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Analyzing spatial mobility patterns with time‐varying graphical lasso: Application to COVID‐19 spread
Author(s) -
Degano Iván L.,
Lotito Pablo A.
Publication year - 2021
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12799
Subject(s) - lasso (programming language) , graphical model , georeference , covid-19 , set (abstract data type) , computer science , data set , data mining , extension (predicate logic) , enhanced data rates for gsm evolution , geography , artificial intelligence , medicine , disease , pathology , physical geography , world wide web , infectious disease (medical specialty) , programming language
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.