Constructing Evacuation Evolution Patterns and Decisions Using Mobile Device Location Data: A Case Study of Hurricane Irma

• URL: http://arxiv.org/abs/2102.12600v1
• Date: Wed, 24 Feb 2021 23:24:10 GMT
• Title: Constructing Evacuation Evolution Patterns and Decisions Using Mobile Device Location Data: A Case Study of Hurricane Irma
• Authors: Aref Darzi, Vanessa Frias-Martinez, Sepehr Ghader, Hannah Younes, Lei Zhang
• Abstract summary: This paper utilized a large mobile phone Location-Based Services (LBS) data to construct the evacuation pattern during the landfall of Hurricane Irma. We studied users' evacuation decisions, departure and reentry date distribution, and destination choice. Our analysis revealed the importance of the individuals' mobility behavior in modeling the evacuation decision choice.
• Score: 5.902556437760098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding individuals' behavior during hurricane evacuation is of paramount importance for local, state, and government agencies hoping to be prepared for natural disasters. Complexities involved with human decision-making procedures and lack of data for such disasters are the main reasons that make hurricane evacuation studies challenging. In this paper, we utilized a large mobile phone Location-Based Services (LBS) data to construct the evacuation pattern during the landfall of Hurricane Irma. By employing our proposed framework on more than 11 billion mobile phone location sightings, we were able to capture the evacuation decision of 807,623 smartphone users who were living within the state of Florida. We studied users' evacuation decisions, departure and reentry date distribution, and destination choice. In addition to these decisions, we empirically examined the influence of evacuation order and low-lying residential areas on individuals' evacuation decisions. Our analysis revealed that 57.92% of people living in mandatory evacuation zones evacuated their residences while this ratio was 32.98% and 33.68% for people living in areas with no evacuation order and voluntary evacuation order, respectively. Moreover, our analysis revealed the importance of the individuals' mobility behavior in modeling the evacuation decision choice. Historical mobility behavior information such as number of trips taken by each individual and the spatial area covered by individuals' location trajectory estimated significant in our choice model and improve the overall accuracy of the model significantly.

Related papers

• Information Retrieval and Classification of Real-Time Multi-Source Hurricane Evacuation Notices [2.500155415916692]
  We developed an approach to timely detect and track the locally issued hurricane evacuation notices. The text data were collected mainly with a spatially targeted web scraping method. The framework may be applied to other types of disasters for rapid and targeted retrieval, classification, redistribution, and archiving of real-time government orders and notifications.
  arXiv  Detail & Related papers  (2024-01-07T16:35:30Z)
• Data-Driven Bayesian Network Models of Hurricane Evacuation Decision Making [2.749589513485177]
  We propose using Bayesian Networks (BNs) to model evacuation decisions during hurricanes. We collected questionnaire data from two significant hurricane events: Hurricane Harvey and Hurricane Irma. We examined and compared the learned structures of both hurricanes, revealing potential causal relationships among key predictors of evacuation.
  arXiv  Detail & Related papers  (2023-11-16T22:58:57Z)
• Dfferentiable Raycasting for Self-supervised Occupancy Forecasting [52.61762537741392]
  Motion planning for autonomous driving requires learning how the environment around an ego-vehicle evolves with time. In this paper, we use geometric occupancy as a natural alternative to view-dependent representations such as freespace. Our key insight is to use differentiable raycasting to "render" future occupancy predictions into future LiDAR sweep predictions.
  arXiv  Detail & Related papers  (2022-10-04T21:35:21Z)
• Location Intelligence Reveals the Extent, Timing, and Spatial Variation of Hurricane Preparedness [8.460587574173035]
  Peak visits to pharmacies often occurred in the early stage, whereas the peak of visits to gas stations happened closer to landfall. The study advances data-driven understanding of human protective actions and provides emergency response managers with novel insights to proactively monitor disaster preparedness.
  arXiv  Detail & Related papers  (2022-03-13T04:59:42Z)
• A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs [0.0]
  One of the most immediate needs of people affected by a disaster is finding shelter. This paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used.
  arXiv  Detail & Related papers  (2021-07-29T18:22:34Z)
• C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak [54.39837683016444]
  C-Watcher aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks.
  arXiv  Detail & Related papers  (2020-12-22T17:02:54Z)
• Physics-informed GANs for Coastal Flood Visualization [65.54626149826066]
  We create a deep learning pipeline that generates visual satellite images of current and future coastal flooding. By evaluating the imagery relative to physics-based flood maps, we find that our proposed framework outperforms baseline models in both physical-consistency and photorealism. While this work focused on the visualization of coastal floods, we envision the creation of a global visualization of how climate change will shape our earth.
  arXiv  Detail & Related papers  (2020-10-16T02:15:34Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)
• Social Media Information Sharing for Natural Disaster Response [0.0]
  Social media has become an essential channel for posting disaster-related information, which provide governments and relief agencies real-time data for better disaster management. This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes towards disaster response and public demands for targeted relief supplies during different types of disasters.
  arXiv  Detail & Related papers  (2020-05-08T21:11:39Z)
• Detecting Perceived Emotions in Hurricane Disasters [62.760131661847986]
  We introduce HurricaneEmo, an emotion dataset of 15,000 English tweets spanning three hurricanes: Harvey, Irma, and Maria. We present a comprehensive study of fine-grained emotions and propose classification tasks to discriminate between coarse-grained emotion groups.
  arXiv  Detail & Related papers  (2020-04-29T16:17:49Z)
• Give more data, awareness and control to individual citizens, and they will help COVID-19 containment [74.10257867142049]
  Contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach raises concerns about citizens' privacy and needlessly strong digital surveillance. We advocate a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores"
  arXiv  Detail & Related papers  (2020-04-10T20:30:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.