GIS Integration for Search Management

The first task in building a search GIS is establishing the coordinate reference system. Choosing the wrong CRS, or failing to reproject layers to a common system before overlaying them, is among the most common and embarrassing errors in operational spatial analysis. For most terrestrial searches, a national projected CRS (UTM zone, British National Grid, GDA2020 for Australia) is preferred over geographic latitude-longitude because it gives distances in metres and preserves shape at local scale.

• Geological map (polygon vector): lithology and superficial deposit types. Source: national geological survey (BGS, USGS, state geological surveys). Provides soil parent material and permeability context.
• Soil survey (polygon vector or raster): texture, drainage class, horizon depth. Source: national soil databases (SSURGO, Cranfield NATMAP). Determines probe depth expectations and subsidence timeline.
• Digital elevation model (raster): for slope, TRI, and viewshed. Source: national LiDAR archive or SRTM/ASTER for initial screening in data-poor regions.
• Remote sensing layers (raster): NDVI change, thermal anomaly mask, SAR backscatter difference. Generated from Sentinel-2, Sentinel-1, UAV survey outputs.
• Infrastructure and access routes (line/polygon vector): road network, tracks, field boundaries. Source: national mapping agency data, OpenStreetMap. Used for access modelling.

A probability-weighted map is built by reclassifying each contributing layer into a common score scale (typically 1-5 or 0-100) and summing the weighted layers. The weighting step is a deliberate judgment: how much does each variable actually predict the target location? For clandestine grave search, published research (primarily from Hunter, Cox, Tibbett, and their collaborators) provides empirical reference points, but case-specific intelligence always modifies the weights.

The output is a colour-coded raster. A search coordinator reads off the high-probability cells (warm colours), confirms their accessibility and feasibility, and assigns the first ground teams to those cells. As cells are cleared, their status is updated and the probability surface can be reweighted: a negative probe result in a cell provides evidence about its neighbours (the burial is unlikely to be in a continuous cluster centred here), which adjusts the remaining search priorities.

A fundamental constraint in clandestine disposal is physical effort. The realistic carry distance for a person transporting a body on foot depends on body mass, terrain gradient, and time available. GIS least-cost path analysis models this constraint spatially by building a cost surface, where each raster cell has a cost of crossing based on slope gradient, vegetation density, and path availability, and then computing the cost-distance from all road and track access points.

Published studies on clandestine burial sites suggest that solo, vehicle-less carry distances of 50 to 150 m from a driveable track account for the majority of recorded cases. Vehicle-assisted transport can extend this to the limits of track accessibility. Modelling both buffers and intersecting them with the probability surface focuses effort on the overlap zone that satisfies both the spatial evidence and the physical feasibility constraint.

• Vehicle track access: buffer the full trafficable track network to the expected vehicle approach distance from last known sighting. This defines the outer boundary of vehicle-accessible terrain.
• Carry distance from track end: apply cost-distance from the track termini using a slope-weighted friction surface. The 50-150 m isoline is the primary search ring; the 150-300 m isoline is secondary.
• Intersection with probability surface: multiply the access distance layer by the probability surface. Cells that are physically accessible and geomorphically or spectrally anomalous receive the highest combined scores.

Ground-penetrating radar, earth resistance, and magnetometry surveys all produce gridded data: a set of measurements on a regular or irregular sampling pattern. Before these can be compared to terrain, soil, or remote sensing layers, they must be georeferenced. This requires that the survey team record the real-world coordinates of at least the four corners of the survey grid, and ideally a set of internal control points, using a GNSS receiver with sub-metre accuracy.

Once georeferenced, the geophysical grids are imported as rasters and overlaid with the search GIS. The spatial question is: does the geophysical anomaly fall within a cell already flagged by terrain, soil, or spectral analysis? Coincidence between independently derived anomalies is the strongest indicator available before excavation. A GPR reflection at a location that also has anomalous NDVI, elevated TRI, and proximity to an access track represents a convergence of independent evidence streams that justifies prioritised excavation.

One of the most practically important uses of GIS in search management is recording what has been done. Without a spatial coverage record, teams revisit areas already cleared, wasting effort, and courts are left with no documentation that a thorough search was conducted. A simple search coverage layer consists of a polygon grid over the search area with attributes for: search method used, date, team identification, weather conditions, and outcome (searched-clear, searched-inconclusive, not yet searched).

Negative outcomes are as evidentially important as positive ones. A systematic record of cells searched by cadaver dog and cleared, followed by cells searched by GPR and cleared, constrains the remaining possibility space and provides proportionate court evidence that the search met a professional standard. In cold cases where the original search was poorly documented, an audit of the coverage record can reveal gaps that were never searched and may justify a re-investigation.

Digital spatial data used as evidence must satisfy the same chain-of-custody requirements as any other forensic exhibit, but the practical implementation differs from physical evidence. The key requirements are: provenance documentation (where did this data come from, on what date, from which source?), processing integrity (what operations were applied and in what order?), and access control (who has handled the file and when?).

1. Acquisition logging
   Record the source, acquisition date, download URL or institutional source, licence terms, and file checksum (MD5 or SHA-256) for every raw dataset at the time of acquisition. Store this log alongside the data.
2. Version control
   Keep raw input files read-only and immutable. All derivative products (reclassified layers, difference rasters, probability surfaces) are stored in named output folders with processing scripts or documented step-by-step logs. No overwriting of inputs.
3. CRS documentation
   Record the coordinate reference system of every layer in the processing log. If reprojection is applied, document the source and target CRS and the software version used. CRS errors are a frequent target of expert challenge in court.
4. Exhibit preparation
   For court presentation, export maps with scale bar, north arrow, CRS declaration, legend, and data source attribution. Preserve the original GIS project file (QGIS .qgz or ArcGIS .aprx) as the auditable master record alongside the exported graphics.

QGIS and ArcGIS both support processing history logging. QGIS's Processing History and ArcGIS's Geoprocessing History pane provide a partial automatic log, but manual documentation of key decisions, why certain weights were chosen, why a particular layer was excluded, is the analyst's responsibility and cannot be delegated to the software. Expert testimony that cannot explain the analytical choices will not survive cross-examination.