Harnessing Visualization for Climate Action and Sustainable Future
October 22, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Narges Mahyar
arXiv ID
2410.17411
Category
cs.HC: Human-Computer Interaction
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The urgency of climate change is now recognized globally. As humanity confronts the critical need to mitigate climate change and foster sustainability, data visualization emerges as a powerful tool with a unique capacity to communicate insights crucial for understanding environmental complexities. This paper explores the critical need for designing and investigating responsible data visualization that can act as a catalyst for engaging communities within global climate action and sustainability efforts. Grounded in prior work and reflecting on a decade of community engagement research, I propose five critical considerations: (1) inclusive and accessible visualizations for enhancing climate education and communication, (2) interactive visualizations for fostering agency and deepening engagement, (3) in-situ visualizations for reducing spatial indirection, (4) shared immersive experiences for catalyzing collective action, and (5) accurate, transparent, and credible visualizations for ensuring trust and integrity. These considerations offer strategies and new directions for visualization research, aiming to enhance community engagement, deepen involvement, and foster collective action on critical socio-technical including and beyond climate change.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted