Print Email Facebook Twitter Methodology for participatory GIS risk mapping and Citizen Science for Solotvyno Salt Mines Title Methodology for participatory GIS risk mapping and Citizen Science for Solotvyno Salt Mines Author Onencan, A.M. (TU Delft Policy Analysis) Meesters, Kenny (TU Delft Policy Analysis) van de Walle, B.A. (TU Delft Multi Actor Systems) Department Multi Actor Systems Date 2018 Abstract The Horizon 2020 interim evaluation (2017) indicates a steep increase in citizen engagement in European Union Citizen Science (CS) projects, with less than 1% in budgetary terms and minimal influence. Research findings attribute weak CS influence to the restriction of citizen actions to data collection, with minimal or no engagement in co-design, co-creation, data analysis, and elucidation of results. We design a participatory GIS and CS methodology aimed at engaging the citizens in the entire Earth Observation (EO) project cycle. The methodology also seeks to address previous CS project challenges related to data quality, data interoperability, citizen-motivation, and participation. We draw the high-level requirements from the SENDAI framework of action and the three pillars of active citizen engagement, as enshrined in Principle 10 of the Rio Declaration and the Aarhus Convention. The primary input of the methodology is the Haklay (2018) approach for participatory mapping and CS, and the Reed (2009) stakeholder analysis framework. The proposed methodology comprises of three main parts: system analysis, stakeholder analysis, and a six-step methodology. We designed the six-step methodology using an iterative and flexible approach, to take account of unforeseen changes. Future research will focus on implementing the methodology and evaluating its effectiveness in the Solotvyno Saltmine case study in Ukraine. Subject Citizen's scienceCommunity engagementDisaster Risk Reduction (DRR)Geographical information systems (GIS)Risk mappingSENDAI framework To reference this document use: http://resolver.tudelft.nl/uuid:85953a57-4917-402b-9744-62928d6bdcce DOI https://doi.org/10.3390/rs10111828 ISSN 2072-4292 Source Remote Sensing, 10 (11) Part of collection Institutional Repository Document type journal article Rights © 2018 A.M. Onencan, Kenny Meesters, B.A. van de Walle Files PDF remotesensing_10_01828.pdf 5.98 MB Close viewer /islandora/object/uuid:85953a57-4917-402b-9744-62928d6bdcce/datastream/OBJ/view