Abstract
Structure (house) fires do not discriminate, they take life, injure, destroying property and damaging the environment. An average of 19,877 structure fires occurs annually in Australia at a cost of $3.3 Billion. Firefighting methods have largely remained unchanged since the Roman bucket brigades. Today, firefighters are still on standby and ready to respond (to a fire). This research highlights a disparate and uncoordinated body of knowledge with a clear gap in both the literature and practice. The research gap is the application of predictive analytics in the Emergency Management Sector that is to predict where and when a fire is most likely to occur. In this paper, sourcing two years of structure fire data combined with other datasets such as weather, we have shown predictive analytics could provide emergency managers with an Information System tool that would enable them to make informed decisions on future fire emergency scenarios. Using Activity Theory, we can demonstrate how Information System can be used as a tool to provide a safer community. The contribution of this research aims to use predictive analytics to identify at risk communities so that response time to fire-fighting can be decreased, and targeted fire safety education delivered.
Recommended Citation
Edwards, Andrew; Smith, Stephen; and Pang, Vincent, "A Data Driven Approach to Reducing the Risk and Impact of Structure Fires" (2017). PACIS 2017 Proceedings. 145.
https://aisel.aisnet.org/pacis2017/145