Search Theory in C4ISR Journal

The C4ISR Journal had a recent search theory article quoting me along with Larry Stone.  I'm quite honored, like the British company in Dirk Gently's Holistic Detective Agency:

...was the only British software company that could be mentioned in the same sentence as ... Microsoft.... The sentence would probably run along the lines of ‘...unlike ... Microsoft...’ but it was a start.

It's a good article, covering the undeniably exciting historical origins hunting U-boats, and looking at what may be a modern renaissance.  I think the article stretches to connect search theory with Big Data, but the author does note that when the data is visual, and you have humans scanning it for objects, there is a connection.  With planning, it could have been used to prioritize the Amazon Mechanical Turk search for Jim Gray.  (The resolution of the actual images in that search was probably too low regardless, but the core idea was sound.)

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Structured Methods for Intelligence Analysis

My colleagues just published a paper in Euro Journal on Decision Processes, for their special issue on risk management.

Karvetski, C.W, Olson, K.C., Gantz, D.T., Cross, G.A., "Structuring and analyzing competing hypotheses with Bayesian networks for intelligence analysis". EURO Journal on Decision Processes, Special Issue on Risk Management: http://link.springer.com/article/10.1007/s40070-013-0001-x

Alas, it's behind a paywall and the printed edition isn't due until Autumn. Here's an excerpt from the abstract:

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Banning et al 2011. Lateral range curve for cluelike objects.

"Small Scatters of Generally Unobtrusive Artifacts"

When searching for an image for this post, I came across several works by E.B. Banning applying search theory to archaeology:

  • Sweep widths and the detection of artifacts in archaeological survey. (2011) [Science Direct]
  • Detection functions for archaeological survey (2006). [JSTOR]
  • Archaeological Survey (2002 book). [Google books]

Now what would archaeologists be doing with sweep widths?  Looking for nails, shards, and other small objects in the soil.  What they nicely call "small scatters of generally unobtrusive artifacts on the surface".

In WiSAR we call them clues.

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Paul Doherty's Research Page

Just a quick note to highlight Paul Doherty's new research page.  It includes:

  • Overview of his research
  • Publications list
  • Software & Datasets page, including links to MapSAR and discussion groups.
  • 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.

 

lateral_range

A Brief Intro to Search Theory (2 of 4)

In the previous post, we began to build a theory of detection over time as the result of a very large number of independent glimpses.  By assuming the environment to be fixed for awhile, we moved all the environmental factors into a constant (to be measured and tabulated), and simplified the function so it depended only on the range to the target.

In this post we simplify still further, introducing lateral range curves and the sweep width (also known as effective sweep width).  We will follow Washburn's Search & Detection, Chapter 2.  (So there's nothing new in this post. Just hopefully a clear and accessible presentation.)

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MapScore public leaderboard, from October 2012.

MapScore: A Portal for Scoring Probability Maps

The SARBayes MapScore 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!)

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