دانلود رایگان مقاله مشخصه های تنوع چشم انداز بواسطه فولکسونومی فضایی

Abstract

Describing current, past and future landscapes for inventory and policy making purposes requires classifications capturing variation in, for example, land use and land cover. Typical land cover classifications for such purposes result from a top-down process and rely on expert conceptualisations, and thus provide limited space for incorporating more widely held views of key landscape elements. In this paper we introduce the notion of spatial folksonomies, which we define as a tuple linking a vocabulary of landscape terms through authors and resources to locations. We demonstrate how spatial folksonomies can automatically be created for Switzerland using two text corpora: the Swiss Alpine Club's yearbook for the past 150 years and user generated content from a website describing a wide range of outdoor activities. The spatial folksonomies capture variation in space of the use of nouns describing 96 natural landscape terms (e.g. ridge, forest, mountain, etc.) and allow us to characterise regions and compute similarities. We compare our spatial folksonomies to two traditional land cover/land use classifications (CORINE and Arealstatistik) and demonstrate that despite their very different sources, the approaches capture landscape variation in broadly similar ways. However, our spatial folksonomies provide new insights into how landscapes are described, through for example variation in space, time and through the prism of different activities. We argue that our spatial folksonomies are a novel way of capturing variation closer to the bottom-up understandings of landscape for instance required to describe cultural ecosystem services.

6. Conclusion

and outlook We opened this paper arguing for a need to develop bottom-up approaches to describing landscapes, land cover and land use. Such approaches have the potential to better capture local variation in the ways in which landscapes are described, and thus also potentially better meet local needs, while dealing with the challenge of ontological mismatches between seemingly transparent terms such as forest (Comber et al., 2005). Our approach to meeting this challenge was to develop what we termed spatial folksonomies for Switzerland using two, thematically similar, but quite different textual corpora. We argued that such corpora contain very rich information, in our case allowing us to build spatial folksonomies containing natural feature terms at a resolution of 10 km. Our approach is a novel one, using full text corpora as a starting point to generate rich, spatially referenced, landscape descriptions. Although we have only scratched the surface of the potential of exploring such methods, we believe our approach has a number of important implications which are demonstrated in this paper. Firstly, the state of the art in methods from Geographic Information Retrieval is now such that, subject to availability of suitable corpora and methods for identifying relevant terms, it is possible to generate meaningful spatial folksonomies. Using diverse, rich textual corpora we captured, at a relatively coarse granularity, variation in descriptions of (mountain) landscapes through natural features in Switzerland. Although the nature of the terms describing regions vary according to individual corpora, descriptions created using the same lists of natural features correlate in space. Thus, our approach can be used to identify similar regions using standard methods for comparing documents.