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Classification methods for spatial data representation

By Toshihiro Osaragi

Abstract

It is necessary to classify numerical values of spatial data when representing them on a map and visually understanding it. In consequence, loss of information from original data is inevitable in the process of this classification. A gate loss of information might lead to a misunderstanding of the nature of original data. In this study, a classification method of spatial data is proposed, in which the loss of information is minimized. Comparing our method with other existing classification methods, some new findings are shown

Topics: spatial data, visualization, classification, information loss, AIC (Akaike’s Information Criterion)
Publisher: Centre for Advanced Spatial Analysis (UCL)
Year: 2002
OAI identifier: oai:eprints.ucl.ac.uk.OAI2:254
Provided by: UCL Discovery

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Citations

  1. (1987). A study on the optimum mesh size in view of the homogeneity of land use ratio”,
  2. (1972). Information theory and an extension of the maximum likelihood principle”
  3. (1967). The Data Model Concept in Statistical Mapping"

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