Shahid Latif

Data‐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.

Building upon the ideas of storytelling and explorable explanations, we introduce Talking Realities, a concept for producing data-driven interactive narratives in virtual reality. It combines an audio narrative with an immersive visualization to communicate analysis results. The narrative is automatically produced using template-based natural language generation and adapts to data and user interactions. The synchronized animation of visual elements in accordance with the audio connects the two representations. In addition, we discuss various modes of explanation ranging from fully guided tours to free exploration of the data. We demonstrate the applicability of our concept by developing a virtual reality visualization for air traffic data. Furthermore, generalizability is exhibited by sketching mock-ups for two more application scenarios in the context of information and scientific visualization.

We conducted a questionnaire study aimed towards PhD students in the field of visualization research to understand how they cope with paper rejections. We collected responses from 24 participants and performed a qualitative analysis of the data in relation to the provided support by collaborators, resubmission strategies, handling multiple rejects, and personal impression of the reviews. The results indicate that the PhD students in the visualization community generally cope well with the negative reviews and, with experience, learn how to act accordingly to improve and resubmit their work. Our results reveal the main coping strategies that can be applied for constructively handling rejected visualization papers. The most prominent strategies include: discussing reviews with collaborators and making a resubmission plan, doing a major revision to improve the work, shortening the work, and seeing rejection as a positive learning experience.

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.

“A picture is worth a thousand words” is a famous English saying. It is true in many cases, because a complex idea or data can be simply conveyed through the use of a single static image or diagram. Therefore, inclusion of graphics and visualizations in scientific texts has been an important aspect ever since researchers started publishing articles. Conventional writing styles allow researchers to provide images, graphs, and tables to enhance the readability and understandability of the text, but usually, these large-scale images need to be placed at a slightly different location from the relevant text, either on the same page or on the next pages. This far-off placement of graphics forces readers to switch their attention between text and graphics, which can cause a split-attention effect, thereby increasing the cognitive effort to comprehend the information.

Publication records and collaboration networks are important for assessing the expertise and experience of researchers. Existing digital libraries show the raw publication lists in author profiles, whereas visualization techniques focus on specific subproblems. Instead, we look at publication records from various perspectives mixing low-level publication data with high-level abstractions and background information. This work presents VIS Author Profiles, a novel approach to generate integrated textual and visual descriptions to highlight patterns in publication records. We leverage template-based natural language generation to summarize notable publication statistics, evolution of research topics, and collaboration relationships. Seamlessly integrated visualizations augment the textual description and are interactively connected with each other and the text. The underlying publication data and detailed explanations of the analysis are available on demand. We compare our approach to existing systems by taking into account information needs of users and demonstrate its usefulness in two realistic application examples.

Information Technology can vastly improve the quality of healthcare by providing fast, secure, efficient and inexpensive ways to acquire, store and analyze patient data electronically. Data acquisition systems help deal a large number of patients at hospitals, especially during an epidemic spread. The patient data can then be analyzed in real-time to visually monitor disease spread and take preventive measures to circumvent it. This paper focuses on the development of a 3-tier HL-71 compliant web and mobile based data acquisition and monitoring system. The use of centralized and shareable database makes it feasible to store all the data at one inventory, as health data from the far-flung rural areas is collected primarily using mobile applications. The experimental results of our system demonstrate its efficiency, ease of use and the ability to analyze real-time streaming health data along with temporal and spatial monitoring of disease outbreaks at a very granular (Mohalla2) level for the Lahore city. The developed system not only collects healthcare data, but also provides real-time monitoring of fatal diseases through the spatiotemporal analysis of the collected data.