2002
DOI: 10.1016/s0020-0255(02)00163-9
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A GIS application to enhance cell-based information modeling

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Cited by 19 publications
(2 citation statements)
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“…By using fuzzy logic in GIS, aspects of ambiguity in linguistic variables can be modeled (Benedikt et al 2002). The fuzzy inference system can be developed in four stages: (1) the definition of linguistic variables, (2) the selection of appropriate membership functions, (3) the definition of rules based on knowledge of the system and (4) the selection of a suitable operator to combine fuzzy sets.…”
Section: Multi-criteria Decision Analysis Methodsmentioning
confidence: 99%
“…By using fuzzy logic in GIS, aspects of ambiguity in linguistic variables can be modeled (Benedikt et al 2002). The fuzzy inference system can be developed in four stages: (1) the definition of linguistic variables, (2) the selection of appropriate membership functions, (3) the definition of rules based on knowledge of the system and (4) the selection of a suitable operator to combine fuzzy sets.…”
Section: Multi-criteria Decision Analysis Methodsmentioning
confidence: 99%
“…GIS in general are considered to precisely represent points, areas and lines on earth. Uncertainties (due to vague as well as ambiguous data), however, remain inherent when using linguistic data as representation of spatial knowledge (Benedikt, Reinberg & Riedl, 2002, 2004Kratochwil & Benedikt, 2005). These uncertainties can be addressed with weighting algorithms; we intend to experiment with spatial weights using ordered weighted averaging operators in ensuing research.…”
Section: Outlook and Conclusionmentioning
confidence: 99%