An integrated cloud model for measurement errors and fuzziness

Tao Cheng, Zhilin Li, Deren Li, Deyi Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Two kinds of uncertainties - measurement errors and concept (or classification) fuzziness, can be differentiated in GIS data. There are many tools to handle them separately. However, an integrated model is needed to assess their combined effect in GIS analysis (such as classification and overlay) and to assess the plausible effects on subsequent decision-making. The cloud model sheds lights on integrated modeling of uncertainties of fuzziness and randomness. But how to adopt the cloud model to GIS uncertainties needs to be investigated. Indeed, this paper proposes an integrated formal model for measurement errors and fuzziness based upon the cloud model. It addresses physical meaning of the parameters for the cloud model and provides the guideline of setting these values. Using this new model, via multi-criteria reasoning, the combined effect of uncertainty in data and classification on subsequent decision-making can be assessed through statistical indicators, which can be used for quality assurance.
Original languageEnglish
Title of host publicationProgress in Spatial Data Handling - 12th International Symposium on Spatial Data Handling, SDH 2006
Pages699-718
Number of pages20
DOIs
Publication statusPublished - 1 Dec 2006
Event12th International Symposium on Spatial Data Handling, SDH 2006 - Vienna, Austria
Duration: 12 Jul 200614 Jul 2006

Conference

Conference12th International Symposium on Spatial Data Handling, SDH 2006
Country/TerritoryAustria
CityVienna
Period12/07/0614/07/06

Keywords

  • Cloud model
  • Error
  • Fuzziness
  • Uncertainty

ASJC Scopus subject areas

  • Software

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