Measuring the attractiveness of Dutch landscapes: Identifying national hotspots of highly valued places using Google Maps |
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| Institution: | 1. Wageningen University and Research Centre, Alterra/Cultural Geography, P.O. Box 47, 6700 AA Wageningen, The Netherlands;2. Wageningen University and Research Centre, Forest and Nature Policy/Alterra, P.O. Box 47, 6700 AA Wageningen, The Netherlands;3. Wageningen University and Research Centre, Alterra, P.O. Box 47, 6700 AA Wageningen, The Netherlands;4. Netherlands Environmental Assessment Agency, P.O. Box 303, NL-3720 AH Bilthoven, The Netherlands;5. University of Groningen, Faculty of Spatial Sciences, P.O. Box 800, 9700 AV Groningen, The Netherlands;1. Norensbergsgatan 94, SE-702 15 Örebro, Sweden;2. Gustaf Kjellbergs väg 32, SE-756 43 Uppsala, Sweden;1. ETH Zurich, Future Cities Laboratory, Singapore-ETH Centre, Singapore;2. Architecture and Sustainable Design, Singapore University of Technology and Design, Singapore;1. CENSE – Center for Environmental and Sustainability Research, NOVA School of Science and Technology, NOVA University Lisbon, Campus da Caparica, 2829-516 Caparica, Portugal;2. Department of Mathematics and Center for Mathematics and Applications, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal;3. Forest Research Centre (CEF), Instituto Superior de Agronomia, Universidade de Lisboa, Portugal;4. Centro de Estatística e Aplicações (CEAUL), Faculdade de Ciências da Universidade de Lisboa, Portugal;1. School of Geography, Planning, and Environmental Management, University of Queensland, Brisbane, QLD 4072, Australia;2. Barbara Hardy Institute, School of NBE, University of South Australia, Mawson Lakes, SA 5085, Australia;3. School of Botany, The University of Melbourne, Parkville, VIC 3010, Australia;4. Parks Victoria, 535 Bourke St, Melbourne, VIC 3000, Australia;1. KU Leuven, Division of Bioeconomics, Department of Earth & Environmental Sciences, Geo-Instituut, Celestijnenlaan 200E, B-3001 Leuven, Belgium;2. Flemish Institute for Technological Research VITO, Boeretang, B-2400 Mol, Belgium;3. KU Leuven, Division of Forest, Nature & Landscape, Department of Earth & Environmental Sciences, Geo-Instituut, Celestijnenlaan 200E, B-3001 Leuven, Belgium;1. Environmental Systems Analysis Group, Wageningen University, PO Box 47, 6700 AA Wageningen, the Netherlands;2. National Accounts Department, Statistics Netherlands, Henri Faasdreef 312, 2492 JP The Hague, the Netherlands;3. Laboratory of Geo-information Science and Remote Sensing, Wageningen University, PO Box 47, 6700 AA Wageningen, the Netherlands |
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| Abstract: | In a Cost-Benefit Analysis (CBA) or an Environmental Impact Assessment (EIA), determining the value that the general public attaches to a landscape is often problematic. To aid the inclusion of this social value in such analyses, a Google Maps-based tool, called the HotSpotMonitor (HSM), was developed. The HSM determines which natural places are highly attractive by having people mark such places on a map. The definition of attractiveness remains open to avoid having marker placement being influenced by preconceived thoughts. The number of markers an area receives is considered to indicate its social value. Six regions were selected, and from these, stratified samples were drawn (total n = 3293). Participants placed markers at three spatial levels: local, regional and national. This paper focuses on the markers at the national level. The first research question is whether the HSM can produce an accurate map of highly attractive places at a national level. The results indicated that while in principle HSM can produce such a map, the spatial representativeness of the sample is important. The region of origin of the participants influenced where they placed their markers, an effect previously termed spatial discounting. The second research question considers which qualities the participants associate with the marked places. These qualities were very similar at all three spatial levels: green, natural, presence of water and quiet were often selected out of the fourteen suggested qualities. The third, and more exploratory, research question concerns which characteristics of an area predict its attractiveness. Natural and forest areas had higher marker densities than water surfaces or all other types of land use combined. The discussion evaluates the potential of the HSM to generate input on social landscape values for CBAs and EIAs. |
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| Keywords: | Google Maps Landscape Natural places Social values Attractiveness Spatial discounting |
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