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.

"Evaluating LPB Models" Published

The "Evaluating Lost Person Behavior" paper is now officially available in the online edition of Transactions in GIS.

The "Evaluating Lost Person Behavior" paper is now officially available in the online edition of Transactions in GIS.

Sava, E., Twardy, C., Koester, R., & Sonwalkar, M. (2016). Evaluating Lost Person Behavior Models. Transactions in GIS, 20(1), 38–53. http://doi.org/10.1111/tgis.12143

Continue reading ""Evaluating LPB Models" Published"

Forthcoming MapScore Paper!

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).

Continue reading "Forthcoming MapScore Paper!"

Coast Guard investigating ground IR detectors for ice rescue

Do you happen to have an infrared WiSAR detector for cold weather?

USCG wants a portable infrared WiSAR detector.  This RFI was posted on 2-OCT:

The Coast Guard Research and Development Center (RDC) is conducting market research to identify technologies that are suitable for conducting IR searches on foot for persons on frozen waterways. The parameters include detection capabilities of one mile, and recognition capabilities at one-half mile, and identification at approximately one-quarter mile by personnel on foot (monopod is possible). The parameters also include the need to function in extremely cold temperatures, be temporarily submersible, and function regardless of weather conditions or the time of day/night for IR detection.

MapScore Updates Summer 2014

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 "MapScore Updates Summer 2014"

Bluefin-21 Analysis

One of our SciCast forecasters posted an excellent analysis of how he estimated the (remaining) chance of success for Bluefin-21 finding MH370 by the end of the question.

Forecast trend for Bluefin-21 success, on SciCast.
Forecast trend for Bluefin-21 success, on SciCast.

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.)

Bayes net model created by jkominek to explore the Bluefin question.
Bayes net model created by jkominek to explore the Bluefin question.

 

The full blog post is here: http://blog.scicast.org/2014/06/11/scicast-bluefin-21-and-genie/

 

Incoherence & Mattson

People are often incoherent: their probabilities don't add to 100%. We get an 18% gain in accuracy if we coherentize their estimates. But we get a 30% gain in accuracy if we also assign more weight to coherent estimates.

What implications does this have for making subjective probability maps?

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?

Continue reading "Incoherence & Mattson"

UAVs, MH370, Prediction Markets

In which I discuss two-and-a-half approaches to crowdsourcing Search & Rescue, and invite you to try one -- namely, mine: http://bit.ly/1eBBMkq

In which I discuss two-and-a-half approaches to crowdsourcing Search & Rescue, and invite you to try one -- namely, mine.  (SciCast)

Continue reading "UAVs, MH370, Prediction Markets"