Copyright M. Colwell. 1994,1997                                                                                May 1997 
 
Search Priority
- an Integrated Approach  to Search Planning 
by
Martin Colwell

1.  Introduction 

 Over the past twenty years a variety of concepts have been developed to assist in systematic search planning. These concepts include Probability of Area (POA), Shifting Probability of Area (shiftPOA), Probability of Detection (POD), Single-Pass POD Calibration, Probability of Success (POS), Probability Density (PDEN), and Search Efficiency. 

 Each of these concepts have improved the theoretical effectiveness of the search process. However the implementation of these techniques into actual search operations has been fairly slow, as certain aspects of these techniques have not been well matched to the realistic constraints of actual search conditions. This article will attempt to show how these variables, along with the new concept of Search Priority, can be applied to provide a useful and workable, integrated search planning tool. 

2.  Concept Limitations 

- Probability of Area 

 The concept of Probability of Area, and how this factor shifts as the search areas are swept, has been well documented for some time. However many search teams are uncomfortable with using this technique for reasons other than just a lack of training. The primary objection, especially in steep, rugged terrain, is that this terrain cannot be divided up into areas that can be realistically searched. Another objection is that large search areas may have to be subdivided into a great many search segments, making the assigning of POA's and their shifting, impractical under the time, and other, pressures of an ongoing search. Difficulties with determining just how large each search area should be made have also detracted from the use of the probability of area concept. 

 - Probability of Detection 

 The primary problem with the probability of detection concept has been the difficulty in obtaining actual field-derived POD data. Most POD 'information' until recently, has been little more than 'educated' guesses. This problem has been addressed by Wartes, Bounds and the author . Now, using dedicated software , local POD data can be rapidly obtained by search teams, using their specific search techniques, in their terrain. Acquiring this POD data is an essential first step in area search planning, and is the basis for the necessary logistic calculations required for a well managed area search. The priority that we give to a particular search resource should also be related to that resource's predicted Probability of Detection. 

- Probability of Success 

 The concept of Probability of Success has probably been overstated. In fact the well-known formula:

     POS = POA x POD 

simply means that as an area is searched to a higher POD there is an increased likelihood of a successful search, i.e. of a 'find'. POS by itself does not add any additional information to the search planning process, but it used to indicate that areas with higher POS's should usually be searched first. The problem with the POS concept is that it is somewhat abstract and does not take into account such real field problems as the size of the search area and of the manpower required to actually perform the search. This paper will illustrate that if we link the search manpower considerations to the Probability of Success then we can develop a much more useful planning tool. 
- Probability Density  

 This useful concept  is defined as the probability of area divided by the size of the search area: 

PDEN = POA / Size of Search Area

The probability density concept tells us, for example, that if two search areas have an equal probability of area, and that one area is smaller than the other, then one may as well search the smaller area first, as this area requires less effort (manpower) to search. This concept is valid but can have one weakness in application; if the size of the search area is defined without adequate care then virtually any PDEN value can be obtained. To be most useful PDEN values should be based on historical or statistical search data, i.e. on what proportion (%POA) of missing persons may been found within each geographically defined distance or area. 

 3.  Search Area Definition  

 Traditional instruction in search management has told us that search areas, once defined, should usually be searched in order of the highest probability of area first. Obviously this is a laudable goal but if this area happens to be very large, i.e. it has a low probability density, then searching such a large area may be a completely impractical proposition. 

The notion of requiring a high probability of success, i.e. of applying a high probability of detection to the large, high-probability area, only makes this POS goal even more unattainable; the logistic impracticality of applying very high POD manpower requirements to search the very large area, soon becomes apparent. Subdividing this large area into smaller, more manageable, segments makes the execution of the search more practical but does not, of course, reduce the overall large manpower requirements. Obviously high POA's, and high POS's for large areas, which both require increased manpower to search, are not of practical assistance to the search manager attempting to deploy limited field resources. 

