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What is geoFOR?

GeoFOR is a crowdsourced forensic taphonomy database that seeks to address current gaps in the field related to postmortem interval estimation by adhering to the Standard for Taphonomic Observations in Support of the Postmortem Interval (2022). Our app remedies longstanding issues in forensic application by providing an ongoing, mass collaborative reference sample that spans a breadth of environments, includes decomposition characteristics that better encapsulate the complexities of the process, and employs machine learning models to improve PMI predictions. GeoFOR also equips forensic practitioners with a readily available tool to enter case information that automates the collection of environmental data and delivers a PMI prediction using statistically robust methods.


Determining the time elapsed since death is a critical piece of information to provide law enforcement when human remains are discovered. An accurate estimation of the postmortem interval can facilitate the identification of an unknown individual and help to reconstruct the events around their time of death. 


geoFOR recognizes the major weakness with the current state of research is the lack of a large and representative reference dataset that can be used to develop and test new methods for estimating the time since death. We seek to improve this through a crowdsourced method for compiling information about cases to create a robust dataset. As practitioners in need of a reliable method of determining PMI, you will be helping gather and moving the field forward. 


This dataset utilizes a spatially coded, GIS application that is accessible from mobile devices and tablets, among other devices. Forensic investigators working on a case use the app to record basic scoring information on the state of decomposition, and the GIS software records the location of the discovery.


Spatial databases can be accessed and aligned to the GIS location data gathered by forensic investigators at scenes. The spatial information collected at the scene can be easily associated with other environmental data, such as temperature and vegetation, that may be of interest to researchers in refining models for calculating the PMI.


The ultimate goal of this project is two-fold--to provide practitioners with a simple app to automate PMI estimations and to create a database that can be used by researchers to devise, test, and refine models of decomposition rates in order to improve PMI methods.

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