This poster was presented at Computer Graphics & Visual Computing (CGVC24) at City St George's, University of London.
Dany Laksono, Radu Jianu, Aidan Slingsby
We explore effective ways of visualising multivariate spatio-temporal data in the context of understanding energy decarbonisation planning. Local Area Energy Plans (LAEPs) are designed to provide holistic, coherent paths towards achieving net-zero emissions for local areas in the United Kingdom. They are meant to integrate a broad range of technical, social, and economic interventions (e.g., deployment of renewable technologies, reduction of fuel poverty, upgrades in the infrastructure of energy transport) over an extended time frame. Such plans are complex and understanding how they unfold across space and over time is difficult. Traditional geo-spatial visualisation methods, such as layered choropleth maps, face limitations in effectively conveying multifaceted data, especially when it varies over time. We instead explore gridded-glyphmaps. We show how we can depict decarbonisation outcomes and costs for different types of interventions across different areas as small time-series glyphs. We designed and validated our approaches through iterative contextual interviews with stakeholders in the energy domain.