One of the major challenges in fire investigation is the determination of the cause of fire. The fire can be accidental or intentional. The determination of ignitable liquid residue (ILR) from fire debris helps the process and this process is called fire debris analysis in forensic science. This is one of the most complex areas in the field of forensics because of the evaporation of the ILR from the debris and the interference of the substrate matrix with the ILR if present. In the present, the final decisions in fire debris analysis are based on categorical statements and it only represents the qualitative but not the quantitative value of the data. The likelihood ratio approach is one of the most widely used methods in forensic science in expressing the evidentiary value. The purpose of this research is to introduce the likelihood ratios calculated by the Naïve Bayes approach. The data for this work was obtained by the Substrate and ILRC Databases from the National Center for Forensic Science. This project also contributed to the expansion of the Substrate Database by adding 1500 new substrate burn data records. The compounds identified from ignitable liquids and substrates were used to calculate the frequency of occurrences of the compounds in substrates and ignitable liquids. The presence or absence of the compounds was determined by the probabilities calculated by logistic regression. These frequencies of occurrences were used in the calculation of Naïve Bayes log likelihood ratios. The application, performance and validation of these models are discussed in this dissertation. These calculated log-likelihood ratios indicated that this method provides high evidentiary values in the classification of fire debris as positive for ILR in most cases but provided low evidentiary values in some other instances.

The current protocols in fire debris analysis rely on ignitable liquid pattern recognition and the identification of target compounds. These practices allow fire debris analysts to determine whether a sample contains or is absent of ignitable liquid residue and to classify that type of ignitable liquid based upon subjective thresholds. A statistical approach using computationally mixed datasets was devised in this research to generate an objective approach to the classification of fire debris samples and to determine error rates. Quadratic, linear, and partial least squares linear discriminant analysis (QDA, LDA, and PLS-DA) models were developed and evaluated to determine the effects of different population distributions and the overall performance on known and unknown ground-truth fire debris samples. The evaluation of the method performance was determined by generating receiver operator characteristic (ROC) curves and calculating an area under the curve (AUC) based on log-likelihood ratios (LLR) scores. The Substrate Database was also expanded within this work to aid fire debris analysts in casework and to provide a more relevant population when generating statistical models. The most optimal population distribution was determined to consist of a uniform population of equal contribution of substrate and ignitable liquids, with each ignitable liquid class represented. The QDA model performed the best when evaluating the cross validation datasets, calculating an AUC of 0.975 ± 0.005. The PLS-DA model calculated the highest AUC for a limited validation 16 known ground-truth dataset (0.991 AUC) in comparison to the other models. All models were evaluated by determining an analyst's threshold for large scale burn data of unknown ground-truth. All models determined a conservative threshold (LLR = 1) as a cutoff score by the informed analyst.

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.

This master’s thesis examines the relationship between emergency management planning and comprehensive land use planning. The incorporation of emergency management practices into the comprehensive planning process allows for a better understanding of the impact of development, zoning, building code, and economic development on the mitigation of hazards that face the community. Academic curricula may provide a brief introduction of the relationship between hazard mitigation and land use; however, a more detailed exploration of how emergency management planning and regional or urban planning are interrelated is needed. The impact of weather-related events, natural disasters, or other human-caused shock or disruption can dramatically impact the physical, social, and psychological structures of a community. This research provides regional planners with the history of emergency management planning in the United States. It examines how cross-sharing of information and process between both planning disciplines can contribute to more robust community development and disaster plans. A case study illustrates the impact of urban development on natural hazard mitigation and the subsequent risks to public safety, which resulted from the planning decisions. Place identity, place dependence, and public participation concerning hazard mitigation and disaster management are explored to provide planners and emergency managers with a context of the psychological influences which may impact a community member’s decisions when faced with significant disruption of place. Best practices that guide the integration of regional planning and emergency management planning are provided to increase the understanding of both planning processes to increase the capacity of a community to absorb and rebound from a natural disaster or sudden shock.