 Subdividing large search areas into smaller more manageable segments is a useful method for making practical the searching of large areas. Unfortunately confusion often occurs over such problems as - how large should each of these segments be, should they be regarded as areas of a particular POA, or as segments with different POA's, and what priority or POS values should be assigned to these segments? Suggestions for segmenting large search areas are given at the end of this article. 

4.  Search Priority  

 Many of the problems outlined above can be addressed if a simple new concept, called Search Priority, is applied to search planning. Obviously in an ideal world, where manpower limitations do not apply, it is preferable to apply whatever manpower is necessary to obtain a high POD in a high POA area. Unfortunately, in practice, manpower availability is usually limited and so whatever search planning decisions are made must take into account the manpower availability. This practical situation has usually been ignored in most high POA and high POS planning scenarios. 

Conventional search planning practice generally states that a higher POA, or a higher POS ranking (which can be raised by simply demanding a higher POD), shall be used to set the priority as to which probability areas shall be searched first. Lower POA or lower POS areas are usually searched subsequently. As stated above this ranking system fails to take into account many practical logistic problems. 

An important requirement of good search planning is to define the search areas carefully and accurately, preferably using the geographic features and local, historical POA data for that region. It is important to remember that trails and creek beds, as well as larger areas, must also be considered as search areas, even if they are extremely narrow and therefore often small in size. 

 Instead of using a high POA (or a high POS) as the basis for the search priority it is useful to also take into account the effort required to actually search that area. This search effort may generally be expressed in two ways: either indirectly, as the size of the search areas, or, more accurately, as the manpower required to search that area to a particular POD. Conceptually Search Priority may be considered as increasing with increased probability of area (%POA) and decreasing with increased search Area. Therefore: 

Search Priority     =                Probability of Area                (Equation #1) 
(Area-based)                   Search Area (Sq. Miles or Sq.Km)  


This area-based definition of Search Priority is identical to the familiar theory of probability density, which has the same formula. Using equation #1, where the Search Priority is based on the size of the search areas, the search manager may rapidly produce a list of Search Priority values, for each of the search areas within the entire search region, using only two factors: historical or estimated POA's, and the size of each search area, which may be read directly off the map. The area with the highest Search Priority value should usually be searched first (all other considerations being equal) as this area will, approximately, have the highest probability of area for the manpower required to search that area. The remaining search  areas should then each be searched in descending order of Search Priority. 

A much more useful definition of Search Priority would also take into account a search team's expected Probability of Detection, along with the manpower required to access, search and exit the area. Generally speaking we may say that the Search Priority should increase with the Probability of Area and with the team's expected Probability of Success, with the Probability of Success defined as the POA x POD. 

Generally speaking we may also state the the Search Priority should decrease with an increase in manpower penalty required to access and search an area. 

If we link these two relationships together we can develop the following, more precise, definition of Search Priority, that takes into account many of the factors actually involved in the effective deployment of search teams: 

 Search Priority   =       Probability of Area x Probability of Detection     (Equation #2) 
(Manpower-based)                               Total Searcher-Hours  
 
- required to access, search and exit the area to the specified POD 

or, more specifically: 

Search Priority  =        Probability of Area x Probability of Detection          (Equation #3) 
(Manpower-based)         # Searchers x (Access hrs + Search hrs + Exit hrs)  
 
- required to access, search and exit the area to the specified POD 

The factors that are integrated into this definition of manpower-based Search Priority include: 

Probability of Detection Factor: 
 If a search resource can be applied in an area to a fairly high POD then this will result in a relatively high Search Priority for that resource in that area. If the resource can only provide a low POD then the Search Priority of that resource in that area will be lowered. 
For example:  
A helicopter may provide a high POD over an open gully, which will cause that area search to have a relatively high Search Priority. If the helicopter is then tasked to search a similar sized area of very dense forest, where its POD will be significantly reduced, then that resource in that area will be given a proportionally lower Search Priority. In this way resources that generally have a higher POD in the assigned search area will be given a higher Search Priority. 

