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UAL Research Online

CEDAR: Collective Environmental Data Sensing using AR

Liu, Mengci (2025) CEDAR: Collective Environmental Data Sensing using AR. PhD thesis, University of the Arts London.

Type of Research: Thesis
Creators: Liu, Mengci
Description:

CEDAR (Collective Environmental Data Sensing using AR) investigates how participatory data visualisation, supported by Augmented Reality (AR), can enhance citizen science by fostering experiential, aesthetic, and situated engagement with environmental data. Situated at the intersection of citizen science, data visualization, and AR, CEDAR is grounded in the theoretical frameworks of pragmatist aesthetics, new materialism, and technoecology, which together provide the critical lens to reconceptualize data as an expressive, relational medium embedded within everyday urban and natural environments.

The project develops and tests sensor-driven AR applications that invite citizens to actively collect, interpret, and interact with environmental data through embodied, multisensory experiences. CEDAR emphasises generating new sensations and environmental practices that deepen
awareness, shift perceptions, and encourage responsible environmental behaviours. Through cocreated participatory sensing activities and innovative data visualisation methods, the project explores how AR can mediate complex human, non-human, and technological entanglements to
create more meaningful public engagement.

CEDAR contributes a novel conceptual framework for participatory data visualisation that integrates aesthetic, emotional, and spatial dimensions, advancing theoretical and practical understandings of how data experiences evolve into environmental participation. This interdisciplinary approach offers valuable insights into leveraging emerging technologies to foster informed, connected, and proactive environmental citizenship in the face of global ecological
challenges.

Your affiliations with UAL: Research Centres/Networks > Institute for Creative Computing
Date: October 2025
Date Deposited: 17 Jul 2026 15:07
Last Modified: 17 Jul 2026 15:07
Item ID: 27365
URI: https://ualresearchonline.arts.ac.uk/id/eprint/27365
Licences:
CEDAR: Collective Environmental Data Sensing using AR : Creative Commons Attribution Non-commercial No Derivatives
Final Thesis Submission Form : Creative Commons Attribution Non-commercial No Derivatives

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