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Currently used methods for representing geographical information are inadequate because they do not tolerate imprecision. This leads to information loss and inaccuracy in analysis. Such expressive inadequacy is largely due to the underlying membership concept of classical set theory. To improve information processing in GIS research and application, an alternative membership concept is required. In this paper, we explore the inadequacy imposed upon geographical information representation by classical set theory and address the problems of information loss. A fuzzy relational data model is defined which is more representative for geographical information. A GIS database for agricultural land resource management is created by using the data model and a new technique for assessing land suitability is developed. The fuzzy representation largely facilitates data analysis in this GIS. The methods are tested with data from North Java, Indonesia using a vector-based GIS software package, Arc Info, and the analysis results are presented.
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Stored only when both papers are in the corpus — 150 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.