Manpower Factor:  
 The manpower-based Search Priority equation (#3) is much more useful than the area-based Search Priority equation (#1) because it takes into account the actual manpower (searcher-hours) required to access and search each area to a particular POD. For example: if only 3 searchers are required to search a creek-bed area, then this is entered directly into the manpower-based Search Priority equation. If an area can be searched with less manpower for the same POD, e.g. by using a Sound-Sweep rather than a visual-sweep, then these reduced searcher-hours are entered into equation (#3). In this way the faster and more efficient use of manpower will be reflected by that resource in that area having an increased Search Priority. 
 
Access & Exit Factors:  
The proximity, or remoteness, of a search area also effects the manpower-based Search Priority value, through the access hours and exit hours required to reach and later exit the search area. If less time is required to reach or exit the search area then more time will be available for searching. So lower access hours and exit hours will result in an increased Search Priority for that area. 

Terrain Factor:  
The effects of terrain are reflected, indirectly, in the manpower-based Search Priority equation: 
If a fixed area of open meadow can be searched by only a few searchers, compared to the much larger manpower requirement to search a dense forest of equal size, then these terrain-influenced searcher-hour requirements will be reflected in differing Search Priority values for these different types of terrain. 

Search Resource Factor:  
The effects of using different types of search resources are also reflected in  manpower-based Search Priority. For example: If, for the same POD level, a faster resource may be used within an area, e.g. a snowmobile instead of a searcher on snowshoes, then the snowmobiles increased speed will reduce the searcher-hours required to search the area. These reduced searcher-hours will increase the Search Priority value if the faster resource can be used within that area. 
(The logical extension to this principle is the very high priority we usually assign to area searching with helicopters; they are so fast that their searcher-hours are very low, and therefore their Search Priority will generally be very high. For this reason areas that may be effectively searched by helicopter are often intuitively given a very high Search Priority). 

 It can be seen from the above discussion that the manpower-based Search Priority equation is a very simple, but practical, tool that integrates many real searching factors into the production of accurate Search Priority ratings. It is, in effect, a cost-benefit analysis of the various search options, priorizing the best potential return (Probability of Success) for the limited search resources (Total Searcher-Hours) available to the search manager. 
 
The general Gridsearch Formula, given at the end of this article, may be used to calculate the number of searchers and search hours required to search a defined area of known size. These number of searchers, search hours and the access and exit hours are then entered directly into the manpower-based Search Priority equation (#3), to produce that areas' Search Priority value. 

 Unless a previous POD calibration has been performed no reliable POD value can be accurately assigned to these number of searchers and search hours deployed in the field. To be able to meet a specified POD, with its known searcher-hour requirements, requires that the search team perform, or have access to, a field POD calibration for that type of terrain. Some of this logistic POD data has already been determined by Wartes, Bounds and the author. 

5.  Producing the Search Priority List.  
 
Whether the area-based Search Priority equation (#1), or the manpower-based Search Priority equation (#3) is used, the goal is to produce a Search Priority list for the entire search region, or at least for those areas for which there is some doubt as to the best search priority. Either pre-defined search areas and their POD-manpower requirements alone, or a complete draft Search Priority list, including tentative POA's, may be prepared well ahead of an actual search, as part of the search teams' pre-planning process. 

The initial, draft, probability of areas should be based, where possible, on the previous historic data of the actual locations of missing persons found within that search region. If this is not possible then general statistical, or concensus-based, probability of areas should be assigned at search time. The specific search areas e.g., basins, valleys, boulder fields, alpine meadows, creek-beds and trails, can often be identified prior to an actual search, and each area's size - and hence manpower requirements - pre-calculated. Given previous search experience, the Gridsearch Formula or field-calibrated (or estimated) POD information , the manpower requirements (searcher-hours), for each search resource to be deployed in each search area, may be determined either prior to, or at, search time. 

