Optimal sampling design for variables with varying spatial importance

It is often desirable to sample in those locations where uncertainty associated with a variable is highest. However, the importance of knowing the variable's value may vary across space. We are interested in the spatial distribution of Received Signal Strength Indicator (RSSI), a measure of the signal strength from a cell tower received at a particular location. It is crucial to estimate RSSI values accurately in order to evaluate the effectiveness of mayday systems designed for rapid emergency notification following vehicle crashes. RSSI estimation is less important for locations where the probability of a crash is low and where the likelihood a call completion is either close to zero or one. We develop a method for augmenting an initial spatial sample of RSSI values to achieve a high-precision estimate of the probability of call completion following a crash. We illustrate the approach using data on RSSI and vehicle crashes in Erie County, NY.