Quantifying human mobility resilience to extreme events using geo-located social media data
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 su...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2020
|
| Online Access: | https://hdl.handle.net/1721.1/126443 |
| _version_ | 1826199264073613312 |
|---|---|
| author | Roy, Kamol Chandra Cebrian, Manuel Hasan, Samiul |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Roy, Kamol Chandra Cebrian, Manuel Hasan, Samiul |
| author_sort | Roy, Kamol Chandra |
| collection | MIT |
| description | 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 | 2024-09-23T11:17:18Z |
| format | Article |
| id | mit-1721.1/126443 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:17:18Z |
| publishDate | 2020 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1264432022-09-27T18:28:23Z Quantifying human mobility resilience to extreme events using geo-located social media data Roy, Kamol Chandra Cebrian, Manuel Hasan, Samiul Massachusetts Institute of Technology. Media Laboratory 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. 2020-07-30T01:40:35Z 2020-07-30T01:40:35Z 2019-05 2018-06 2020-06-26T13:27:57Z Article http://purl.org/eprint/type/JournalArticle 2193-1127 https://hdl.handle.net/1721.1/126443 Roy, Kamol Chandra et al. "Quantifying human mobility resilience to extreme events using geo-located social media data." EPJ Data Science 8, 1 (May 2019): 18 © 2019 Springer Nature en http://dx.doi.org/10.1140/epjds/s13688-019-0196-6 EPJ Data Science Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer Science and Business Media LLC Springer Berlin Heidelberg |
| spellingShingle | Roy, Kamol Chandra Cebrian, Manuel Hasan, Samiul Quantifying human mobility resilience to extreme events using geo-located social media data |
| 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 |
| url | https://hdl.handle.net/1721.1/126443 |
| work_keys_str_mv | AT roykamolchandra quantifyinghumanmobilityresiliencetoextremeeventsusinggeolocatedsocialmediadata AT cebrianmanuel quantifyinghumanmobilityresiliencetoextremeeventsusinggeolocatedsocialmediadata AT hasansamiul quantifyinghumanmobilityresiliencetoextremeeventsusinggeolocatedsocialmediadata |