clusters.

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 Dates: 2020-08-122020-08-12
 Publication Status: Finally published
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 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41467-020-17634-2
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
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Title: Nature Communications
Source Genre: Journal, SCI, Scopus, p3, oa
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Pages: - Volume / Issue: 11 Sequence Number: 4036 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals354
Publisher: Nature