High profile structure fires, like the Grenfell Tower tragedy, have demonstrated that the quality of the information provided to firefighters arriving on the scene of an emergency is a matter of life or death. It has been suggested that access to structural information such as electronic building plans or unmanned aerial vehicle footage may bridge the information gap to help first responders build situation awareness at the incident scene. However, these technologies have not been fully evaluated from a human performance perspective. The use of pre-incident plans (PIPs), information captured systematically about a facility prior to an emergency, provides a way for firefighters to leverage data about a structure, increasing their efficiency and effectiveness in managing a fire and ultimately reducing fatalities and property damage. However, no standard interface configuration currently exists for presenting and displaying PIP information to firefighters digitally. This dissertation investigates the human factors implications associated with leveraging emerging technology in the form of 3D models and situated visualization techniques for displaying PIP information to fireground incident commanders. Through a series of mixed-method (qualitative and quantitative) studies, this dissertation directly captures user requirements and human performance data from firefighters in the form of focus groups, field data, surveys, and user study data. Based on qualitative participant feedback and objective user study data, this series of studies evaluate the usability (efficiency, effectiveness, satisfaction) of three different user interface configurations. This data serves as a foundation for standardizing the way PIP information is presented to first responders. Further recommendations are suggested for how to effectively present and display PIP information to better support fireground incident commanders operating in dangerous and unpredictable environments.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1802 |
| Date | 01 January 2020 |
| Creators | Kapalo, Katelynn |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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