User-centered visual explorer of in-process comparison in spatiotemporal space

Abstract

We propose a user-centered visual explorer (UcVE) for progressive comparing multiple visualization units in spatiotemporal space. We create unique unit visualization with the customizable aggregated view based on the visual metaphor of flower bursts. Each visualization unit is encoded with the abstraction of spatiotemporal properties. To reduce user cognition load, UcVE allows users to visualize, save, and track in-the-process exploration results. In coordination of storage sequence and block tracking views, UcVE can facilitate comparison with multiple visualization units concurrently, selected from historical and current exploration results. UcVE offers a flexible geo-based layout, with aggregation functions and temporal views of the timeline with categorized events, to maximize the user’s exploration capabilities. Finally, we demonstrate the usefulness by using COVID-19 datasets, case studies with different user scenarios, and expert feedback. Graphical abstract

ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty

“…Stepping forward to 2022, an increasing number of visual representations began to appear that were based on a more thorough understanding of the pandemic. Zhao et al, for example, introduced six main data visualizations that are frequently collaborated as public platforms in China during the COVID-19 pandemic [69], while Dong et al [18] presented UcVE as a progressive spatiotemporal comparison of the COVID-19 situation in multiple locations. Additionally, Phyloview [33], a visual analytics system, examines the spatiotemporal evolution of COVID-19 over time, while EpiMob [26], a visual interface for city-wide human mobility restrictions to control epidemics, simulates changes in human mobility and the infection states that may result from implementing a particular restriction policy (or combination of policies).…”

“…3) The 2021 Census dataset, which contains locationbased demographic information gathered from the most recent census 4 ; the unstructured data pertain to government interventions. These data were crawled and then processed using a natural language processing (NLP) model as part of prior research [18].…”

“…User-centered design is being incorporated into an increasing number of visual analytics systems. In ClinicLens, we employ a user-centered design that is similar to our previous design in UcVE [18] to support the ability to save the results of users' explorations for later recall. The Storage View receives the save command and further compresses and lists the LGAs that Map-Lens View had selected for sequential allocation, according to the exploration results on Map-Lens View.…”

“…This is a need that has been listed in a systematic survey as a valuable future direction of research to prevent the spread of disease [6]. In these situations, despite the introduction of adequate visual analytics methods [18], [26], [34] and AI4VIS [25], [27], [32] with the goal of providing interactions and artificial intelligence advantages to deal with data correlations and other complexities, dynamic and multi-aspect visual analyses of COVID-19 remain insufficient.…”

Interactive visual query of density maps on latent space via flow‐based models

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Spatiotemporal data are spatial data that change over time. By analyzing spatiotemporal data, people can uncover patterns related to issues such as the spread of air pollution (Deng et al, 2020), traffic congestion (Shi et al, 2021Wang et al, 2013Wang et al, , 2014, and the dynamics of disease outbreaks (Yu et al, 2022). The exponential growth of spatiotemporal data presents a challenge for people to efficiently explore it.

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