The conservation of Natural Capital emerges as a critical challenge for future generations, requiring the integration of nature and technology for effective monitoring and management. Leveraging tools like IT sensors, software, and AI, particularly evident in Agriculture 4.0, has yielded promising outcomes across various fields such as environmental conservation. However, to harness the full potential of the data collected, efficient presentation and comprehension mechanisms are essential, considering diverse stakeholders, especially end-users like scientists and policymakers. This necessitates the adoption of data visualisation and visual data analysis techniques, incorporating user participation through methods like personalisation and co-design. The exploration of data visualisation methods in the environmental sector, particularly within Italy's context where digitalisation in protected areas is low, is crucial. With upcoming investments in digitalisation and environmental sectors through strategic plans like PNRR, understanding how new technologies will be implemented becomes pivotal. Consequently, this research seeks to present and analyse optimal data visualisation methods in digital tools, aiming to enhance decision-makers comprehension and utilisation of data for strategic conservation decisions. The study proposes a novel approach to the design process, paving the way for further advancements in this domain.

The Importance of Data Visualisation in Natural Capital Conservation Scenario. Enhancing Decision-making Processes Through User Analysis, Personalisation and Co-design / Ucchiello, MATTIA FRANCESCO. - (2024), pp. 162-169. (Intervento presentato al convegno 8th International Visual Methods Conference tenutosi a Rome; Italy) [10.21467/proceedings.168.18].

The Importance of Data Visualisation in Natural Capital Conservation Scenario. Enhancing Decision-making Processes Through User Analysis, Personalisation and Co-design

Mattia Francesco Ucchiello
2024

Abstract

The conservation of Natural Capital emerges as a critical challenge for future generations, requiring the integration of nature and technology for effective monitoring and management. Leveraging tools like IT sensors, software, and AI, particularly evident in Agriculture 4.0, has yielded promising outcomes across various fields such as environmental conservation. However, to harness the full potential of the data collected, efficient presentation and comprehension mechanisms are essential, considering diverse stakeholders, especially end-users like scientists and policymakers. This necessitates the adoption of data visualisation and visual data analysis techniques, incorporating user participation through methods like personalisation and co-design. The exploration of data visualisation methods in the environmental sector, particularly within Italy's context where digitalisation in protected areas is low, is crucial. With upcoming investments in digitalisation and environmental sectors through strategic plans like PNRR, understanding how new technologies will be implemented becomes pivotal. Consequently, this research seeks to present and analyse optimal data visualisation methods in digital tools, aiming to enhance decision-makers comprehension and utilisation of data for strategic conservation decisions. The study proposes a novel approach to the design process, paving the way for further advancements in this domain.
2024
8th International Visual Methods Conference
decision-support-system; participatory design; natural capital conservation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
The Importance of Data Visualisation in Natural Capital Conservation Scenario. Enhancing Decision-making Processes Through User Analysis, Personalisation and Co-design / Ucchiello, MATTIA FRANCESCO. - (2024), pp. 162-169. (Intervento presentato al convegno 8th International Visual Methods Conference tenutosi a Rome; Italy) [10.21467/proceedings.168.18].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1726557
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