In this study, researchers explore the use of a GIS application for building a nationally representative forensic taphonomy database.
This project aimed to demonstrate a proof-of-concept data collection method to capture observations of decomposition along with weather and environmental data to create a reference dataset. Once the data collection method, geoFOR, was created and tested, the geoFOR application was used to create a large forensic taphonomy reference database through mass collaboration efforts of practitioners within medical examiner and coroner’s offices and human decomposition research facilities. Subsequently, the reference dataset was used to create a machine learning model to provide PMI estimates along with an 80% prediction interval based on observations of decomposition characteristics and historical 3 weather data. Finally, the machine learning model was and continues to be updated as new data is added to the reference dataset. Estimating time since death, or the postmortem interval (PMI), is one of the most critical questions after the discovery of human remains. However, PMI estimation remains an enduring challenge to medicolegal death investigations despite decades of research and the creation of six U.S. human decomposition facilities created explicitly to inform this looming question. Current methods for estimating the PMI lack the scientific rigor required within the medicolegal realm as they are often based upon small sample sizes in environmentally homogeneous regions, and the definitions of the gross morphological changes associated with the decomposition process are inconsistent. For these reasons, existing methods are not statistically robust and cannot adequately account for the wide variation and influence of external and internal variables that are known to influence the rate of decay postmortem.