Today we are witnessing an increased use of data visualization in society. Across domains such as work, education and the news, various forms of graphs, charts and maps are used to explain, convince and tell stories. In an era in which more and more data are produced and circulated digitally, and digital tools make visualization production increasingly accessible, it is important to study the conditions under which such visual texts are generated, disseminated and thought to be of societal benefit. This book is a contribution to the multi-disciplined and multi-faceted conversation concerning the forms, uses and roles of data visualization in society. Do data visualizations do 'good' or 'bad'? Do they promote understanding and engagement, or do they do ideological work, privileging certain views of the world over others? The contributions in the book engage with these core questions from a range of disciplinary perspectives.
Today we are witnessing an increased use of data visualization in society. Across domains such as work, education and the news, various forms of graphs, charts and maps are used to explain, convince and tell stories. In an era in which more and more data are produced and circulated digitally, and digital tools make visualization production increasingly accessible, it is important to study the conditions under which such visual texts are generated, disseminated and thought to be of societal benefit. This book is a contribution to the multi-disciplined and multi-faceted conversation concerning the forms, uses and roles of data visualization in society. Do data visualizations do 'good' or 'bad'? Do they promote understanding and engagement, or do they do ideological work, privileging certain views of the world over others? The contributions in the book engage with these core questions from a range of disciplinary perspectives.
Today we are witnessing an increased use of data visualization in society. Across domains such as work, education and the news, various forms of graphs, charts and maps are used to explain, convince and tell stories. In an era in which more and more data are produced and circulated digitally, and digital tools make visualization production increasingly accessible, it is important to study the conditions under which such visual texts are generated, disseminated and thought to be of societal benefit. This book is a contribution to the multi-disciplined and multi-faceted conversation concerning the forms, uses and roles of data visualization in society. Do data visualizations do 'good' or 'bad'? Do they promote understanding and engagement, or do they do ideological work, privileging certain views of the world over others? The contributions in the book engage with these core questions from a range of disciplinary perspectives.
Introduction: The relationships between graphs, charts, maps and meanings, feelings, engagements / Helen Kennedy and Martin Engebretsen -- Section I Framing data visualization -- Ways of knowing with data visualizations / Jill Walker Rettberg -- Inventorizing, situating, transforming: Social semiotics and data visualization / Giorgia Aiello -- The political significance of data visualization: Four key perspectives / Torgeir Uberg Ncerland -- Section II Living and working with data visualization -- Rain on your radar: Engaging with weather data visualizations as part of everyday routines / Eef Masson and Karin van Es -- Between automation and interpretation: Using data visualization in social media analytics companies / Salla-Maaria Laaksonen and Juho Pääkkonen -- Accessibility of data visualizations: An overview of European statistics institutes / Mikael Snaprud and Andrea Velazquez -- Evaluating data visualization: Broadening the measurements of success / Arran L. Ridley and Christopher Birchall -- Approaching data visualizations as interfaces: An empirical demonstration of how data are imag(in)ed / Daniela van Geenen and Maranke Wieringa -- Visualizing data: A lived experience / Jill Simpson -- Data visualization and transparency in the news / Helen Kennedy, Wibke Weber, and Martin Engebretsen -- Section Ill Data visualization, learning, and literacy -- What is visual-numeric literacy, and how does it work? / Elise Seip Tønnessen - Data visualization literacy: A feminist starting point / Catherine D'Ignazio and Rahul Bhargava -- Is literacy what we need in an unequal data society? / Lulu Pinney -- Multimodal academic argument in data visualization / Arlene Archer and Travis Noakes -- Section IV Data visualization semiotics and aesthetics -- What we talk about when we talk about beautiful data visualizations / Sara Brinch - A multimodal perspective on data visualization / Tuomo Hiippala -- Exploring narrativity in data visualization in journalism / Wibke Weber -- The data epic: Visualization practices for narrating life and death at a distance / Jonathan Gray -- What a line can say: Investigating the semiotic potential of the connecting line in data visualizations / Verena Elisabeth Lechner -- Humanizing data through 'data comics': An introduction to graphic medicine and graphic social science / Aria Alamalhodaei, Alexandra Alberda, and Anna Feigenbaum -- Section V Data visualization and inequalities -- Visualizing diversity: Data deficiencies and semiotic strategies / John P. Wihbey, Sarah] Jackson, Pedro M Cruz, and Brooke Foucault Welles -- What is at stake in data visualization? A feminist critique of the rhetorical power of data visualizations in the media / Rosemary Lucy Hill -- The power of visualization choices: Different images of patterns in space / Britta Ricker, Menno-Jan Kraak, and Yuri Engelhardt -- Making visible politically masked risks: Inspecting unconventional data visualization of the Southeast Asian haze / Anna Berti Suman -- How interactive maps mobilize people in geoactivism / Miren Gutiérrez.