In the event of a search the Search Priority list may be utilized to rapidly deploy resources to the field. If necessary the list may be modified, to re-assign different probabilities of area, or resources, based on the current information available. Obviously a number of draft Search Priority lists may be pre-prepared, to meet different search situations; e.g. prepare one list for summer hikers in the local hiking region, and another list for lost skiers in the vicinity of the downhill ski resort, etcetera. 

A significant advantage of preparing Search Priority lists - in addition to the optimum deployment of manpower - is a more carefully planned and executed search operation. As well as being able to compare the priority of searching many areas with a variety of resources, we may also include the priority of deploying multiple resources within each area, in the overall Search Priority ranking list. 

Thus, many of the difficult POA, POD, resource and manpower deployment questions, that are often hurriedly performed in the hectic early minutes of a callout, may now be carefully planned and priorized using Search Priority. 

6.  An Example Integrated Search Priority Plan. 

Steep forests, steep drainage gullies, cliff bands and waterfalls, occasional open talus slopes, limited alpine meadows and a variety of hiking trails - often along the ridge tops - characterize the mountains around the Howe Sound Crest Trail, immediately to the north of Vancouver, in British Columbia. This area, which is typical of much of the terrain of British Columbia, Southeast Alaska and Washington State, has not usually been regarded as model terrain for Probability of Area searching. In fact it is seldom applied in these regions. Most Search Managers rely on previous history and experience to determine where to deploy their resources. Large area searching is seldom performed, primarily due to the ruggedness of the terrain, although the development of the Sound Sweep has seen some change in this direction.

An integrated Search Priority list was prepared for the Howe Sound Crest Trail (HSCT) region, taking into account the region's POA statistics, local field-calibrated POD information, the geographic search areas, access and exit times for these areas, search speeds within the areas and local search resource availability. 

 The example Search Priority Worksheet included with this article may be used to simplify the preparation of the Search Priority list. Alternatively, search management software  may be used to rapidly prepare the Search Priority list. Either way, the general procedure is as follows: 

Step 1. Assign Probability of Areas 
Step 2. Determine the Size of each search area 
Step 3. Calculate the area-based Search Priority                     (Equation. #1) 
          or, for manpower-based Search Priority... 
Step 4. Determine the Access Time to each search area 
Step 5. Estimate the POD of the search resource within the search area. 
Step 6. Determine the Searcher-Hours required to search each area to the estimated POD. 
Step 7. Calculate the manpower-based Search Priority           (Equation. #3) 

The detailed procedure for calculating the Search Priority is described below: 

Step 1.  Assign Probability of Areas  
The entire region surrounding the HSCT was broken down into nine areas. Each area was initially assigned a probability of area (POA) directly proportional to the percentage of the total number of lost persons found in each area over the last ten year period. These draft POA values were then adjusted to meet the details of the immediate search incident. If historical POA information is not available then statistical and circumstantial information, specific to the current incident, will have to be used alone to assign the probabilities of area. 

Step 2.  Determine the Size of each search area  
- For all area-based Search Priority calculations 
The size of each area was determined directly from the map of the region. The area of each creek-bed was determined from its length and a 'default' width of 100m. 

The probability of area assignments, and each area's size, were as follows: 

 
Step 3. Calculate the area-based Search Priority 

For each search area the area-based Search Priority was calculated using Equation #1: 


 Search Priority  =                     Probability of Area                     (Equation #1) 
 (Area-based)                 Search Area (Sq. Miles or Sq. Km)   
                                                 
The relative area-based Search Priority rankings are given in Table #2 below: 

 
OR,  for manpower-based Search Priority calculations: 

Step 4. Determine the Access and Exit Time for each search area 

The time required for search teams on foot to hike into the boundary of each search area was estimated from typical hiking times from the closest trailheads. If transportation (such as helicopters, vehicles or snowmobiles) is used to reach the search areas then the access time is based on the speed of the resource used to reach the search area. Exit times may differ from access times depending on whether the searchers exit by a different route than that used to access the area, or where exit times are different to access times for the same (access and exit) trail. For example a downhill exit may be faster than an uphill access to the search area.

