Tag Archives: Probability Maps

Forthcoming MapScore Paper!

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).

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The Lognormal Distance Model

Eric Cawi

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.

Child 4-6 lognormal plot

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Incoherence & Mattson

The following figure is from a recent paper I co-authored*:

Figure from Karvetski et al. 2014 showing we get more accuracy by ignoring incoherent estimates than by simple unweighted averages.

Figure from Karvetski et al. 2014 showing we get more accuracy by ignoring incoherent estimates than by simple unweighted averages.  (The unfortunately abbreviated 'BS' means 'Brier Score'. Lower is better, with 0 being perfect.)

What implications does it have for making subjective "consensus" probability maps at the start of a search?

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MapScore Updates

The MapScore site has been updated! The most exciting new feature is Batch Upload.

  • Batch Upload!  In one fell swoop, upload and re-score all your models and cases (or as many as necessary).  No more clicking around or messing with “active” vs “inactive” cases.  (Unless you want to...)
MapScore batch upload screen. New Nov. 2013.

MapScore batch upload screen. New Nov. 2013.

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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.


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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Bayesian Motion Models of LPB

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.

Partly reconstructed March 2012.

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