Table 1.
Ref. | Goal | Datasources | Granularity level | Location Setting | Validation method | COVID-19 related |
---|---|---|---|---|---|---|
[5] | Predict the spread of SARS-CoV-2 through the analysis of geolocated tweets | TWT | Inter-countries | Worldwide | none | yes |
[4] | Prediction of the spatiotemporal spread of infectious diseases using social media data | TWT | Inter-city | China | none | yes |
[6] | Early epidemic predictions | PTD | Intra-city | Fortaleza (Brazil) | OD and time series comparison | no |
[29] | Estimation of commuting trip distribution | TWT & census tracts | Intra-city | New York | none | no |
[1] | Detection of population’s spatio-temporal and demographic features | TWT | Intra-city | Chicago | Census and activity centers data | no |
[25] | Natural disaster detection | TWT | Inter-cities | N/A | none | no |
[30] | Spatial distribution of the worldwide population | TWT | Inter-countries | Worldwide | Statistical methods | no |
[20] | Human mobility study | TWT & travel surveys | Intra-city | New York | Statistical methods | no |
[19] | Human mobility study | TWT & MPL | Nation-wide | Australia | Statistical methods | no |
[17] | Human mobility study | TWT & MPL | Nation-wide | USA | Statistical methods | yes |
[15] | Human mobility study | TWT & demographics | Worldwide | - | Statistical methods | no |
[31] | Use of telephone antenna signals to establish human relationships between geographic areas | MLP | Inter-cities | Chile | Graph Clustering | no |
Our approach | Human mobility study | TWT & MPL | Nation-wide | Spain | OD and time series comparison | yes |