Berkshires 2006 Sweep Width Experiment

In the summer of 2006, Rick Toman and Dan O'Connor (NewSAR) organized a sweep width experiment and summit called "Detection in the Berkshires" at Mount Greylock in Massachusetts. The data from that experiment has been used in previous blog posts, but hasn't been published independently.

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.

The data from that sweep width experiment has been used in previous blog posts and in a new article in Wilderness & Environmental Medicine, but hasn't been published independently.

Detection in the Berkshires. Back row: Andy Petrie, Bob Rando, George Rice, Ken Hill, Pete Roberts, Charles Twardy, Dave Perkins, Rick Toman Seated: Dan O’Connor, Joe Hess. Not shown: Jack Frost, Art Allen
Detection in the Berkshires. Back row: Andy Petrie, Bob Rando, George Rice, Ken Hill, Pete Roberts, Charles Twardy, Dave Perkins, Rick Toman
Seated: Dan O’Connor, Joe Hess.
Not shown: Jack Frost, Art Allen

Overview

Thirty-four searchers from several Massachusetts SAR groups ran the course which had 15-17 of each type of search object, yielding between 470 and 580 total detection opportunities per object. The main results are summarized in the following table:

AMDR ESW
Adult Hi-Vis 39m 40m
Clue Hi-Vis 16m 22m
Clue Lo-Vis 9m 11m

It's also interesting to see the raw POD data. Note that the experimental design spaces objects according to their AMDR, so as to keep PODs in the middle ranges. We need both detections and misses to measure detectability!  Averaging over the 34 searchers, we find:

  • Adult Hi-Vis: 44%   (24%-59%)
  • Clue Hi-Vis  : 47%   (21%-64%)
  • Clue Lo-Vis : 37%   (13%-73%)

Almost all objects could be detected if you knew where to look, but by design some were placed out near the limits of detectability. The conditions were otherwise ideal.  Searchers who did not know where to look found just under half the high-visibility targets, and more like one third of the low-visibility ones.

Sweep Width Experiment

Charles Twardy, Jack Frost (USCG), and Art Allen (USCG) set up an ESW (Effective Sweep Width) experiment following the procedure in Koester et al. (2004).  We deviated from that procedure in a few ways:

  • We used only one adult target: the high-visibility adult manikin.
  • Our clues targets were not colored gloves but assorted shoes donated by New Balance: brown-or-black shoes for low-visibility clues, and white shoes for high-visibility clues.

More tbd...

Clues

Adult hi-vis manikin on the trail.
Adult hi-vis manikin on the trail.
Hi-vis clue (white sneaker) among vegetation.
Hi-vis clue (white sneaker) among vegetation.
Low-vis clue (brown shoe) amid vegetation.
Low-vis clue (brown shoe) amid vegetation.

Data

The IDEA spreadsheet with all the data that has been entered so far is mostly in this sheet:

IDEA_GreyLock_Revisited

However, stand by for updates: in preparing this post I realized that some AMDR measurements were done on a different version of the spreadsheet.  This section will be revised....

Miscellanea

It's worth posting an observation by Ken Hill.  He served as a data logger for one searcher, and decided that while logging he couldn't really do more than look at one side of the course.  The searcher of course was looking at both sides.  Ken ended up detecting slightly more objects than the searcher, and being a psychologist, suggested that we are not actually perceiving anything while swiveling our head, and might do better to search in pairs, with one person looking left and the other looking right.  I don't know if anyone has followed up on this idea, but this kind of assigned "sector" searching is common on aircraft and, I am told, in military ground patrols.  There is plenty of data on saccadic masking and change blindness to suggest we try Ken's idea.

Thanks

I'm going to crib from Rick Toman's thank-you email to all participants.  It reminds me there were many more people involved in making this happen. To hit just the highlights:

  • Lt. Scibelli and other EPO's from Massachusetts State Police
  • Massachusetts SERT participated in both the ESW and tactical training
  • Civil Air Patrol participated and helped with organization.
  • Central Mass. SAR Team participated and helped
  • Camp Mohawk for the Friday venue and quarters
  • George Rice from NASAR and NJ SAR provided a management perspective
  • Ranger Joe Hess from New York participated, engaged in several discussions, and helped clean up the course.
  • Robb Grace of Berkshire Mountain SAR assisted with data collection
  • DCR Ranger Bob Rando and his staff facilitated the whole event and provided valuable assistance throughout.
  • State Police Tactical Ops provided extra support
  • Andie Petrie, EPO MIS Section Chief, provided maps, data management, and a mobile command post, and a great can-do attitude.

Author: ctwardy

Charles Twardy started the SARBayes project at Monash University in 2000. Work at Monash included SORAL, the Australian Lost Person Behavior Study, AGM-SAR, and Probability Mapper. At George Mason University, he added the MapScore project and related work. More generally, he works on evidence and inference with a special interest in causal models, Bayesian networks, and Bayesian search theory, especially the analysis and prediction of lost person behavior. From 2011-2015, Charles led the DAGGRE & SciCast combinatorial prediction market projects at George Mason University, and has recently joined NTVI Federal as a data scientist supporting the Defense Suicide Prevention Office. Charles received a Dual Ph.D. in History & Philosophy of Science and Cognitive Science from Indiana University, followed by a postdoc in machine learning at Monash.

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