Characterizing Spatial Uncertainty when Integrating Social Data in Conservation Planning

Lechner, A. M., Raymond, C. M., Adams, V. M. , Polyakov, M., Gordon, A., Rhodes, J. R., Mills, M., Stein, A., Ives, C. D. and Lefroy, E. C. (2014) Characterizing Spatial Uncertainty when Integrating Social Data in Conservation Planning. Conservation Biology, 28 (6). pp. 1497-1511. ISSN 0888-8892

Full content URL: https://doi.org/10.1111/cobi.12409

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Item Type:Article
Item Status:Live Archive

Abstract

Recent conservation planning studies have presented approaches for integrating spatially referenced social (SRS) data with a view to improving the feasibility of conservation action. We reviewed the growing conservation literature on SRS data, focusing on elicited or stated preferences derived through social survey methods such as choice experiments and public participation geographic information systems. Elicited SRS data includes the spatial distribution of willingness to sell, willingness to pay, willingness to act, and assessments of social and cultural values. We developed a typology for assessing elicited SRS data uncertainty which describes how social survey uncertainty propagates when projected spatially and the importance of accounting for spatial uncertainty such as scale effects and data quality. These uncertainties will propagate when elicited SRS data is integrated with biophysical data for conservation planning and may have important consequences for assessing the feasibility of conservation actions. To explore this issue further, we conducted a systematic review of the elicited SRS data literature. We found that social survey uncertainty was commonly tested for, but that these uncertainties were ignored when projected spatially. Based on these results we developed a framework which will help researchers and practitioners estimate social survey uncertainty and use these quantitative estimates to systematically address uncertainty within an analysis. This is important when using SRS data in conservation applications because decisions need to be made irrespective of data quality and well characterized uncertainty can be incorporated into decision theoretic approaches.

Keywords:conservation opportunity, conservation planning, elicited values, public participation gis, research, social, spatial data quality, spatial uncertainty, systematic conservation assessment
Subjects:F Physical Sciences > F810 Environmental Geography
Divisions:College of Science > School of Geography
ID Code:42037
Deposited On:14 Oct 2020 14:24

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