Using human mobility data to detect evacuation patterns in hurricane Ian
Hurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuati...
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Taylor & Francis Group
2024-10-01
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| Series: | Annals of GIS |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475683.2024.2341703 |
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| author | Xiang Li Yi Qiang Guido Cervone |
| author_facet | Xiang Li Yi Qiang Guido Cervone |
| author_sort | Xiang Li |
| collection | DOAJ |
| description | Hurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuation orders in high-risk zones to reduce casualties and injuries. However, there is limited data available to monitor the actual evacuation patterns and compliance with the evacuation orders at a large geographic scale. This study utilizes human mobility data (i.e. SafeGraph Weekly Pattern) to analyse the spatial patterns of evacuation during Hurricane Ian in 2022. The objectives of the study include three key aspects: 1) proposing an analytical workflow that utilizes human mobility data to detect mobility patterns in disasters and other emergency events; 2) identifying significant evacuation patterns, and 3) revealing the spatial variations in the compliance with evacuation orders in the affected areas. Using data science and spatial analysis techniques, this study detected notable changes in population movements, both within Florida and nationwide, which are potentially linked to the hurricane-induced population evacuation. The distance decay pattern of population flows from Florida demonstrates a propensity for individuals to relocate to nearby areas during the hurricane. Furthermore, the increase in population outflows from the impacted areas suggests the effectiveness of mandatory evacuation orders. A more pronounced increase in outflows from designated mandatory evacuation areas points to the public awareness of the evacuation zone designation. This study provides large-scale, fine-resolution analysis of evacuation behaviours in natural disasters which cannot be easily detected in traditional data sources. The analytical workflows provide actionable tools for government agencies and policymakers to evaluate the effectiveness of evacuation orders and improve evacuation plans in future disasters. |
| format | Article |
| id | doaj-art-1e4a16d5e8eb4ded8fddb6a2f00f646a |
| institution | Kabale University |
| issn | 1947-5683 1947-5691 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Taylor & Francis Group |
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| series | Annals of GIS |
| spelling | doaj-art-1e4a16d5e8eb4ded8fddb6a2f00f646a2024-11-21T11:36:02ZengTaylor & Francis GroupAnnals of GIS1947-56831947-56912024-10-0130449351110.1080/19475683.2024.2341703Using human mobility data to detect evacuation patterns in hurricane IanXiang Li0Yi Qiang1Guido Cervone2School of Geosciences, University of South Florida, Tampa, FL, USASchool of Geosciences, University of South Florida, Tampa, FL, USAInstitute for Computational and Data Science, Pennsylvania State University, University Park, PA, USAHurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuation orders in high-risk zones to reduce casualties and injuries. However, there is limited data available to monitor the actual evacuation patterns and compliance with the evacuation orders at a large geographic scale. This study utilizes human mobility data (i.e. SafeGraph Weekly Pattern) to analyse the spatial patterns of evacuation during Hurricane Ian in 2022. The objectives of the study include three key aspects: 1) proposing an analytical workflow that utilizes human mobility data to detect mobility patterns in disasters and other emergency events; 2) identifying significant evacuation patterns, and 3) revealing the spatial variations in the compliance with evacuation orders in the affected areas. Using data science and spatial analysis techniques, this study detected notable changes in population movements, both within Florida and nationwide, which are potentially linked to the hurricane-induced population evacuation. The distance decay pattern of population flows from Florida demonstrates a propensity for individuals to relocate to nearby areas during the hurricane. Furthermore, the increase in population outflows from the impacted areas suggests the effectiveness of mandatory evacuation orders. A more pronounced increase in outflows from designated mandatory evacuation areas points to the public awareness of the evacuation zone designation. This study provides large-scale, fine-resolution analysis of evacuation behaviours in natural disasters which cannot be easily detected in traditional data sources. The analytical workflows provide actionable tools for government agencies and policymakers to evaluate the effectiveness of evacuation orders and improve evacuation plans in future disasters.https://www.tandfonline.com/doi/10.1080/19475683.2024.2341703Hurricane evacuationspatial analysispopulation flowhuman mobilitynatural hazards |
| spellingShingle | Xiang Li Yi Qiang Guido Cervone Using human mobility data to detect evacuation patterns in hurricane Ian Annals of GIS Hurricane evacuation spatial analysis population flow human mobility natural hazards |
| title | Using human mobility data to detect evacuation patterns in hurricane Ian |
| title_full | Using human mobility data to detect evacuation patterns in hurricane Ian |
| title_fullStr | Using human mobility data to detect evacuation patterns in hurricane Ian |
| title_full_unstemmed | Using human mobility data to detect evacuation patterns in hurricane Ian |
| title_short | Using human mobility data to detect evacuation patterns in hurricane Ian |
| title_sort | using human mobility data to detect evacuation patterns in hurricane ian |
| topic | Hurricane evacuation spatial analysis population flow human mobility natural hazards |
| url | https://www.tandfonline.com/doi/10.1080/19475683.2024.2341703 |
| work_keys_str_mv | AT xiangli usinghumanmobilitydatatodetectevacuationpatternsinhurricaneian AT yiqiang usinghumanmobilitydatatodetectevacuationpatternsinhurricaneian AT guidocervone usinghumanmobilitydatatodetectevacuationpatternsinhurricaneian |