GLOBE is a global correlation engine, a project to study the effects on Land Change based on a set of parameters that include temperature, forest cover, human population, atmospheric parameters and many other variables. The aim of this research is to understand, how a study or a set of studies of specific geographic areas is representative of other areas of the world. The generic form of the question is, given a set of data points with a set of variables, how to determine how much a selected subset of points represents the rest of the distribution.
The research aims to answer a set of questions which include the definition of representativeness of a geographical site and how the representativeness can be computed. Researchers studying land change will dynamically select a subset of variables which they would like to study. Hence the method developed not only computes representativeness, but does so in an efficient manner. For this purpose, we apply dimension reduction techniques to reduce the size of computation and analyze the effectiveness of using these techniques to calculate representativeness.
|Commitee:||Finin, Tim, Schmill, Matt|
|School:||University of Maryland, Baltimore County|
|School Location:||United States -- Maryland|
|Source:||MAI 51/04M(E), Masters Abstracts International|
|Subjects:||Geography, Geographic information science, Computer science|
|Keywords:||Globe, Principal component analysis, Representativeness|
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