Analyzing spatial mobility patterns with time‐varying graphical lasso: Application to COVID‐19 spread
| Autor: | Iván L. Degano, Pablo A. Lotito |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Transactions in Gis |
| ISSN: | 1467-9671 1361-1682 |
| Popis: | This work applies the time‐varying graphical lasso (TVGL) method, an extension of the traditional graphical lasso approach, to address learning time‐varying graphs from spatiotemporal measurements. Given georeferenced data, the TVGL method can estimate a time‐varying network where an edge represents a partial correlation between two nodes. To achieve this, we use a COVID‐19 data set from the Argentine province of Chaco. As an application, we use the estimated network to study the impact of COVID‐19 confinement measures and evaluate whether the measures produced the expected result. [ABSTRACT FROM AUTHOR] Copyright of Transactions in GIS is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Databáze: | OpenAIRE |
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