Step 5. Estimate the Probability of Detection of the search resource within the search area 

The POD estimate was initially set at 80%, for all of the search resources in all the search zones,. This estimate was based on previously obtained field POD data. 

Step 6. Determine the Searcher-Hours (Manpower) required to search each area 
 
(i) Define the Search Conditions required to meet the estimated POD goal(s). 
- All of the area searching (i.e. excluding the creek-beds) was initially based on the very efficient Sound Sweep , which requires 3 sweeps at 210m (689ft) spacing to attain an 80% POD. 
- It was assumed that in the creek-beds a 3-man search team would also attain an 80% POD, over a 'default' 100m (328ft) wide creek width (between the forested creek banks). 

(ii) Determine the Searcher-Hour requirements to meet the POD goal(s) 
- Define a fixed search time:  
A 6 hour field search time was assumed for all teams and the manpower requirements calculated on the basis of this one 6-hour search time. 
(Whether more or less elapsed search time is actually required is not relevant to the Search Priority ranking, which uses only the total searcher-hours required to search that area to the required POD.) 

- Define a default area search speed: 
The default area ground search speed was set at 0.40 km/hour (0.25 miles/Hr), a speed determined from previous rugged forest area searches. 

- Calculate the searcher-hour requirements: 
a).  For the search areas: 
 The Gridsearch Formula (given at the end of this article) was used to calculate the searcher-hours required to search each area, for 6 hours of Sound Sweep searching at a speed of 0.40 km/hr 
(0.25 miles/hr). 
The Sound Sweep conditions of 3 sweeps at 210m (328ft) spacing (to 80% POD) were entered into the Grid Search Formula, to calculate the number of searchers required. 

b). For the creek-beds: 
 A 3-man search team was assigned to each creek. where it was estimated that an 80% visual POD would be obtained. Each creek-bed zone required 3 searchers for 6 search hours. 

(In practice, if the search times are much shorter or longer than 6 hours is not critical, as what is important is that each team can practically only search one creek area in one day.) 

The calculated Total Searcher-Hour requirements, to access,search and exit each area, by each initially assigned resource, are shown in Table #3. 

Step 7. Calculate the manpower-based Search Priority 

For each search area the manpower-based Search Priority was calculated using Equation #3: 

Search Priority  =                   Probability of Area X Probability of Detection          (Eqn #3) 
(Manpower-based)        # of Searchers X (Access hours + Exit hours + Search hours)         

- required to access, search and exit the area to the specified POD. 

The calculated Total Searcher-Hours, required for determining manpower-based Search Priority, are given in Table #3 below: 

After calculating the total Searcher-Hours, in Table #3, the manpower-based Search Priority ranking may now summarised in Table #4 as follows:

 
Note:  All of the Search Priority calculation steps described above may be quickly performed on a single worksheet, see ICS 215a Operations Plan Worksheet or by using dedicated search management software. See the example worksheet #1 included with this article. 

7.  Discussion 

 Each of these Search Priority lists shows that the creek-beds, which are only 5% POA areas, generally have the highest Search Priority and should be searched first, followed by the highest POA area, the 40% POA Upper Harvey Basin. This is in distinct contrast to classic search management teachings, which would generally indicate searching the highest POA, (and in this example, a much larger and more manpower intensive) area first. The Search Priority ranking indicates the most efficient deployment of resources, by ranking them according to the best potential benefit (probability of finding the missing person) for the limited manpower resources available. 

This example clearly indicates the benefits of searching some of the much higher Search Priority, but low POA, creek-bed areas before searching the more manpower intensive, high POA areas. 

