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Embracing Uncertainty in Participatory GIS: Perceptions of tree planting in the English Lake District: Perceptions of tree planting in the English Lake District

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Bibliographic Details
Title: Embracing Uncertainty in Participatory GIS: Perceptions of tree planting in the English Lake District: Perceptions of tree planting in the English Lake District
Authors: Huck, Jonny, Denwood, Timna, Taylor, Joanna
Source: Huck, J, Denwood, T & Taylor, J 2025, 'Embracing Uncertainty in Participatory GIS: Perceptions of tree planting in the English Lake District', Paper presented at 33rd GISRUK Conference 2025, 23/04/25-25/04/25 pp. 1-6. https://doi.org/10.5281/zenodo.15309827
Publisher Information: Zenodo, 2025.
Publication Year: 2025
Subject Terms: PPGIS, Map-Me, Dempster Shafer, PGIS, Evidence, Dempster Shafer, Evidence, PGIS, PPGIS, Map-Me
Description: One of the most significant challenges in Participatory GIS (PGIS) is ensuring that results are incorporated into decision-making processes. A key issue is the lack of a means to quantify the findings of PGIS surveys, which typically contain interdependent, contradictory and conflicting information with varying degrees of participant confidence; resulting in high levels of uncertainty that are not adequately represented in conventional methods. This research describes a novel extension to Dempster-Shafer theory that permits PGIS datasets to be quantified in a manner that adequately incorporates these uncertainties, enabling their meaningful adoption into analysis and decision- making.
Document Type: Article
Conference object
File Description: application/pdf
Language: English
DOI: 10.5281/zenodo.15309827
DOI: 10.5281/zenodo.15309828
Rights: CC BY
CC BY NC
Accession Number: edsair.doi.dedup.....41e368a61bed5df19b6bea242f37093f
Database: OpenAIRE
Description
Abstract:One of the most significant challenges in Participatory GIS (PGIS) is ensuring that results are incorporated into decision-making processes. A key issue is the lack of a means to quantify the findings of PGIS surveys, which typically contain interdependent, contradictory and conflicting information with varying degrees of participant confidence; resulting in high levels of uncertainty that are not adequately represented in conventional methods. This research describes a novel extension to Dempster-Shafer theory that permits PGIS datasets to be quantified in a manner that adequately incorporates these uncertainties, enabling their meaningful adoption into analysis and decision- making.
DOI:10.5281/zenodo.15309827