Visualizing data is central to social scientific work. Despite a promising early beginning, sociology has lagged in the use of visual tools. We review the history and current state of visualization in sociology. Using examples throughout, we discuss recent developments in ways of seeing raw data and presenting the results of statistical modeling. We make a general distinction between those methods and tools designed to help explore data sets and those designed to help present results to others. We argue that recent advances should be seen as part of a broader shift toward easier sharing of code and data both between researchers and with wider publics, and we encourage practitioners and publishers to work toward a higher and more consistent standard for the graphical display of sociological insights.
The importance given by the governments to building a sound intellectual property infrastructure is increasing in developing countries and especially in Central Asian countries. This infrastructure is continuously improved to live up to a common standard in collaboration with government agencies, educational institutions and international agencies. In this paper, the infrastructure developments that took place in the Central Asian countries is going to be elaborated and furthermore some statistical analyses will be used in order to compare the differences and similarities between the Central Asian republics within themselves and the rest of the world. Patent based statistical data reveal a broad range of information concerning the innovative capability of countries, regions and firms. The number of patents that a country obtains in different technological fields and the change in this number over the years may provide useful information regarding the growth potential of the country and the ability to follow technological advances. For this purpose, patent statistics collected by institutions like World Intellectual Property Organization (WIPO) have been analyzed using statistical techniques. In addition to basic statistics, multidimensional scaling analysis (MDS) has been applied to the data sets.
On the whole, the field of data visualization is white. Contemporary views of historical data visualization tend to trace back to a few iconic visuals tied to European wars and conquests. The modern explosion of the field has been centered around the ideas of white men, as if they invented data visualization. Yet, Indigenous populations world-wide have been incorporating data visualization into their record keeping for centuries before anyone had heard of Edward Tufte. In this article, three Indigenous evaluators (Mohican/Munsee, Cherokee, and Tlingit) along with a non-Indigenous co-conspirator, will discuss their journeys creating space to weave together Western notions of data visualization best practices and Indigenous ways of knowing and storytelling. The authors focus their evaluative work on the support of Indigenous communities and will reflect on what has worked in communicating data, what hasn't, and how far data visualization has to go in all four directions.
It is logical that the generalization of digital approaches in history is leading to a democratization of the graphic representation of the data produced by these processes. Rather than presenting long series of examples, this very cursory chapter seeks to fuel reflection on our uses: why do we visualize historical data? Is it for illustrative purposes, to "show" our historical object and make it understandable to a large audience? Or is it, on the contrary, because the raw data is unintelligible to us, and visualization is therefore a heuristic tool intended for their exploration? The central point of my argument is based on a typology of sources and uses, a double entry table which is intended as a kind of decision-making aid for those seeking to make their data speak in the right way to the right audience.
In: Christensen, Theodore E. and Fronk, Karson and Lee, Joshua A. and Nelson, Karen K., Data Visualization in 10-K Filings (July 20, 2023). Journal of Accounting & Economics (JAE), Forthcoming., Available at SSRN: https://ssrn.com/abstract=4520788 or https://doi.org/10.1016/j.jacceco.2023.101631.
This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader's expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective "small multiple" plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible.