Evaluating narrative visualization: a survey of practitioners.

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Nina Errey, Jie Liang, Tuck Wah Leong, Didar Zowghi
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引用次数: 2

Abstract

Narrative visualization is characterized by the integration of data visualization and storytelling techniques. These characteristics provide challenges in its evaluation. Little is known about how these evaluation challenges are addressed by narrative visualization practitioners. We surveyed experienced narrative visualization practitioners to investigate their methods of evaluation. To gain deeper insight we conducted a series of semi-structured interviews with practitioners. We found that there is usually an informal approach to narrative visualization evaluation, where practitioners rely on prior experience and their peers for evaluation. Our study also revealed novel approaches to evaluation. We introduce a practice-led heuristic framework to aid practitioners to evaluate narrative visualization systematically. Our practice-led heuristic framework couples first-hand practitioner experience with recent research literature. This work sheds light on how to address narrative visualization evaluation to better inform both academic research and practice.

Abstract Image

Abstract Image

评价叙事可视化:对从业者的调查。
叙事可视化的特点是数据可视化和讲故事技术的结合。这些特点对其评估提出了挑战。关于叙事可视化从业者如何应对这些评估挑战,我们知之甚少。我们调查了经验丰富的叙事可视化从业者,以调查他们的评估方法。为了获得更深入的见解,我们对从业者进行了一系列半结构化的采访。我们发现,叙事可视化评估通常有一种非正式的方法,从业者依靠先前的经验和同行进行评估。我们的研究还揭示了新的评估方法。我们引入了一个以实践为导向的启发式框架,以帮助从业者系统地评估叙事可视化。我们以实践为导向的启发式框架将第一手从业者经验与最近的研究文献相结合。这项工作揭示了如何处理叙事可视化评估,以更好地为学术研究和实践提供信息。
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来源期刊
CiteScore
6.40
自引率
8.30%
发文量
72
期刊介绍: Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social sci­ence, and lifestyle. The field encompasses the larger ar­eas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new sci­entific chal­lenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and vis­ualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The jour­nal is composed of three streams: Regular, to communicate original and reproducible theoretical and experimental findings on data science and analytics; Applications, to report the significant data science applications to real-life situations; and Trends, to report expert opinion and comprehensive surveys and reviews of relevant areas and topics in data science and analytics.Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applica­tions of data science and analytics, with a primary focus on:statistical and mathematical foundations for data science and analytics;understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems;active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interopera­bility, exchange, and recommendation;in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;review, surveys, trends, prospects and opportunities of data science research, innovation and applications;data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains; andethics, quality, privacy, safety and security, trust, and risk of data science and analytics
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