What is POC?
The Probability of Containment (POC) reflects the estimated likelihood that the search object is physically located within a defined search area, sub-area, or grid cell.
Key Characteristics
Pre-search estimation based on all available information
Dynamic value that updates after each search operation
Foundation metric for calculating Probability of Success (POS)
Factors Influencing POC
POC is fundamentally a pre-search estimation based on all available information, including:
Last Known Position (LKP)
The most recent confirmed location of the search object, serving as the primary reference point for probability calculations.
Drift and Error Models
Mathematical models accounting for environmental factors like wind, current, and navigation errors that affect object movement.
Historical Data
Previous incident patterns and statistical data from similar search scenarios in the same geographic area.
Environmental Factors
Current weather conditions, sea state, terrain features, and other environmental variables affecting object location.
Calculating Initial POC
For Uniform Distributions
When you have no strong preference for specific areas within your search zone:
Divide the total search area into equal-sized grid cells
Assign each cell an equal share of total probability (100% / number of cells)
For Non-Uniform Distributions
When you have specific datum points or lines (e.g., flight path, drift trajectory):
Statistical Models
Use mathematical models based on error propagation and uncertainty analysis
Historical Patterns
Apply data from previous similar incidents in the same geographic area
Expert Judgment
Incorporate operational experience and local knowledge from SAR professionals
Updating POC After Searching
Once a search is performed, the POC must be updated for each grid cell within the searched area. This ensures that search planning remains dynamically linked to previous search outcomes.
Update Formula
POC_new = (1 - POD) x POC_old
Where POD is the Probability of Detection achieved during the search
Practical Example
Initial Conditions
POC Initial: 65%
POD from search: 79%
Calculation
POC_new = (1 - 0.79) x 0.65
POC_new = 0.21 x 0.65
POC_new = 0.14 or 14%
Important: Cells outside the searched area retain their previous POC values. This procedure ensures that search planning remains dynamically linked to previous search outcomes.
Key Takeaways
POC represents your confidence in area selection
Higher POC areas should be prioritized early
POC must be updated after each search operation
Base calculations on all available information
Use appropriate distribution models for your scenario
POC directly impacts your overall Probability of Success