{"title":"The power of visualization choices:","authors":"B. Ricker, M. J. Kraak, Y. Engelhardt","doi":"10.2307/j.ctvzgb8c7.30","DOIUrl":"https://doi.org/10.2307/j.ctvzgb8c7.30","url":null,"abstract":"","PeriodicalId":437386,"journal":{"name":"Data Visualization in Society","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123966253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-16DOI: 10.5117/9789463722902_ch11
H. Kennedy, Wibke Weber, Martin Engebretsen
This chapter explores the role of data visualization in relation to transparency in the news, a field in which a decline in trust and a subsequent need to reassert credibility is an ongoing challenge. Being transparent about how the news is produced is seen as one way of generating trust, yet there has been very little empirical research into transparency practices in newsrooms. Our chapter fills this gap, focusing on transparency and data visualization. We argue that working with data visualization involves particular enactments of transparency, many of which are surprisingly not visual.
{"title":"Data visualization and transparency inthe news","authors":"H. Kennedy, Wibke Weber, Martin Engebretsen","doi":"10.5117/9789463722902_ch11","DOIUrl":"https://doi.org/10.5117/9789463722902_ch11","url":null,"abstract":"This chapter explores the role of data visualization in relation to transparency in the news, a field in which a decline in trust and a subsequent need to reassert credibility is an ongoing challenge. Being transparent about how the news is produced is seen as one way of generating trust, yet there has been very little empirical research into transparency practices in newsrooms. Our chapter fills this gap, focusing on transparency and data visualization. We argue that working with data visualization involves particular enactments of transparency, many of which are surprisingly not visual.","PeriodicalId":437386,"journal":{"name":"Data Visualization in Society","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127388986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Having the skills and awareness to make sense of data visualizations has become a contributing factor in determining who gets to participate in our data-driven society. Initiatives that seek to enable people to make sense of some aspect of our digital, datafied worlds are often described in terms of literacy. However, taking a closer look at different usages of literacy across academia, policy, and practice reveals different notions of power embedded in different populations’ implicit understanding of the term. Situated in the emerging field of critical data studies, the field that is concerned with understanding data’s role in reproducing and creating social inequalities, this is a conceptual chapter that asks how useful literacy is in this context.
{"title":"14. Is literacy what we need in an unequal data society?","authors":"L. Pinney","doi":"10.2307/j.ctvzgb8c7.20","DOIUrl":"https://doi.org/10.2307/j.ctvzgb8c7.20","url":null,"abstract":"Having the skills and awareness to make sense of data visualizations has become a contributing factor in determining who gets to participate in our data-driven society. Initiatives that seek to enable people to make sense of some aspect of our digital, datafied worlds are often described in terms of literacy. However, taking a closer look at different usages of literacy across academia, policy, and practice reveals different notions of power embedded in different populations’ implicit understanding of the term. Situated in the emerging field of critical data studies, the field that is concerned with understanding data’s role in reproducing and creating social inequalities, this is a conceptual chapter that asks how useful literacy is in this context.","PeriodicalId":437386,"journal":{"name":"Data Visualization in Society","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134097647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"What is at stake in data visualization?","authors":"R. Hill","doi":"10.2307/j.ctvzgb8c7.29","DOIUrl":"https://doi.org/10.2307/j.ctvzgb8c7.29","url":null,"abstract":"","PeriodicalId":437386,"journal":{"name":"Data Visualization in Society","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132052164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many news stories are based on data visualization, and storytelling with data has become a buzzword in journalism. But what exactly does storytelling with data mean? When does a data visualization tell a story? And what are narrative constituents in data visualization? This chapter first defines the key terms in this context: story, narrative, narrativity, showing and telling. Then, it sheds light on the various forms of narrativity in data visualization and, based on a corpus analysis of 73 data visualizations, describes the basic visual elements that constitute narrativity: the instance of a narrator, sequentiality, temporal dimension, and tellability. The paper concludes that understanding how data are transformed into visual stories is key to understanding how facts are shaped and communicated in society.
