Incident times follow a von Mises distribution centered near 5:30pm.
One goal of the SARBayes project is to forecast the probability of survival for lost persons. Such models could be useful in deciding to continue searching, and researchers making motion models can use survival predictions when generating probability maps of the lost person's location. We are analyzing data from the International Search & Rescue Incident Database (ISRID) to describe the probability of survival as a function of various features, such as age or temperature.
The MapScore project described here provides a way to evaluate probability maps using actual historical searches. On a metric where random maps score 0 and perfect maps score 1, the ISRID Distance Ring model scored 0.78 (95%CI: 0.74-0.82, on 376 cases). The Combined model was slightly better at .81 (95%CI: 0.77-0.84).
Our MapScore paper is now in press at Transactions in GIS! From the abstract:
The MapScore project described here provides a way to evaluate probability maps using actual historical searches. In this work we generated probability maps based on the statistical Euclidean distance tables from ISRID data (Koester, 2008), and compared them to Doke’s (2012) watershed model. Watershed boundaries follow high terrain and may better reflect actual barriers to travel. We also created a third model using the joint distribution using Euclidean and watershed features. On a metric where random maps score 0 and perfect maps score 1, the ISRID Distance Ring model scored 0.78 (95%CI: 0.74-0.82, on 376 cases). The simple Watershed model by itself was clearly inferior at 0.61, but the Combined model was slightly better at .81 (95%CI: 0.77-0.84).
A logical extension of the Distance Rings model is to fit a smooth function to the distribution of data found in ISRID. Examining the Euclidean Distance data for different categories, it was found that a lognormal curve roughly captured the shape of the data. The Log-Normal (LN) is a two parameter distribution which assumes that the logarithm of your data follows a normal distribution. The probability density function of the LN curve is given by, where are the mean and standard deviation of the logarithm of distance.
Linkspage with a SAR & GIS bibliography including the memorably titled
Heggie, Travis W, and Michael E Amundson. 2009. “Dead Men Walking: Search and Rescue in US National Parks.” Wilderness & Environmental Medicine.
And the humorously mangled: Is, Information, Releasable To, and Foreign Nationals. “Search and Rescue Optimal Planning System ( SAROPS ).” Training 2.
And three articles it sounds like I should read soon:
Jobe, T.R., and P.S. White. 2009. “A New Cost-distance Model for Human Accessibility and an Evaluation of Accessibility Bias in Permanent Vegetation Plots in Great Smoky Mountains National Park , USA.” Journal of Vegetation Science: 1099–1109.
Miller, Harvey J., and Scott a. Bridwell. 2009. “A Field-Based Theory for Time Geography.” Annals of the Association of American Geographers 99 (1) (January 8): 49–75. link
Pingel, Thomas J. 2011. “Estimating an Empirical Hiking Function from GPS Data.” Sports Medicine: 1–3.
At Mason we're collaborating with Paul to test a Watershed-Distance model developed by his research group. Based on 58 tests run so far by Elena Sava on MapScore, this simple model scores 0.55. Not bad for a model that doesn't yet discriminate by category (or any other feature). Elena just finished a multivariate model combining Watersheds with the more usual crows'-flight distance, and we will begin testing that soon.
The SARBayesMapScore server has been running for a month now at http://mapscore.sarbayes.org. It's a portal for scoring probability maps, so researchers like us can measure how well we are doing, and see which approaches work best for which situations. Take a look. (And if you have a model, register and start testing it!)
Lin & Goodrich at Brigham Young are working on Bayesian motion models for generating probability maps. They have an interesting model, but need GPS tracks to train it. It's a nice complement to our approach, and it will be interesting to see how they compare.
~Originally a very cool review published in the first half of 2010. The review led to phone calls and a very productive collaboration on MapScore and other work.
Syrotuck's main study is his 1976, with N=242. But he gives much more detail about distance travelled in his 1975 paper, breaking distance down every 0.2 miles. Unfortunately, he only reports probabilities, not numbers, and doesn't even report total N. We know he got more data between 1975 and 1976, but didn't know how much. Is the 1975 breakdown representative of the 1976 data? Unfortunately, no one has Syrotuck's original data. But we re-created it. (Spreadsheets available!)
We have been collecting data on land SAR incidents in Australia since 2000. In November 2003 we wrote a draft report that was presented at the NATSAR council but not generally released. It was styled after the U.K. report. In June 2006 we released the final version (see below), which has evolved its own style.
Our data was collected using the form available below (or an earlier version thereof). The form itself helps define what is meant by each term or category, and is essential to interpreting the data. We have also prepared a definition key in the report, to explain our terms more precisely. The UK report gave us the idea, but there are some differences in definition.