AbstractMissing person behaviour datasets and other literature highlight that a significant proportion of missing people are found dead in water. Despite this, there is little knowledge to assist search operatives and investigators in establishing how human bodies move in an aquatic environment, which can be used to locate them more expeditiously.
This research began in 2008 and involves the collection and analysis of quantitative and qualitative data relating to deceased human bodies found in inland water. The study aims to identify factors that might affect body movement and their subsequent location. It seeks to establish if these variables could determine search parameters more effectively and increase the chances of successful search operations and associated investigations. This could provide earlier resolution in missing person cases, reduce the risk to search operatives, and ensure timely evidence gathering. The information may also facilitate preventative measures to reduce the number of drowning incidents.
This thesis is presented in two parts. Data on the case circumstances and variables were initially collected through a questionnaire. The sample used for analysis in part one of this thesis consists of the first 280 of these cases. Statistical analysis identified relationships between a range of variables and body buoyancy and movement. Two of these variables, the amount of clothing and the type of footwear worn by the subject were identified for further investigation through laboratory experiments. These are presented in part two of this thesis.
This work will cover the findings of the field data analysis and laboratory experimentation and will examine some basic predictive models constructed to assess the likely movement of human bodies in inland waterways. It will discuss ways in which the research can enhance knowledge of body movement in inland waterways, inform current search strategies and assist with missing person and crime investigations.
|Date of Award||2021|
|Supervisor||Lucina Hackman (Supervisor) & Masoud Hayatdavoodi (Supervisor)|