Adoption of data-driven decision making in fire emergency management

Stephen Smith, Vincent Pang, Kurt Liu, Manolya Kavakli-Thorne, Andrew Edwards, Mehmet Orgun, Richard Host

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

Building fires fortunately rarely occur however they may cause the tragic loss of properties and lives. While we all watch the news and see the damage caused by fire on buildings and on rural vegetation, very little research has been undertaken on the rate a fire consumes a building's structure. This research began when the question was asked "If a building is ablaze, how long does it take before the structure is severely damaged". This project is an implementation of Big Data Analysis for Emergency Management through the use of statistical computing tool R and its data visualisation features to analyse historical data set provided by a NSW Government Public Safety Agency that responds to structural fire incidents. The main goal of this project is to determine the optimum fire services' response time on property losses due to urban fires in Australia. More precisely, the results of this study will aid the decision making of Fire Service Agencies by determining the correlation between the damage level and the Emergency services response time. This project has implications for Fire Services not only in NSW but nationally and internationally as there is a research gap in the analysis of (Australian) fire data.

Original languageEnglish
Title of host publication24th European Conference on Information Systems, ECIS 2016
Place of PublicationAtlanta, GA
PublisherAssociation for Information Systems
Pages1-15
Number of pages15
Publication statusPublished - 2016
Event24th European Conference on Information Systems, ECIS 2016 - Istanbul, Turkey
Duration: 12 Jun 201615 Jun 2016

Other

Other24th European Conference on Information Systems, ECIS 2016
CountryTurkey
CityIstanbul
Period12/06/1615/06/16

Keywords

  • Big data
  • Data-driven decision making (DDDM)
  • Emergency management
  • Theory of action

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