Investigations that involve missing persons are time sensitive. Law enforcement agencies, often constrained by limited resources, base their investigative response on an individual’s personal history or perceived risk to safety. While this approach aims to maximize case closure rates, the accumulation of cases with a lower perceived solvability has contributed to the current humanitarian crisis of long-term unidentified and missing person cold cases in the United States. Missing persons events connect members of the community and families with resources and support in the effort to help identify and locate missing loved ones. In this chapter, the authors report on a novel application of missing persons data using a series of spatial clustering analysis methods to generate spatially informed locations to host missing persons outreach events. In this study, the last known location of 378 missing persons from the Louisiana Repository for Unidentified and Missing Persons Information Program provided geographic anchor points which were characterized as a clustered pattern distribution using the nearest neighbor analysis (NNA) index. The nearest neighbor hierarchical spatial clustering (NNHC) routine was then performed and visualized with convex hulls. Functioning as a density gradient, the kernel density estimate (KDE) identifies areas of greater intensity and, when referenced with the center of minimum distance, a more generalized yet delimited area becomes easier to evaluate potential event locations. The entire process of identifying potential event locations using both Euclidean distance and spatial interpolation were replicated for each cluster order, which represent different tiers of geographic generalization, as no single location in the state of Louisiana could be accommodating to all of its residents. These findings demonstrate the latent value of geospatial analyses when applied to missing persons data and how this approach can benefit an investigating stakeholder’s ability to alleviate human suffering.