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Characterising landscape variation through spatial folksonomies - Zurich Open Repository and Archive


Derungs, Curdin; Purves, Ross S (2016). Characterising landscape variation through spatial folksonomies. Applied Geography, 75:60-70.

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 classifi- cations 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 ac- tivities. 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 vari- ation closer to the bottom-up understandings of landscape for instance required to describe cultural ecosystem services.

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 classifi- cations 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 ac- tivities. 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 vari- ation closer to the bottom-up understandings of landscape for instance required to describe cultural ecosystem services.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
08 University Research Priority Programs > Language and Space
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2016
Deposited On:13 Sep 2016 14:04
Last Modified:19 Oct 2016 07:06
Publisher:Elsevier
ISSN:0143-6228
Publisher DOI:https://doi.org/10.1016/j.apgeog.2016.08.005

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Licence: Creative Commons: Attribution-No Derivatives 4.0 International (CC BY-ND 4.0)

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