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Predicting the impact of urban flooding using open data
This paper aims to explore whether there is a relationship between search patterns for flood risk information on the Web and how badly localities have been affected by flood events. We hypothesize that localities where people stay more actively informed about potential flooding experience less negat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4892441/ https://www.ncbi.nlm.nih.gov/pubmed/27293779 http://dx.doi.org/10.1098/rsos.160013 |
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author | Tkachenko, Nataliya Procter, Rob Jarvis, Stephen |
author_facet | Tkachenko, Nataliya Procter, Rob Jarvis, Stephen |
author_sort | Tkachenko, Nataliya |
collection | PubMed |
description | This paper aims to explore whether there is a relationship between search patterns for flood risk information on the Web and how badly localities have been affected by flood events. We hypothesize that localities where people stay more actively informed about potential flooding experience less negative impact than localities where people make less effort to be informed. Being informed, of course, does not hold the waters back; however, it may stimulate (or serve as an indicator of) such resilient behaviours as timely use of sandbags, relocation of possessions from basements to upper floors and/or temporary evacuation from flooded homes to alternative accommodation. We make use of open data to test this relationship empirically. Our results demonstrate that although aggregated Web search reflects average rainfall patterns, its eigenvectors predominantly consist of locations with similar flood impacts during 2014–2015. These results are also consistent with statistically significant correlations of Web search eigenvectors with flood warning and incident reporting datasets. |
format | Online Article Text |
id | pubmed-4892441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-48924412016-06-10 Predicting the impact of urban flooding using open data Tkachenko, Nataliya Procter, Rob Jarvis, Stephen R Soc Open Sci Computer Science This paper aims to explore whether there is a relationship between search patterns for flood risk information on the Web and how badly localities have been affected by flood events. We hypothesize that localities where people stay more actively informed about potential flooding experience less negative impact than localities where people make less effort to be informed. Being informed, of course, does not hold the waters back; however, it may stimulate (or serve as an indicator of) such resilient behaviours as timely use of sandbags, relocation of possessions from basements to upper floors and/or temporary evacuation from flooded homes to alternative accommodation. We make use of open data to test this relationship empirically. Our results demonstrate that although aggregated Web search reflects average rainfall patterns, its eigenvectors predominantly consist of locations with similar flood impacts during 2014–2015. These results are also consistent with statistically significant correlations of Web search eigenvectors with flood warning and incident reporting datasets. The Royal Society 2016-05-25 /pmc/articles/PMC4892441/ /pubmed/27293779 http://dx.doi.org/10.1098/rsos.160013 Text en http://creativecommons.org/licenses/by/4.0/ © 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Computer Science Tkachenko, Nataliya Procter, Rob Jarvis, Stephen Predicting the impact of urban flooding using open data |
title | Predicting the impact of urban flooding using open data |
title_full | Predicting the impact of urban flooding using open data |
title_fullStr | Predicting the impact of urban flooding using open data |
title_full_unstemmed | Predicting the impact of urban flooding using open data |
title_short | Predicting the impact of urban flooding using open data |
title_sort | predicting the impact of urban flooding using open data |
topic | Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4892441/ https://www.ncbi.nlm.nih.gov/pubmed/27293779 http://dx.doi.org/10.1098/rsos.160013 |
work_keys_str_mv | AT tkachenkonataliya predictingtheimpactofurbanfloodingusingopendata AT procterrob predictingtheimpactofurbanfloodingusingopendata AT jarvisstephen predictingtheimpactofurbanfloodingusingopendata |