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.
Thanks very much to summer intern Jonathan Lee (@jonathanlee1) for many MapScore fixes. Jonathan is a keen Python programmer with extra geek points for running Linux on his Macbook Air and having an ASCII-art avatar. He learned his way around Django in no time and brought us a slew of features and code refactoring including: Continue reading
Jkominek was wondering why the probability kept jumping up, and created a Bayes Net to argue that there was no good estimation reason for it. (There may be good market reasons -- cashing in to use your points elsewhere.)
The full blog post is here: http://blog.scicast.org/2014/06/11/scicast-bluefin-21-and-genie/
The following figure is from a recent paper I co-authored*:
What implications does it have for making subjective "consensus" probability maps at the start of a search?
In which I discuss two-and-a-half approaches to crowdsourcing Search & Rescue, and invite you to try one -- namely, mine. (SciCast)
Short interview showcasing GIS for search and rescue. The reporter only identifies the system as "ArcGIS" but I believe it is Don Ferguson's IGT4SAR.
In the summer of 2006, Rick Toman (Massachusetts State Police) and Dan O'Connor (NewSAR) organized a sweep width experiment and summit called "Detection in the Berkshires" at Mount Greylock in Massachusetts. In addition to the sweep-width experiment, Perkins & Roberts provided search tactics training for several teams, and the summit provided a chance for us to explore similarities and differences between formal search theory and formalized search tactics. It was an important the chance to meet many key people, compare notes, and discuss ideas. I wish I had been more diligent about following up. Many thanks to Rick & Dan for organizing the event, and to many others listed at the end. However, this post is mostly to provide a reference for the sweep width experiment.