Urban Space Explorer: A Visual Analytics System for Urban Planning

A Comparison of Spatiotemporal Visualizations for 3D Urban Analytics

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. Lastly, we report on a series of interviews with four practitioners, and their feedback and suggestions for further work on the visualizations to support 3D spatiotemporal urban data analysis.

Mapping the Hazard: Visual Analysis of Flood Impact on Urban Mobility

With the growing influence of climate change, the intensity of flood disasters has significantly increased throughout the world over the past decades. Among the various types of hazards caused by floods, disruption of the road network has a particularly severe impact on the mobility of emergency responders, and therefore, poses a difficult challenge to damage mitigation, especially in the urban environment. The aim of this article is to present a mapping model for analyzing the spatial pattern of flood impact on urban mobility. Specifically, by incorporating the theory of space syntax, this model focuses on two dimensions of the analysis: the performance of the road network and the relationship between the factors behind it. The former can demonstrate the extent to which the city is affected by flooding in terms of mobility, whereas the latter can provide valuable reference for enhancing the efficiency of evacuation and rescue operations.

VitalVizor: A Visual Analytics System for Studying Urban Vitality

Creating lively places with high urban vitality is an ultimate goal for urban planning and design. The VitalVizor visual analytics system employs well-established visualization and interaction techniques to facilitate user exploration of spatial physical entities and non-spatial urban design metrics when studying urban vitality.