From perceived values to shared values: a Multi-Stakeholder Spatial Decision Analysis (M-SSDA) for resilient landscapes

M. Cerreta, Simona Panaro

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    Abstract

    This paper puts forth a Multi-Stakeholder Spatial Decision Analysis (M-SSDA) which combines Multi-Stakeholders Decision Analysis (M-SDA) and GIS processing based on a collaborative, hybrid and adaptive evaluative approach to support the elaboration of enhancement strategies designed for resilient landscapes. This methodology has been tested in the research project “Cilento Labscape: An integrated model for the activation of a Living Lab in the National Park of Cilento, Vallo di Diano and Alburni”, which was conducted in the Cilento National Park (Southern Italy). The aim of the methodology is to find alternative touristic routes for the promotion of the Park. The different steps in the process will be described, as will the employment of multiple tools/techniques (interviews, GIS tools, Semantic Analysis, and Geo-Statistical Analysis) to improve the reliability of the decision-making process. The most relevant results of the methodology will also be outlined in terms of the transition from the identification of the perceived landscape’s values to the acknowledgement of the shared values, and their consequent employment for the outlining of new thematic itineraries for the Park. To realise sustainable territorial strategies and preserve the landscape through bottom-up decision-making processes, the different local communities need to gain a new awareness of their identity shared values and make an active contribution towards promoting and managing their site-specific resources.
    Original languageEnglish
    Article number1113
    JournalSustainability
    Volume9
    Issue number7
    DOIs
    Publication statusPublished - 26 Jun 2017

    Keywords

    • landscape assessment
    • perceived values
    • shared values
    • subjective indicators
    • Multi-Stakeholder Decision Analysis (M-SDA)
    • GIS
    • Semantic Analysis
    • Geo-Statistical Analysis

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