{"title":"Exploring narrativity in data visualization in journalism","authors":"Wibke Weber","doi":"10.2307/j.ctvzgb8c7.24","DOIUrl":"https://doi.org/10.2307/j.ctvzgb8c7.24","url":null,"abstract":"Many news stories are based on data visualization, and storytelling with data has become a buzzword in journalism. But what exactly does storytelling with data mean? When does a data visualization tell a story? And what are narrative constituents in data visualization? This chapter first defines the key terms in this context: story, narrative, narrativity, showing and telling. Then, it sheds light on the various forms of narrativity in data visualization and, based on a corpus analysis of 73 data visualizations, describes the basic visual elements that constitute narrativity: the instance of a narrator, sequentiality, temporal dimension, and tellability. The paper concludes that understanding how data are transformed into visual stories is key to understanding how facts are shaped and communicated in society.","PeriodicalId":437386,"journal":{"name":"Data Visualization in Society","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126068188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
‘Beautiful’ is an adjective often used in descriptions of well-designed data visualizations. How the concept is used, however, reveals that it is applied to characterize a variety of qualities. Going beyond mere descriptions, the use of the concept also lays bare a certain ambivalence among scholars and practitioners towards how beauty matters, and which means it serves in data visualization. Interrogating ‘beautiful’ as a characterizing word, combined with a study of cases of ‘best practice’ used as examples of beautiful visualizations in various discourses, this chapter presents an analysis of what is regarded as beautiful within the field of data visualization design. This, in turn, can inform the understanding of what beauty means in visualizing data, in the purpose of facilitating the viewer’s comprehension and engagement.
{"title":"16. What we talk about when we talk about beautiful data visualizations","authors":"Sara Brinch","doi":"10.2307/j.ctvzgb8c7.22","DOIUrl":"https://doi.org/10.2307/j.ctvzgb8c7.22","url":null,"abstract":"‘Beautiful’ is an adjective often used in descriptions of well-designed data visualizations. How the concept is used, however, reveals that it is applied to characterize a variety of qualities. Going beyond mere descriptions, the use of the concept also lays bare a certain ambivalence among scholars and practitioners towards how beauty matters, and which means it serves in data visualization. Interrogating ‘beautiful’ as a characterizing word, combined with a study of cases of ‘best practice’ used as examples of beautiful visualizations in various discourses, this chapter presents an analysis of what is regarded as beautiful within the field of data visualization design. This, in turn, can inform the understanding of what beauty means in visualizing data, in the purpose of facilitating the viewer’s comprehension and engagement.","PeriodicalId":437386,"journal":{"name":"Data Visualization in Society","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129337856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-16DOI: 10.5117/9789463722902_ch01
H. Kennedy, Martin Engebretsen
{"title":"Introduction : The relationships between graphs, charts, maps and meanings, feelings, engagements","authors":"H. Kennedy, Martin Engebretsen","doi":"10.5117/9789463722902_ch01","DOIUrl":"https://doi.org/10.5117/9789463722902_ch01","url":null,"abstract":"","PeriodicalId":437386,"journal":{"name":"Data Visualization in Society","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133685268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We assert that visual-numeric literacy, indeed all data literacy, must take as its starting point that the human relations and impacts currently produced and reproduced through data are unequal. Likewise, white men remain overrepresented in data-related fields, even as other STEM (Science, Technology, Engineeering and Medicine) fields have managed to narrow their gender gap. To address these inequalities, we introduce teaching methods that are grounded in feminist theory, process, and design. Through three case studies, we examine what feminism may have to offer visualization literacy, with the goals of cultivating self-efficacy for women and underrepresented groups to work with data, and creating learning spaces where, as Philip et al. (2016) state, ‘groups influence, resist, and transform everyday and formal processes of power that impact their lives’.
{"title":"Data visualization literacy:","authors":"C. D’Ignazio, Rahul Bhargava","doi":"10.2307/j.ctvzgb8c7.19","DOIUrl":"https://doi.org/10.2307/j.ctvzgb8c7.19","url":null,"abstract":"We assert that visual-numeric literacy, indeed all data literacy, must take as its starting point that the human relations and impacts currently produced and reproduced through data are unequal. Likewise, white men remain overrepresented in data-related fields, even as other STEM (Science, Technology, Engineeering and Medicine) fields have managed to narrow their gender gap. To address these inequalities, we introduce teaching methods that are grounded in feminist theory, process, and design. Through three case studies, we examine what feminism may have to offer visualization literacy, with the goals of cultivating self-efficacy for women and underrepresented groups to work with data, and creating learning spaces where, as Philip et al. (2016) state, ‘groups influence, resist, and transform everyday and formal processes of power that impact their lives’.","PeriodicalId":437386,"journal":{"name":"Data Visualization in Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130493444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The political significance of data visualization:","authors":"Torgeir Uberg Nærland","doi":"10.2307/j.ctvzgb8c7.10","DOIUrl":"https://doi.org/10.2307/j.ctvzgb8c7.10","url":null,"abstract":"","PeriodicalId":437386,"journal":{"name":"Data Visualization in Society","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116195143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：