A Comparison of Spatiotemporal Visualizations for 3D Urban Analytics

Abstract

Recent technological innovations have led to an increase in the availability of 3D urban data, such as shadow, noise, solar potential, and earthquake simulations. These spatiotemporal datasets create opportunities for new visualizations to engage experts from different domains to study the dynamic behavior of urban spaces in this under explored dimension. However, designing 3D spatiotemporal urban visualizations is challenging, as it requires visual strategies to support analysis of time-varying data referent to the city geometry. Although different visual strategies have been used in 3D urban visual analytics, the question of how effective these visual designs are at supporting spatiotemporal analysis on building surfaces remains open. To investigate this, in this paper we first contribute a series of analytical tasks elicited after interviews with practitioners from three urban domains. We also contribute a quantitative user study comparing the effectiveness of four representative visual designs used to visualize 3D spatiotemporal urban data: spatial juxtaposition, temporal juxtaposition, linked view, and embedded view. Participants performed a series of tasks that required them to identify extreme values on building surfaces over time. Tasks varied in granularity for both space and time dimensions. Our results demonstrate that participants were more accurate using plot-based visualizations (linked view, embedded view) but faster using color-coded visualizations (spatial juxtaposition, temporal juxtaposition). Our results also show that, with increasing task complexity, plot-based visualizations perform better in preserving efficiency (time, accuracy) compared to color-coded visualizations. Based on our findings, we present a set of takeaways with design recommendations for 3D spatiotemporal urban visualizations for researchers and practitioners.

Accepted at IEEE VIS 2022. Video: https://youtu.be/3TBx-njP_sU

References in corpus (2)

• Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows Over Time
• Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data

Cited by in corpus (7)

• The State of the Art in Visual Analytics for 3D Urban Data
• The Urban Toolkit: A Grammar-based Framework for Urban Visual Analytics
• Reimagining TaxiVis through an Immersive Space-Time Cube metaphor and reflecting on potential benefits of Immersive Analytics for urban data exploration
• Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics
• STRive: An association rule-based system for the exploration of spatiotemporal categorical data
• DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research
• Understanding the Impact of Referent Design on Scale Perception in Immersive Data Visualization