Hurricane Evacuation Analysis with Large-scale Mobile Device Location Data during Hurricane Ian
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917729821261824 |
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| author | Liu, Luyu Zhang, Xiaojian Jiang, Shangkun Zhao, Xilei |
| author_facet | Liu, Luyu Zhang, Xiaojian Jiang, Shangkun Zhao, Xilei |
| contents | Hurricane Ian is the deadliest and costliest hurricane in Florida's history, with 2.5 million people ordered to evacuate. As we witness increasingly severe hurricanes in the context of climate change, mobile device location data offers an unprecedented opportunity to study hurricane evacuation behaviors. With a terabyte-level GPS dataset, we introduce a holistic hurricane evacuation behavior algorithm with a case study of Ian: we infer evacuees' departure time and categorize them into different behavioral groups, including self, voluntary, mandatory, shadow and in-zone evacuees. Results show the landfall area (Fort Myers, Lee County) had lower out-of-zone but higher overall evacuation rate, while the predicted landfall area (Tampa, Hillsborough County) had the opposite, suggesting the effects of delayed evacuation order. Out-of-zone evacuation rates would increase from shore to inland. Spatiotemporal analysis identified three evacuation waves: during formation, before landfall, and after landfall. These insights are valuable for enhancing future disaster planning and management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15249 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hurricane Evacuation Analysis with Large-scale Mobile Device Location Data during Hurricane Ian Liu, Luyu Zhang, Xiaojian Jiang, Shangkun Zhao, Xilei Computational Engineering, Finance, and Science Emerging Technologies Physics and Society Hurricane Ian is the deadliest and costliest hurricane in Florida's history, with 2.5 million people ordered to evacuate. As we witness increasingly severe hurricanes in the context of climate change, mobile device location data offers an unprecedented opportunity to study hurricane evacuation behaviors. With a terabyte-level GPS dataset, we introduce a holistic hurricane evacuation behavior algorithm with a case study of Ian: we infer evacuees' departure time and categorize them into different behavioral groups, including self, voluntary, mandatory, shadow and in-zone evacuees. Results show the landfall area (Fort Myers, Lee County) had lower out-of-zone but higher overall evacuation rate, while the predicted landfall area (Tampa, Hillsborough County) had the opposite, suggesting the effects of delayed evacuation order. Out-of-zone evacuation rates would increase from shore to inland. Spatiotemporal analysis identified three evacuation waves: during formation, before landfall, and after landfall. These insights are valuable for enhancing future disaster planning and management. |
| title | Hurricane Evacuation Analysis with Large-scale Mobile Device Location Data during Hurricane Ian |
| topic | Computational Engineering, Finance, and Science Emerging Technologies Physics and Society |
| url | https://arxiv.org/abs/2407.15249 |