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Optimal Sampling Design for Variables with Varying Spatial Importance
Author(s) -
Rogerson Peter A.,
Delmelle Eric,
Batta Rajan,
Akella Mohan,
Blatt Alan,
Wilson Glenn
Publication year - 2004
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/j.1538-4632.2004.tb01131.x
Subject(s) - computer science , crash , variable (mathematics) , motor vehicle crash , sampling (signal processing) , statistics , signal strength , sample (material) , measure (data warehouse) , data mining , poison control , mathematics , telecommunications , wireless , injury prevention , medicine , mathematical analysis , chemistry , environmental health , chromatography , detector , programming language
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 of 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.