Membership Functions for Spatial Proximity

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Conference Proceeding
AI 2006: Advances in Artificial Intelligence, 2006, pp. 942 - 949
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Formalising nearness has been the subject of extensive work, resulting in many membership functions based on absolute distance metrics, relative distance metrics, and combinations of those. The possible strengths and weaknesses of these functions have been discussed and argued at length, but strangely enough, no experiment seems to have been conducted to assess the merits and shortcomings of competing approaches. Conducting such experiments can be expected not only to provide an objective evaluation of the various measures that have been proposed, but also to suggest new measures that outperform all those being analysed. This paper fulfills these expectations, and gives further evidence that fuzzy logic provides fruitful and powerful methods to formalise qualitative reasoning and capture fundamental qualitative notions. The proposed fuzzy membership functions can be directly used in qualitative reasoning about spatial proximity in Geographic Information Systems, which are becoming more and more important in software development for diverse purposes such as Tourist Information Systems or property development.
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