Coconino County Sheriff's Search & Rescue
The WiSAR (Wilderness Search and Rescue) Decision Support Tool is a web-based geospatial application designed to assist SAR coordinators with search area prioritization and probability modeling for lost person incidents. The tool generates terrain-aware travel cost surfaces and probability contours from an Initial Planning Point (IPP) using anisotropic cost-distance analysis.
The tool supports two operational workflows: (1) IPP-only mode, which generates a probability surface and travel cost analysis from a single point with a user-defined search radius, and (2) CalTopo import mode, which imports search segments and IPP data from CalTopo for full segment overlay and Probability of Area (POA) ranking.
This tool was developed as a proof-of-concept to bridge the gap between spatial probability modeling and operational SAR workflows. It is intended to provide SAR coordinators with terrain-aware probability surfaces that replace traditional Euclidean range rings with contours shaped by actual terrain conditions, improving search area prioritization during active incidents.
The travel cost surface is constructed by combining landcover friction values with slope-derived travel cost. Landcover impedance values are sourced from the IGT4SAR framework (Doherty et al. 2013) as documented in Danser (2018). Trails and roads from OpenStreetMap are burned into the friction surface at impedance value 1 (no impedance). Waterways are assigned graduated impedance values based on feature type.
Cost-distance from the IPP is computed using an anisotropic implementation of Tobler's Hiking Function (Tobler, 1993). The algorithm computes directional slope between each pair of neighboring cells during traversal, applying the asymmetric speed function where slight downhill travel (~-2.86 degrees) achieves maximum speed of 6 km/h. The implementation uses true surface distance (3D hypotenuse) rather than horizontal distance, and includes a cross-slope traversal penalty.
User-provided percentile find distances (25th, 50th, 75th) are applied as contour thresholds on the cost-distance surface. These percentiles, typically sourced from Koester's Lost Person Behavior (ISRID data), define terrain-aware probability zones that replace traditional Euclidean range rings. The contours stretch along corridors of easy travel and compress against steep or densely vegetated terrain.
| Dataset | Source | Resolution | Usage |
|---|---|---|---|
| Digital Elevation Model | USGS 3DEP 1/3 Arc-Second | ~30m (resampled) | Slope derivation, surface distance, anisotropic cost |
| Land Cover | NLCD 2021 (MRLC/USGS) | 30m (native) | Landcover friction / impedance classification |
| Trails & Roads | OpenStreetMap (Overpass API) | Vector | Trail/road burn-in at impedance 1 |
| Waterways | OpenStreetMap (Overpass API) | Vector | Stream/river impedance overlay |
| Search Segments | CalTopo API (user-provided) | Vector | Segment import for POA ranking |
Landcover impedance values follow the IGT4SAR framework as documented by Doherty et al. (2013) and Danser (2018, Table 5). Values range from 1 (no impedance) to 99 (complete barrier).
| NLCD Code | Description | Impedance |
|---|---|---|
| 11 | Open Water | 99 |
| 12 | Perennial Ice/Snow | 85 |
| 21 | Developed, Open Space | 5 |
| 22 | Developed, Low Intensity | 10 |
| 23 | Developed, Medium Intensity | 15 |
| 24 | Developed, High Intensity | 20 |
| 31 | Barren Land | 30 |
| 41 | Deciduous Forest | 45 |
| 42 | Evergreen Forest | 50 |
| 43 | Mixed Forest | 35 |
| 52 | Shrub/Scrub | 45 |
| 71 | Grassland/Herbaceous | 20 |
| 81 | Pasture/Hay | 25 |
| 82 | Cultivated Crops | 30 |
| 90 | Woody Wetlands | 80 |
| 95 | Emergent Herbaceous Wetlands | 80 |
| Layer | Description | Default |
|---|---|---|
| Travel Cost Surface | Accumulated anisotropic cost-distance from the IPP. Continuous gradient from green (low cost) to red (high cost). | On |
| Percentile Contours | Terrain-aware range rings at 25th, 50th, and 75th percentile find distances. | On |
| Terrain Difficulty | Local terrain traversal difficulty independent of distance from IPP. | Off |
| Parameter | Value |
|---|---|
| Analysis Resolution | 30 meters (matched to NLCD) |
| Coordinate Reference System | EPSG:4326 (WGS 84) |
| Cost-Distance Algorithm | Dijkstra's shortest path, 8-connected, anisotropic |
| Slope Cost Function | Tobler's Hiking Function (1993), directional |
| Distance Calculation | 3D surface distance |
| Cross-Slope Factor | Up to 30% additional penalty |
| Max Search Radius | 25,000m (IPP-only mode) |
| Max Segment Extent | 50 km per side (CalTopo mode) |
| Server | Ubuntu 24.04, Python 3.12, Flask, Gunicorn, Nginx |
Danser, R.A. (2018). Applying Least Cost Path Analysis to Search and Rescue Data: A Case Study in Yosemite National Park. USC Thesis.
Doherty, P.J., Guo, Q., Doke, J., & Ferguson, D. (2014). An analysis of probability of area techniques for missing persons in Yosemite National Park. Applied Geography, 47, 99-110.
Ferguson, D. (2013). Integrated Geospatial Tools for Search and Rescue (IGT4SAR). GitHub.
Koester, R.J. (2008). Lost Person Behavior. dbS Productions.
Sava, E., Twardy, C., Koester, R., & Sonwalkar, M. (2016). Evaluating lost person behavior models. Transactions in GIS, 20(1), 38-53.
Tobler, W. (1993). Three presentations on geographical analysis and modeling. Technical Report 93-1, NCGIA.
Developer: Jamie Weleber
Organization: Coconino County Sheriff's Search & Rescue
URL: https://sar.weleber.net
Metadata Date: March 23, 2026
Version: 1.0 (Proof of Concept)
Created by: Jamie Weleber