{"title":"Design practices in visualization driven data exploration for non-expert audiences","authors":"Natasha Tylosky, Antti Knutas, Annika Wolff","doi":"10.1016/j.cosrev.2025.100731","DOIUrl":null,"url":null,"abstract":"<div><div>Data exploration is increasingly relevant to the average person in our data-driven world, as data is now often open source and available to the general public and other non-expert users via open data portals and other similar data sources. This has introduced the need for data exploration tools, methods and techniques to engage non-expert users in data exploration, and thus a proliferation of new research in the field of Human Computer Interaction (HCI) that relates to engaging non-expert audiences with data. In particular data exploration that contains a data visualization component can be useful for making data understandable and engaging for non-expert audiences.</div><div>Currently, the range of design practices most commonly used in the field of HCI to engage non-expert audiences in data exploration that includes a visualization component has yet to be formalized or given a comprehensive overview. This paper is a systematic mapping study (SMS) which aims to fill that gap by analyzing design trends engaging non-expert audiences in visualization driven data exploration via interactive systems, providing an overview of existing design practices and engagement methods, as well as set of three recommendations for how future designers can best engage non-expert audiences in visualization driven data exploration.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100731"},"PeriodicalIF":13.3000,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science Review","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574013725000085","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Data exploration is increasingly relevant to the average person in our data-driven world, as data is now often open source and available to the general public and other non-expert users via open data portals and other similar data sources. This has introduced the need for data exploration tools, methods and techniques to engage non-expert users in data exploration, and thus a proliferation of new research in the field of Human Computer Interaction (HCI) that relates to engaging non-expert audiences with data. In particular data exploration that contains a data visualization component can be useful for making data understandable and engaging for non-expert audiences.
Currently, the range of design practices most commonly used in the field of HCI to engage non-expert audiences in data exploration that includes a visualization component has yet to be formalized or given a comprehensive overview. This paper is a systematic mapping study (SMS) which aims to fill that gap by analyzing design trends engaging non-expert audiences in visualization driven data exploration via interactive systems, providing an overview of existing design practices and engagement methods, as well as set of three recommendations for how future designers can best engage non-expert audiences in visualization driven data exploration.
期刊介绍:
Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.