Quantifying Human Mobility Perturbation and Resilience in Hurricane Sandy
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{"title"=>"Quantifying human mobility perturbation and resilience in hurricane sandy", "type"=>"journal", "authors"=>[{"first_name"=>"Qi", "last_name"=>"Wang", "scopus_author_id"=>"56978602400"}, {"first_name"=>"John E.", "last_name"=>"Taylor", "scopus_author_id"=>"55741147200"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84919372647", "sgr"=>"84919372647", "issn"=>"19326203", "arxiv"=>"1402.1987", "doi"=>"10.1371/journal.pone.0112608", "pmid"=>"25409009", "isbn"=>"1932-6203 (Electronic) 1932-6203 (Linking)", "pui"=>"600653313"}, "id"=>"6dc3c801-eb11-3fc5-adf6-cb22be80e239", "abstract"=>"Human mobility is influenced by environmental change and natural disasters. Researchers have used trip distance distribution, radius of gyration of movements, and individuals' visited locations to understand and capture human mobility patterns and trajectories. However, our knowledge of human movements during natural disasters is limited owing to both a lack of empirical data and the low precision of available data. Here, we studied human mobility using high-resolution movement data from individuals in New York City during and for several days after Hurricane Sandy in 2012. We found the human movements followed truncated power-law distributions during and after Hurricane Sandy, although the β value was noticeably larger during the first 24 hours after the storm struck. Also, we examined two parameters: the center of mass and the radius of gyration of each individual's movements. We found that their values during perturbation states and steady states are highly correlated, suggesting human mobility data obtained in steady states can possibly predict the perturbation state. Our results demonstrate that human movement trajectories experienced significant perturbations during hurricanes, but also exhibited high resilience. We expect the study will stimulate future research on the perturbation and inherent resilience of human mobility under the influence of hurricanes. For example, mobility patterns in coastal urban areas could be examined as hurricanes approach, gain or dissipate in strength, and as the path of the storm changes. Understanding nuances of human mobility under the influence of such disasters will enable more effective evacuation, emergency response planning and development of strategies and policies to reduce fatality, injury, and economic loss.", "link"=>"http://www.mendeley.com/research/quantifying-human-mobility-perturbation-resilience-hurricane-sandy", "reader_count"=>59, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>7, "Researcher"=>10, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>3, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>7, "Researcher"=>10, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>3, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Arts and Humanities"=>3, "Computer Science"=>7, "Earth and Planetary Sciences"=>4, "Economics, Econometrics and Finance"=>1, "Engineering"=>16, "Environmental Science"=>7, "Materials Science"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>3, "Physics and Astronomy"=>3, "Psychology"=>2, "Social Sciences"=>4}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Social Sciences"=>{"Social Sciences"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>7}, "Environmental Science"=>{"Environmental Science"=>7}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Engineering"=>{"Engineering"=>16}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>4}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Computer Science"=>{"Computer Science"=>7}}, "reader_count_by_country"=>{"Sri Lanka"=>1, "United States"=>2, "Japan"=>2, "United Kingdom"=>2, "Spain"=>2, "India"=>1}, "group_count"=>5}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1933365"], "description"=>"<p><b>A</b>, <b>C</b>, and <b>E</b>, locations visited by Twitter users. <b>B</b>, <b>D</b>, and <b>F</b>, movement trajectories of Twitter users. The insets in <b>A</b> and <b>B</b> show an enlarged map of the lower Manhattan area. Red areas indicate the evacuated zones enforced by New York City government, though some of the areas were still active with human activity/mobility in this 24-hour period. The green nodes indicate the locations where fatalities occurred.</p>", "links"=>[], "tags"=>["disaster", "emergency response planning", "trip distance distribution", "data", "perturbation", "Hurricane Sandy", "movement", "Quantifying Human Mobility Perturbation", "mobility patterns", "Hurricane Sandy Human mobility", "New York City"], "article_id"=>1324202, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Qi Wang", "John E. Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112608.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Geographical_distribution_of_visited_locations_and_movement_trajectories_over_24_hour_periods_/1324202", "title"=>"Geographical distribution of visited locations and movement trajectories over 24-hour periods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-19 21:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1933366"], "description"=>"<p><b>A</b>, Statistical distribution of displacements for each 24-hour period. Each line represents the change of numbers of trips within different ranges of displacements. <b>B</b>, Displacement distribution for each 24-hour period. Each line represents a probability density function <i>P</i>(<i>Δr</i>). All plotted distributions followed a truncated power-law distribution (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112608#pone.0112608.s003\" target=\"_blank\">Table S3</a>).