 The Search Priority rankings also show that the second and third highest POA areas, the two large forests, should only be searched after all the other areas have been searched. This is because of the high manpower requirements necessary to search these areas, relative to their assigned probability of areas. Interestingly, the 'Parking Lot Problem' has also been resolved; ie whether a small, low POA area, such as the parking lot near the Point Last Seen, should be searched before the higher probability areas. In this case it is optimal to search the parking lot area before embarking on the higher POA, but much more manpower intensive, forest regions. 

8.  Priorizing Multiple Resources within a Single Search Area. 

For the sake of simplicity the example manpower-based Search Priority lists shown above, and on worksheet 1, details only a single resource within each search area. In practise it is common to deploy multiple resources within the same search area. The manpower requirements of these additional resources may be added as new entries to the original Search Priority worksheet, where they will be ranked with all the original area resources. 

The example worksheet #2 shows how these additional resources have been included along with the original entries for the Search Priority calculations. This new Search Priority ranking may now be used to determine how all of the listed resources may be deployed to the search areas. 

 The following search properties were assigned to the new resources: 

The helicopter access speed and search speed were both estimated to be 50 km/hr (31 miles/hr), with the access and search hours determined from the time required to access and search up and down each gully. Two 'spotter' searchers were assumed to be riding in the helicopter and providing an estimated 80% POD. 

The search dogs access speed was assumed to be 2 km/hr (1.2 miles/hr). 
A 10% POD was estimated, based on one sweep of the search area at 100 meter (328ft) spacing, at a search speed of 0.4 km/hr (0.25 miles/hr), for 6 search hours. 

An 40% POD (field-calibrated) 'Standard' Visual Sweep was based on one sweep of the search area at 63 meter (207ft) spacing, at a search speed of 0.40 km/hr (0.25 miles/hr), for 6 search hours. 

This area search data was entered into the Gridsearch Formula, to determine the number of searchers required for each resource. The number of searchers, search hours and access hours for each resource were then included as additional resource entries, for their search areas, in the Search Priority worksheet #2. 

9.  Updating the Search Priority Ranking  

It should be noted that the Search Priority ranking is based on the Probability of Area assignments given at the time the Search Priority calculation was performed. Once one resource has searched an area that area's POA will be lowered and all the other area POA's will change. A new Search Priority ranking should be calculated after each area has been searched, to determine the updated sequence for best deploying the search resources to the areas. 

10.  Comparison to Current Search Practice 

 Skilled Search Managers' have long known, through training and experience, that they have to deploy their resources sparingly, and have often been reluctant to perform large area searches when localized, small-area searching appears to be much more effective. Even when these small search areas have a much lower POA, as in the creeks in this example, the effort required to find the missing person is also much lower, and so these small areas are frequently amongst the first field assignments. This is an efficient deployment of manpower, and is reflected in the high Search Priority values assigned to these creek-bed search areas.

 
A large-scale search for a missing hiker, involving multiple search teams and various search managers, was conducted along the Howe Sound Crest Trail region, a few months before this Search Priority study was performed. It is interesting to note that the Search Priority rankings detailed above match, almost exactly, the actual sequence of searching conducted over this extensive, multi-day search. It would appear therefore that the mathematical Search Priority model quite closely mirrors, and may help justify, the intuitive decision-making of experienced Search Managers. 

11.  Conclusions  

 The Search Priority concept provides for the rapid and efficient deployment of search resources into the field. Local area information is used to build an integrated Search Priority list for local search planning. This list takes into account such factors as probability of areas and the previous search history of the area, probability of detection goals, manpower and resource availability, resource search speeds, access time and the size of the local geographic areas. This comprehensive, but easily prepared, Search Priority plan may be used to expedite the fast and efficient deployment of search resources to the field. 

 It is strongly recommended that search teams prepare their own, local, Search Priority lists, as part of an integrated approach to developing a comprehensive and effective search plan. 

