Crowdsourcing indicators for cultural ecosystem services : a geographically weighted approach for mountain landscapes
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Integrating cultural dimensions into the ecosystem service framework is essential for appraising non-material benefits stemming from different human-environment interactions. This study investigates how the actual provision of cultural services is distributed across the landscape according to spatially varying relationships. The final aim was to analyse how landscape settings are associated to people’s preferences and perceptions related to cultural ecosystem services in mountain landscapes. We demonstrated a spatially explicit method based on geo-tagged images from popular social media to assess revealed preferences. A spatially weighted regression showed that specific variables correspond to prominent drivers of cultural ecosystem services at the local scale. The results of this explanatory approach can be used to integrate the cultural service dimension into land planning by taking into account specific benefiting areas and by setting priorities on the ecosystems and landscape characteristics which affect the service supply. We finally concluded that the use of crowdsourced data allows identifying spatial patterns of cultural ecosystem service preferences and their association with landscape settings.
Tenerelli , P , Demsar , U & Luque , S 2016 , ' Crowdsourcing indicators for cultural ecosystem services : a geographically weighted approach for mountain landscapes ' , Ecological Indicators , vol. 64 , pp. 237–248 . https://doi.org/10.1016/j.ecolind.2015.12.042
Copyright © 2016 Elsevier Ltd. All rights reserved. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://dx.doi.org/10.1016/j.ecolind.2015.12.042
DescriptionThis study was partially supported by the OpenNESS project funded from the European Union's Seventh Programme for research; technological development and demonstration under grant agreement n° 308428.
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