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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.

Data Visualization Project Topic: Analysis of Military, Education, Healthcare spendings of G20 countires for 2009-2016 Data Source: Organisation for Economic Co-operation and Development (OECD) Database: GDP Education Population | Global Health Expenditure Database of WHO: Healthcare Description: • Compare the spending data to that country's GDP • Compare the education and health to the overall military spending of the country • Compare the per person educational spending to the per person GDP • Compare the per person healthcare spending to the per person GDP • Compare the per person military spending to the per person GDP • Fastest growing countries in healthcare and educational spending in fixed value and in percentage • interesting and useful relationships and trends Copyright ©| HTML5 | CSS3 | By Siqi Jiang

Data Visualization Project Topic: Analysis of Military, Education, Healthcare spendings of G20 countires for 2009-2016 Data Source: Organisation for Economic Co-operation and Development (OECD) Database: GDP Education Population | Global Health Expenditure Database of WHO: Healthcare Description: • Compare the spending data to that country's GDP • Compare the education and health to the overall military spending of the country • Compare the per person educational spending to the per person GDP • Compare the per person healthcare spending to the per person GDP • Compare the per person military spending to the per person GDP • Fastest growing countries in healthcare and educational spending in fixed value and in percentage • interesting and useful relationships and trends Copyright ©| HTML5 | CSS3 | By Siqi Jiang

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.

AbstractThis chapter elaborates on the definition of data visualization, highlights its historical development, and offers examples of how data visualization has been used in evaluations to help aid understanding, collect data and information, conduct analysis, and communicate to a variety of stakeholders. This chapter also outlines future trends in data visualization and their potential influence on evaluation practice. The chapter concludes with some of the main limitations and cautions that are associated with data visualization. © Wiley Periodicals, Inc., and the American Evaluation Association.

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.

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.

Data Visualization and Information Literacy