MIST: Missing Person Intelligence Synthesis Toolkit

TL;DR

The paper presents MIST, a data-driven geospatial inference toolkit that improves search efficiency for missing persons by ranking search areas based on expert performance, significantly reducing search areas in case studies.

Contribution

Introduces MIST, a novel geospatial abductive inference system that enhances missing person search strategies by leveraging expert performance data.

Findings

Reduced search area by 31 square miles in experiments

Significantly outperforms current practices of FMG

Currently applied in an active case

Abstract

Each day, approximately 500 missing persons cases occur that go unsolved/unresolved in the United States. The non-profit organization known as the Find Me Group (FMG), led by former law enforcement professionals, is dedicated to solving or resolving these cases. This paper introduces the Missing Person Intelligence Synthesis Toolkit (MIST) which leverages a data-driven variant of geospatial abductive inference. This system takes search locations provided by a group of experts and rank-orders them based on the probability assigned to areas based on the prior performance of the experts taken as a group. We evaluate our approach compared to the current practices employed by the Find Me Group and found it significantly reduces the search area - leading to a reduction of 31 square miles over 24 cases we examined in our experiments. Currently, we are using MIST to aid the Find Me Group in an active missing person case.