GIS for Search & Rescue Strategies & Concepts

Mountaineer Area Rescue Group

Appalachian Search and Rescue Conference

Geospatial Modeling for the Optimization of Search and Rescue Strategies

WVAGP Membership MeetingNovember 27, 2012

Sutton, WV

Don Ferguson, PhDMountaineer Area Rescue Group

A member of Appalachian Search and Rescue Conference



Where Do We Start Looking?

“Search” for a missing subject could be considered the proverbial “needle in a haystack”

Time is limited - Search is an emergencySubject may be injured / will not survive indefinitelyClues deteriorate over timeWeatherAvailability of resources

Goal of search management is to maximize the probability of success at the greatest rate possible.

Improve PSR by reducing the search area



Improving the Success Rate

Majority of searches are resolved within a few hours

Only about 1% – 3% required Extended Planning of operations

Extended searches typically encompass relatively large geographical areas

Two options for improving the Success RateReduce the search area

Search in most likely areas first

Increase the rate of detectionProper resource allocation



Need a systematic method to prioritize regions within the search area and evaluate progress.

SAR operations typically time and resource limited

OR provides framework to aid in decision-making and improve efficiency.

Developed during WWII to search for enemy submarines and improve resource allocation1

Bayesian Probability TheoryAssigning a probability to a hypothesis and adjusting the probability through testing without needing to fully resolving the hypothesis

Application of Operational Research to SAR

𝑃 (𝛼𝑖∨𝛽)=𝑃 (𝛼 𝑖 )×𝑃 (𝛽∨𝛼𝑖 )

∑𝑗=1

𝑘

[𝑃 (𝛼 𝑗 )×𝑃 (𝛽∨𝛼 𝑗 ) ]a1, a2, …, ak are mutually exclusive events (hypothesis), of which none has a zero probability. b is any event (data used to test the hypothesis).

1. Koopman, B.O., “Search and Screening: General Principles with Historical Applications”, Pergamon Press, New York, 1980., 2003.



Defining the Search Area Search area is defined by

a collection of methodologies

TheoreticalDistance traveledHistorical/Categorical mobility data

StatisticalHistorical/categorical search data

Subjective-DeductiveSubject and environment specific

AutisticDistance from IPP (km)

n 20

25% 0.6

50% 1.6

75% 3.7

95% 15.2

ISRID Data

Theoretical and Statistical Search Area

25%

50%

75%



Theoretical Search Area

This is the distance that the subject could have traveled in the time elapsed. How far, how fast?

Subject can theoretically travel in any direction3.14 * (1mile)2 = 3.14miles2

Identifying a clue or DOT could help to limit the search area 10 mile radius

Could also alter theoretical distance by considering terrain and environmental impedance



Theoretical Search Area…contCost Surface- High slope impassable- Reclassify NLCD- Roads, Trails,

Streams, Utilities, Fenceline, Water bodies

Speed Surface

Tobler, W. 1993. Three presentations on geographical analysis and modeling. Technical report. 93-1, National center for geographic information and Analysis, University of California, Santa Barbara.

Travel Time

Hours1

2

3 - 4

5 - 6

7 - 8

9 - 10

11 - 12

13



Subjective / Deductive Area

Formulate a series of hypotheses based on all available information (geospatial, subject profile, etc).

Compare hypotheses to determine most likely scenario (Analysis of Competing Hypotheses)



Subjective / Deductive Area: Probability Regions

A Region is a defined subset of the Search Area within which there is a uniform distribution of the likelihood of containing the search object1 – Probability of Containment (POC)

Assign “a priori” probability of the subject being in a location

Based on scenarios comprised ofTheoretical Search AreaStatistical Search AreaSurrounding geography

Natural and man-made features

Physical and Behavioral profiles of the missing subject



Well defined field recognizable boundaries

Segment POCArea-weighted POC associated with the Region POC.

Develop Searchable Segments

Sub-divisions of Probability Regions based on logistical and operational issues associated with conducting the search itself

Resource capabilities / availability

𝑃𝑂𝐶𝑠 , 𝑟=𝑃𝑂𝐶𝑟×𝐴𝑟𝑒𝑎𝑠 ,𝑟

𝐴𝑟𝑒𝑎𝑟

𝑃𝑂𝐶𝑟= ∑𝑠=1

𝑆𝑒𝑔𝑚𝑒𝑛𝑡𝑠

𝑃𝑂𝐶𝑠 , 𝑟



Goal is to reduce (minimize) the POC of a segment to zero

Hopefully locate the subject sooner

Update POC after search of each segment

Tracking Success

Segments are assigned based on POC with most likely locations being searched first.

𝑃𝑂𝐶 𝑠 ,𝑟 ,𝑡 1=𝑃𝑂𝐶𝑠 ,𝑟 ,𝑡 0×(1−𝑃𝑂𝐷𝑠 ,𝑟 ,𝑡 1)

𝑃𝑂𝐶 𝑠 ,𝑟 ,𝑡 2=𝑃𝑂𝐶𝑠 ,𝑟 ,𝑡 1×(1− 𝑃𝑂𝐷𝑠 ,𝑟 ,𝑡 2)

𝑃𝑂𝐶𝑟=∑𝑠=1

𝑆𝑒𝑔

𝑃𝑂𝐶𝑠 , 𝑟



Statistical Search Area Statistical data from previous searches

involving persons that could be considered categorically similar could provide valuable insight in defining the search area

Doherty, P., Guo, Q., Liu, Y., Wieczorek, J., and Doke, J., “Georeferencing Incidents from Locality Descriptions and its Applications: a Case Study from Yosemite National Park Seach and Rescue”, Transactions in GIS, 15(6): 775-793, 2011..