  Appendix:

   Search Area Segmentation

The recommended method for defining when, and how, to segment a search area is as follows: 

1. Define the entire search region into search areas. 
These areas should preferably have clearly delineated boundaries and should, wherever possible, be searchable by a single search team in one day. Each search area should be defined as the smallest area that can be given its own POA rating. 

2. Assign a %Probability of Area to each area. 

3. If the search area is still too large to be searched in one day then it should be subdivided further into a number of search segments, each capable of being searched in one day. A typical size for a search segment would be: 
0.30 Sq Km (0.12 sq miles) for an 80% POD standard visual sweep, or 1.00 Sq Km (0.40 sq miles) for an 80% POD sound sweep, - using 6-man search teams for 6 hours at 0.40 km/hr (0.25 miles/hr). 

4. The Probability of Area of each segment will be: 
 
                    %POA Segment  =                     %POA Area       
                                                                Number of Segments  

For example, if an area has a 10% POA and is sub-divided into ten segments then each segment will have a POA of 1.0%. 

5. The Search Priority will remain the same in each segment, as for the entire area because, although the POA's of the segments have been reduced (e.g.) to one tenth, so also has the size of the search segments. 

* Therefore it does not matter in which order the segments are searched within one area, as they will all have the same Search (and Segment) Priority 

                                                   SEARCH PRIORITY

Search Planning Worksheet #1  (Example: Single Resource per Search Area)

  Area                POA    %POD     (POAx    Search    Access+ Exit+ Search   # of       Total-Hours    Search  Search 
Description     Rating  Estimate  %POD)   Area        Hrs      Hrs     Hrs   Searchers  x # Searchers  Priority  Priority 
..& Search        1 to 9        %              %       Sq.Kms    ----Total Hours---                    SearcherHours Value  Ranking
Resource          or %                                 or Sq.Miles                                                                          (%POA/%POD   #  
                                                                      or 'Route'                                                                             /Searcher-Hrs)     
Montizambert Cr......3-Man team      5%    80%   4%  Route   05+0.25 + 6             3         20.25            0.198        # 1  
Lembke Creek...........3-Man team      5%    80%   4%  Route   0.5+0.5 + 6              3          21                0.190        # 2  
U. Harvey Basin.......Sound Sweep 40%   80%  32%    4.0      1.25+1.0 + 6          24        198                0.162        # 3  
Lonetree Creek.........3-Man team      5%    80%   4%  Route   1.25+1.25 + 6          3           25.5            0.157        # 4  
Newman Creek.........3-Man team       5%    80%   4%  Route  1.5 +1.5+ 6               3           27               0.148       # 5
Lions Mtn  & talus..Sound Sweep   5%    80%   4%   0.75     2.0 +1.0+ 6               4            36              0.111       # 6
Yew Lk./Parking lot.Sound Sweep    3%   80%  2.4%   1.0     0.25+ 0.25 + 6           6            39              0.062      # 7  
Sisters Forest...........Sound Sweep  10%   80%    8%    6.0     1.0 +1.0+ 6              36          288              0.028      # 8  
W. Side Forest.........Sound Sweep  10%   80%    8%  10.0     0.5+1.5 + 6              60         480               0.017      # 9  

Search Planning Worksheet #2  (Example: Multiple Resources per Search Area)

