From: Quantifying human mobility resilience to extreme events using geo-located social media data
Type | Disaster Name | Disaster Location | No. of Tweets | No. of Users |
---|---|---|---|---|
Hurricane | Sandy (all tweets) | USA | 52,493,130 | 13,745,659 |
Sandy (geo-tagged tweets) | USA | 24,149,780 | 5,981,012 | |
Earthquake | Bohol (Bohol) | Bohol, Philippines | 114,606 | 7942 |
Iquique (Iquique) | Iquique, Chile | 15,297 | 1470 | |
Napa (Napa) | Napa, USA | 38,019 | 1850 | |
Typhoon | Wipha (Tokyo) | Tokyo, Japan | 849,173 | 73,451 |
Halong (Okinawa) | Okinawa, Japan | 166,325 | 5,124 | |
Kalmaegi (Calasiao) | Calasiao, Philippines | 21,698 | 1,063 | |
Rammasun (Manila) | Manila, Philippines | 408,760 | 27,753 | |
Winter storm | Xaver (Norfolk) | Norfolk, Britain | 115,018 | 8498 |
Xaver (Hamburg) | Hamburg, Germany | 15,054 | 2745 | |
Storm (Atlanta) | Atlanta, USA | 157,179 | 15,783 | |
Thunder storm | Storm (Phoenix) | Phoenix, USA | 579,735 | 23,132 |
Storm (Detroit) | Detroit, USA | 765,353 | 15,949 | |
Storm (Baltimore) | Baltimore, USA | 328,881 | 14,582 | |
Wildfire | New South Wales (1) | New South Wales, Australia (1) | 64,371 | 9246 |
New South Wales (2) | New South Wales, Australia (2) | 34,157 | 4147 |