Statistical Search Area Assume you are the

administrator for a regional park.

The park consists of a series of trails with 11 different trailheads.

This park is used primarily by day hikers



Statistical Search Area Other the course of the

previous year you had 100 SAR incidents occur within your park.

Limited the dataset to only subjects reported as missing.Although your subjects ranged in age from 15 – 85 they all reported being in the park to hike.As a result of good record keeping you had coordinates for both the find and the IPP. In 100% of the cases the IPP was a subject’s vehicle parked at one of the trailheads.



Statistical Search Area Although you did not

have data reporting the actual distance the subject had traveled from the IPP to the point that they were found, you can measure the straight-line (crow-flies) distance between the IPP and the find location.



Statistical Search Area

With the IPP-Find distances plotted we can easily see the min and max values (Range)

Min = 0.4 kmMax = 14.5 km

Also may be interested in the mean or average distance recorded

Mean = 5.8

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 960

2

4

6

8

10

12

14

16

Distance from IPP to Find (km)

Incidents in Chronological Order

Dis

tance (

km

)

While interesting, the distances could contain “outliers” that could impact the value of the statistics.

𝑑= 1𝑛∑

𝑖=1

𝑛

𝑑𝑖



0 20 40 60 80 100 1200

2

4

6

8

10

12

14

16


Incidents Sorted by Distance (low - high)

Dis

tance (

km

)


If however, the distances are sorted by length we can obtain the “median” value

Middle value of an ordered set of numbersMedian = 5.2 km



0 20 40 60 80 100 1200

2

4

6

8

10

12

14

16


Incidents Sorted by Distance (low - high)

Dis

tance (

km

)


If however, the distances are sorted by length we can obtain the “median” value

Middle value of an ordered set of numbersMedian = 5.2 km

QuartilesDivide the distances into four equal groups25%, 50%, 75% and 100%

50%

75%95%

25%

Due to outliers, the 100% distance may not be of interest so limit the data to 95%.

3.23 km

5.2 km

8.0 km

12.0 km



Statistical Search Area…Application

Return to the original IPP and find locations.




Let’s consider only the IPP for a moment.




Let’s collapse all of the IPP’s to a single virtual IPP as shown on the map.




If we now re-plot virtual find locations at the same distances of our actual IPP-Find distances we would end up with a distribution that looks similar to this




If we divide up the 100 find locations into groups of 25

Draw a ring around the closest 25Draw a second ring around the next closest 25…and so on

If we assume that behaviors of “hikers” within the park is similar then we would expect other lost hikers within the park to be found at similar distances.

More data is better

However, we are not always just looking for lost hikers and sometimes we need to look for subjects outside of our own park.



Statistical Search Area…Find Locations

What if you also had collected data on the find location and categorized it by a predominate feature. Feature Percen

t

Road 3%

Fields 4%

Woods 88%

Koester, R.J., “Lost Person Behavior: A search and rescue guide on where to look - for land, air and water”, dbS Productions LLC, 2008.

Find features for “Hikers”

Using readily available geospatial data to “reclassify” spatial data to coincide with Find Locations



Statistical Search Area…Find Locations

Utilize: roads, trails and drainage layers along with land cover data (from National Land Cover Database)

Roads, Trails and Drainages



Statistical Search Area…Find Locations Identify features where finds are made. These

features may or may not represent attractants for the subject category

Roads – 3%

Fields – 4%

Woods – 88%

Find Locations



Statistical Search Area…Track Offset

Track Offset (meters)

25% 70

50% 126

75% 254

100% 964

Feature (0-20 m)

25% (21-70 m)

50% (71-126 m)

75% (127-254 m)

Consider the shortest distance between a linear feature (roads, trails, drainages, powerline ROWs, etc) and where the subject was found.



Statistical Search Area…Elevation

Elevation (meters)

3% +/-20

42% +21 : 1701

55% -1606 : -21

+/- 20 m

+31 : 1701 m

-1606 : -21 m

Consider the difference in elevation between the IPP and where the subjects were found.



Statistical Search Area…Mobility

Similar to the Theoretical Search Area, data is collected from subjects regarding the length of time they were mobile.

Mobility (hours)

1

2

3

4

5

6

7

8

> 8

Because we can predict travel time from spatial data (land cover, terrain, travel aides, etc) we can estimate travel time within the search area.

Note: Steep slopes inhibit access within this area. In this hypothetical situation the IPP (orange symbol) was adjusted due to its original location being position on a steep slope.

Mobility (hrs)

25% < 3

50% < 5

100% 9



Statistical Search AreaInternational Search and Rescue Database

ISRID project began in 2002 focused on the collection of information from missing/lost person incidents for the purpose of understanding lost person behavior

Currently contains over 50,000 missing/lost person incidents41 subject categoriesStatistical Search Area defined as the straight line distance (“Crow Flies”) from the Initial Planning Point (IPP – typically the last known location of the subject) to the find location.Additional analysis provided on the nature of the find location (structure, water, woods, etc). Koester, R., “Lost Person Behavior”, dbS Productions, Charlottesville, VA, 2008




Utilize historical data to predict location of current missing subject.

We are all a creature of habit even when we are lost. Results from ISRID have been published in

“Lost Person Behavior”Data from various eco-regions and includes 41 different subject categories



Track Offset Model

Hazardous Terrain Model


Mobility Model Elevation Difference

Model

Find Location Model

Modeling Lost Person Behavior

Operational Map



Questions?

Contact information:

Don FergusonMountaineer Area Rescue Group

[email protected](304) 290-9118

mailto:[email protected]

Documents

GIS for Search & Rescue Strategies & Concepts