Search and Rescue
In Wilderness Search and Rescue, finding the lost target as quickly as possible is the best way to ensure the target's survival. One way to facilitate a quick rescue is knowing where to look. My research focuses on developing probability based models predicting lost person behavior. This project is affiliated and supported by SARBayes.
Using gas simulations inspired by Burgen and Darken, I am developing a diffusion based model for human movement. Using elevation and land cover data, I estimate the relative walking speed in each cell. This is used as the maximum speed of diffusion across the cell. This model, run out to infinite time/equilibrium, has been tested on MapScore for roughly 200 cases in Yosemite National Park, and is showing promising results thus far.
Sample maps generated with an elevation based diffusion model. The lighter pixels denote higher probabilities of the target being found in the corresponding cell.
Using the International Search and Rescue Incident Database (ISRID)and Bob Koester's Lost Person Behavior, I have created different models in ArcGIS and Python. These models are based off of features such as distance from the last known point, elevation, and offset from roads/trails/rivers/etc. These models were also scored with Mapscore.
One specific model fitted distance from the last known point to a log-normal distribution. The distributions were calibrated to data from ISRID for each target category. I then ran statistical cross validation on the results, and a complete rightup can be found here.