Mapping Louisiana's Missing: Spatiotemporal Profiling of Louisiana's Missing Persons- An Experimental Application of Geographic Information Systems and Forensic Anthropology
Bibliographic record
Abstract
The growing number of unresolved unidentified and missing persons cases in the United States is this nation’s ‘silent mass disaster’ (Ritters, 2007). In addition to contextualizing biocultural traits of these cases, forensic anthropologists are uniquely qualified to address this underrecognized humanitarian crisis due to their proven ability to bridge conflicting stakeholders in often complex sociopolitical environments and to create improved opportunities for community collaboration. This project explores local and state demographic trends of missing persons cases and how this information can be used to assist investigative agencies with their missing population, analyzes gaps in identification data, and selects optimal locations for community events that call attention to unidentified and missing persons cases.\nA total of 557 open and closed missing persons cases were used from the database of the Louisiana Repository for Unidentified and Missing Persons Information Program, hereinafter referred to as the Repository. CrimeStat© and ArcMap software were used to process and analyze geographic anchor point locations of missing persons and to produce visual representations of that data. While missing persons data in the Repository were generally comparable to other missing populations from the United Kingdom, Canada, and Australia, the demographic composition of those trends varied greatly among populations. The application of GIS helped illustrate gaps in data that are useful for the identification of unknown decedents; these cold spots can inform investigative agencies for future data collection. Lastly, a series of spatial clustering analysis methods were performed to elucidate five strategic spatially-informed locations for hosting missing persons outreach events that are designed to increase public awareness, collect additional information, and engage families of missing persons as valued stakeholders.\nThis study highlights the importance of discussing demographic trends in regional missing persons data that provide context as to why the reported missing population does not necessarily reflect the population at large. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".