40-Issue 3
Permanent URI for this collection
Browse
Browsing 40-Issue 3 by Author "Chen, Siming"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item A Deeper Understanding of Visualization-Text Interplay in Geographic Data-driven Stories(The Eurographics Association and John Wiley & Sons Ltd., 2021) Latif, Shahid; Chen, Siming; Beck, Fabian; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonData-driven stories comprise of visualizations and a textual narrative. The two representations coexist and complement each other. Although existing research has explored the design strategies and structure of such stories, it remains an open research question how the two representations play together on a detailed level and how they are linked with each other. In this paper, we aim at understanding the fine-grained interplay of text and visualizations in geographic data-driven stories. We focus on geographic content as it often includes complex spatiotemporal data presented as versatile visualizations and rich textual descriptions. We conduct a qualitative empirical study on 22 stories collected from a variety of news media outlets; 10 of the stories report the COVID-19 pandemic, the others cover diverse topics. We investigate the role of every sentence and visualization within the narrative to reveal how they reference each other and interact. Moreover, we explore the positioning and sequence of various parts of the narrative to find patterns that further consolidate the stories. Drawing from the findings, we discuss study implications with respect to best practices and possibilities to automate the report generation.Item Exploring Multi-dimensional Data via Subset Embedding(The Eurographics Association and John Wiley & Sons Ltd., 2021) Xie, Peng; Tao, Wenyuan; Li, Jie; Huang, Wentao; Chen, Siming; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonMulti-dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformlyformatted embeddings. We implement the SEN as multiple subnets with separate loss functions. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features, thus achieving accurate pattern exploration, which in most cases is searching for subsets having similar values on few features. Moreover, each subnet is a fully-connected neural network with one hidden layer. The simple structure brings high training efficiency. We integrate the SEN into a visualization system that achieves a 3-step workflow. Specifically, analysts (1) partition the given dataset into subsets, (2) select portions in a projected latent space created using the SEN, and (3) determine the existence of patterns within selected subsets. Generally, the system combines visualizations, interactions, automatic methods, and quantitative measures to balance the exploration flexibility and operation efficiency, and improve the interpretability and faithfulness of the identified patterns. Case studies and quantitative experiments on multiple open datasets demonstrate the general applicability and effectiveness of our approach.