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Modelling map positional error to infer true feature location
Author(s) -
Barber Jarrett J.,
Gelfand Alan E.,
Silander John A.
Publication year - 2006
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5550340407
Subject(s) - unobservable , feature (linguistics) , computer science , process (computing) , data mining , artificial intelligence , ground truth , pattern recognition (psychology) , mathematics , philosophy , linguistics , econometrics , operating system
The authors consider the issue of map positional error, or the difference between location as represented in a spatial database (i.e., a map) and the corresponding unobservable true location. They propose a fully model‐based approach that incorporates aspects of the map registration process commonly performed by users of geographic informations systems, including rubber‐sheeting. They explain how estimates of positional error can be obtained, hence estimates of true location. They show that with multiple maps of varying accuracy along with ground truthing data, suitable model averaging offers a strategy for using all of the maps to learn about true location.