CEDAR: Collective Environmental Data Sensing using AR

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.

Author dc:creator

• Liu, Mengci

• Human-computer Interaction

Statement dc:rights

• cc_by_nc_nd

Language dc:language

en

Identifier

Liu, Mengci <https://ualresearchonline.arts.ac.uk/view/creators/Liu=3AMengci=3A=3A.html> (2025) CEDAR: Collective Environmental Data Sensing using AR. PhD thesis, University of the Arts London.

OAI identifier oai:identifier

oai:ualresearchonline.arts.ac.uk:27365