  Area                POA    %POD     (POAx    Search    Access+ Exit+ Search   # of       Total-Hours    Search  Search 
Description     Rating  Estimate  %POD)   Area        Hrs      Hrs     Hrs   Searchers  x # Searchers  Priority  Priority 
..& Search        1 to 9        %              %       Sq.Kms    ----Total Hours---                    SearcherHours Value  Ranking
Resource          or %                                 or Sq.Miles                                                                          (%POA/%POD   #  
                                                                     or 'Route'                                                                               /Searcher-Hrs)  
Newman Creek.........Helicopter        5%    20%   1%   Route   0.03+ 0.03+0.09     2           0.30             3.333        # 1  
Lonetree Creek.........Helicopter        5%    20%   1%   Route   0.02+0.02+0.13      2           0.34             2.941        # 2  
Lembke Creek...........Helicopter        5%    20%   1%   Route   0.07+0.04+ 0.08     2           0.38             2.632        # 3  
Montizambert Cr......Helicopter        5%    20%   1%   Route   0.02+0.09+ 0.11     2           0.44             2.273        # 4  
Montizambert Cr......3-Man team      5%    80%   4%  Route   05+0.25 + 6             3         20.25            0.198        # 5  
Lembke Creek...........3-Man team      5%    80%   4%  Route   0.5+0.5 + 6              3          21                0.190        # 6  
U. Harvey Basin.......Sound Sweep 40%   80%  32%    4.0      1.25+1.0 + 6          24        198                0.162        # 7  
Lonetree Creek.........3-Man team      5%    80%   4%  Route   1.25+1.25 + 6          3           25.5            0.157        # 8  
Newman Creek.........3-Man team       5%    80%   4%  Route  1.5 +1.5+ 6               3           27               0.148        # 9  
Lions Mtn  & talus..Sound Sweep   5%    80%   4%   0.75     2.0 +1.0+ 6               4            36              0.111      # 10  
U. Harvey Basin......Visual Sweep   40%   40%  16%    4.0     1.0+1.25 + 6            26          215              0.074      # 11  
Yew Lk./Parking lot.Sound Sweep    3%   80%  2.4%   1.0     0.25+ 0.25 + 6           6            39              0.062      # 12  
Sisters Forest...........Sound Sweep  10%   80%    8%    6.0     1.0 +1.0+ 6              36          288              0.028      # 13  
W. Side Forest.........Sound Sweep  10%   80%    8%  10.0     0.5+1.5 + 6              60         480                0.017      # 14  
Yew Lk./Parking lot.Search Dogs     3%   10%  0.3%   1.0     0.0+ 0.5 + 6                4          26                 0.012      # 15 

Note: The higher the Search Priority Value, the higher the Search Priority Ranking

Probability of Area's (POA) are more accurately expressed as a % 
(All areas, inc. ROW, must add to 100%.) 
 
or, less accurately (but more easily), as a Relative POA Rating: 
 
 POA Rating Scale:  9=V.Likely  7=Likely  5=Even Chance  3=Unlikely  1=V.Unlikely  

 Search Priority as POA/Search Area  is quick to calculate. 
 Search Priority as (POA x %POD)/Searcher-Hours  is more accurate, as it accounts for actual manpower useage. Preplan by calculating search Areas and their Searcher-Hours prior to a search. 
 

Grid Search Formula  Metric Units: 

Number of Searchers =     Searchable Area (Square Km)  x  Number of Sweeps  x  1000  
                                             Search Hours  x  Speed (Km/Hr)  x  Searcher-Spacing (m) 

Search Hours   =      Searchable Area (Sq. Km)   x  Number of Sweeps  x  1000   
                                 No. of Searchers  x  Speed (Km/Hr)  x  Searcher-Spacing (m) 

Searchable   =   No. Searchers x Search Hours x Speed (Km/Hr) x Searcher-Spacing (m)  
 Area (Sq.Km)                                   Number of Sweeps  x  1000  

Grid Search Formula  US Units:

Number of Searchers  =   Searchable Area (Square Miles)  x  Number of Sweeps  x  5280 
                                              Search Hours  x  Speed (Miles/Hr)  x  Searcher-Spacing (ft) 

Search Hours  =       Searchable Area (Sq. Miles)   x  Number of Sweeps  x  5280     
                                  No. of Searchers  x  Speed (Miles/Hr)  x  Searcher-Spacing (ft) 

Searchable  =   No.Searchers x Search Hours x Speed (Miles/Hr) x Searcher-Spacing (ft) 
Area (Sq.Miles)                                     Number of Sweeps  x  5280                                          
 

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