This paper presents the results of the first investigation into Kurdish linguistic varieties and their spatial distribution. Kurdish dialects are used across five nation states in the Middle East and only one dialect, Sorani, has official status in one of these nation states. The study employs the “draw-a-map” task established in Perceptual Dialectology; the analysis is supported by Geographical Information Systems (GIS). The results show that, despite the geolinguistic and geopolitical situation, Kurdish respondents have good knowledge of the main varieties of their language (Kurmanji, Sorani, and the related variety Zazaki) and where to localize them. Awareness of the more diverse Southern Kurdish varieties is less definitive. This indicates that the Kurdish language plays a role in identity formation, but also that smaller isolated varieties are not only endangered in terms of speakers, but also in terms of their representations in Kurds’ mental maps of the linguistic landscape they live in.
Satellite images increasingly become a major data source for monitoring changes in the natural environment. A main task in the analysis of satellite images is concerned with the modelling of land use classes by reducing uncertainty during a classification process. In the approach presented in this paper uncertainty is perceived to be due to the vagueness of geographical categories caused by either the complexity of the category (like 'urban area') or by the use of the category in several application contexts. Two circumstances of use of an extended set theoretical concept (fuzzy sets) are discussed. First, two algorithms from the ISODATA class are used to determine the grades of membership to three a priori defined classes (woodland, suburban area, urban area) of a LANDSAT TM satellite image of Vienna, Austria. The results are visualized by a RGB image of the grades of membership to each category. Second, a measure of fuzziness is employed on the results. The measure relies on the concept of distance between a seUcategory and its complement. The so determined vagueness provide more information on the uncertainty of the different categories and may improve further information processing tasks.
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