Quantifying human mobility resilience to extreme events using geo-located social media data
Abstract Mobility is one of the fundamental requirements of human life with significant societal impacts including productivity, economy, social wellbeing, adaptation to a changing climate, and so on. Although human movements follow specific patterns during normal periods, there are limited studies...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-05-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1140/epjds/s13688-019-0196-6 |
| _version_ | 1818975221897494528 |
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| author | Kamol Chandra Roy Manuel Cebrian Samiul Hasan |
| author_facet | Kamol Chandra Roy Manuel Cebrian Samiul Hasan |
| author_sort | Kamol Chandra Roy |
| collection | DOAJ |
| description | Abstract Mobility is one of the fundamental requirements of human life with significant societal impacts including productivity, economy, social wellbeing, adaptation to a changing climate, and so on. Although human movements follow specific patterns during normal periods, there are limited studies on how such patterns change due to extreme events. To quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and transient loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socio-economic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation’s overall disaster resilience strategies. |
| first_indexed | 2024-12-20T15:52:31Z |
| format | Article |
| id | doaj.art-34179eea3c9e439d8b7aa2836d802297 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-20T15:52:31Z |
| publishDate | 2019-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-34179eea3c9e439d8b7aa2836d8022972022-12-21T19:34:37ZengSpringerOpenEPJ Data Science2193-11272019-05-018111510.1140/epjds/s13688-019-0196-6Quantifying human mobility resilience to extreme events using geo-located social media dataKamol Chandra Roy0Manuel Cebrian1Samiul Hasan2Department of Civil, Environmental, and Construction Engineering, University of Central FloridaMedia Laboratory, Massachusetts Institute of TechnologyDepartment of Civil, Environmental, and Construction Engineering, University of Central FloridaAbstract Mobility is one of the fundamental requirements of human life with significant societal impacts including productivity, economy, social wellbeing, adaptation to a changing climate, and so on. Although human movements follow specific patterns during normal periods, there are limited studies on how such patterns change due to extreme events. To quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and transient loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socio-economic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation’s overall disaster resilience strategies.http://link.springer.com/article/10.1140/epjds/s13688-019-0196-6Human mobilityResilienceGeo-location dataSocial media |
| spellingShingle | Kamol Chandra Roy Manuel Cebrian Samiul Hasan Quantifying human mobility resilience to extreme events using geo-located social media data EPJ Data Science Human mobility Resilience Geo-location data Social media |
| title | Quantifying human mobility resilience to extreme events using geo-located social media data |
| title_full | Quantifying human mobility resilience to extreme events using geo-located social media data |
| title_fullStr | Quantifying human mobility resilience to extreme events using geo-located social media data |
| title_full_unstemmed | Quantifying human mobility resilience to extreme events using geo-located social media data |
| title_short | Quantifying human mobility resilience to extreme events using geo-located social media data |
| title_sort | quantifying human mobility resilience to extreme events using geo located social media data |
| topic | Human mobility Resilience Geo-location data Social media |
| url | http://link.springer.com/article/10.1140/epjds/s13688-019-0196-6 |
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