</p>", "links"=>[], "tags"=>["disaster", "emergency response planning", "trip distance distribution", "data", "perturbation", "Hurricane Sandy", "movement", "Quantifying Human Mobility Perturbation", "mobility patterns", "Hurricane Sandy Human mobility", "New York City"], "article_id"=>1324203, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Qi Wang", "John E. Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112608.g002", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Human_mobility_perturbation_/1324203", "title"=>"Human mobility perturbation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-19 21:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1933367"], "description"=>"<p><b>A</b>, <i>Δd<sub>CM</sub></i> versus <i>r<sub>g</sub><sup>N</sup></i>. The correlation coefficient between the two parameters is 0.59 (<i>p</i><0.001). The red line is the fitted function of the correlation where (m). The green line in Fig. 3A indicates where <i>Δd<sub>CM</sub></i> equals <i>r<sub>g</sub><sup>N</sup></i>. <b>B</b>, <i>r<sub>g</sub><sup>P</sup></i> versus <i>r<sub>g</sub><sup>N</sup></i>. The notations are the same as in <b>A</b>. The correlation coefficient between these two parameters is 0.25 (<i>p</i><0.001) with fitted function (m).</p>", "links"=>[], "tags"=>["disaster", "emergency response planning", "trip distance distribution", "data", "perturbation", "Hurricane Sandy", "movement", "Quantifying Human Mobility Perturbation", "mobility patterns", "Hurricane Sandy Human mobility", "New York City"], "article_id"=>1324204, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Qi Wang", "John E. Taylor"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112608.g003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relation_between_perturbation_states_and_steady_states_/1324204", "title"=>"Relation between perturbation states and steady states.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-19 21:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1933368", "https://ndownloader.figshare.com/files/1933369", "https://ndownloader.figshare.com/files/1933370", "https://ndownloader.figshare.com/files/1933371", "https://ndownloader.figshare.com/files/1933372"], "description"=>"<div><p>Human mobility is influenced by environmental change and natural disasters. Researchers have used trip distance distribution, radius of gyration of movements, and individuals' visited locations to understand and capture human mobility patterns and trajectories. However, our knowledge of human movements during natural disasters is limited owing to both a lack of empirical data and the low precision of available data. Here, we studied human mobility using high-resolution movement data from individuals in New York City during and for several days after Hurricane Sandy in 2012. We found the human movements followed truncated power-law distributions during and after Hurricane Sandy, although the <i>β</i> value was noticeably larger during the first 24 hours after the storm struck. Also, we examined two parameters: the center of mass and the radius of gyration of each individual's movements. We found that their values during perturbation states and steady states are highly correlated, suggesting human mobility data obtained in steady states can possibly predict the perturbation state. Our results demonstrate that human movement trajectories experienced significant perturbations during hurricanes, but also exhibited high resilience. We expect the study will stimulate future research on the perturbation and inherent resilience of human mobility under the influence of hurricanes. For example, mobility patterns in coastal urban areas could be examined as hurricanes approach, gain or dissipate in strength, and as the path of the storm changes. Understanding nuances of human mobility under the influence of such disasters will enable more effective evacuation, emergency response planning and development of strategies and policies to reduce fatality, injury, and economic loss.</p></div>", "links"=>[], "tags"=>["disaster", "emergency response planning", "trip distance distribution", "data", "perturbation", "Hurricane Sandy", "movement", "Quantifying Human Mobility Perturbation", "mobility patterns", "Hurricane Sandy Human mobility", "New York City"], "article_id"=>1324205, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Qi Wang", "John E. Taylor"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0112608.s001", "https://dx.doi.org/10.1371/journal.pone.0112608.s002", "https://dx.doi.org/10.1371/journal.pone.0112608.s003", "https://dx.doi.org/10.1371/journal.pone.0112608.s004", "https://dx.doi.org/10.1371/journal.pone.0112608.s005"], "stats"=>{"downloads"=>31, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantifying_Human_Mobility_Perturbation_and_Resilience_in_Hurricane_Sandy_/1324205", "title"=>"Quantifying Human Mobility Perturbation and Resilience in Hurricane Sandy", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-11-19 21:11:57"}

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