Predicting the impact of urban flooding using open data. Issue 5 (May 2016)

• Record Type: Journal Article
• Authors: Tkachenko, Nataliya; Procter, Rob; Jarvis, Stephen
• Is Part Of: Royal Society open science. Volume 3:Issue 5(2016)
• Publication Date: 2016-05
• Subjects: Web search -- Google Analytics -- flood risk management -- urban resilience -- predictive analytics; Science -- Periodicals; 500
• DOI: 10.1098/rsos.160013
• Languages: English
• ISSNs: 2054-5703

Abstract

Abstract : 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.