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Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have used Twitter to spontaneously share data visualizations from news outlets and government agencies and to post casual data visualizations that they individually crafted. We conducted a Twitter crawl of 5409 visualizations (from the period between 14 April 2020 and 9 May 2020) to capture what people are posting. Our study explores what people are posting, what they retweet the most, and the challenges that may arise when interpreting COVID-19 data visualization on Twitter. Our findings show that multiple factors, such as the source of the data, who created the chart (individual vs. organization), the type of visualization, and the variables on the chart influence the retweet count of the original post. We identify and discuss five challenges that arise when interpreting these casual data visualizations, and discuss recommendations that should be considered by Twitter users while designing COVID-19 data visualizations to facilitate data interpretation and to avoid the spread of misconceptions and confusion.

Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have used Twitter to spontaneously share data visualizations from news outlets and government agencies and to post casual data visualizations that they individually crafted. We conducted a Twitter crawl of 5409 visualizations (from the period between 14 April 2020 and 9 May 2020) to capture what people are posting. Our study explores what people are posting, what they retweet the most, and the challenges that may arise when interpreting COVID-19 data visualization on Twitter. Our findings show that multiple factors, such as the source of the data, who created the chart (individual vs. organization), the type of visualization, and the variables on the chart influence the retweet count of the original post. We identify and discuss five challenges that arise when interpreting these casual data visualizations, and discuss recommendations that should be considered by Twitter users while designing COVID-19 data visualizations to facilitate data interpretation and to avoid the spread of misconceptions and confusion.

Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired 'inference at a glance' nature of barplots and other similar visualization devices. These "raincloud plots" can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter. ; MA is supported by a Lundbeckfonden Fellowship (R272-2017-4345), the AIAS-COFUND II fellowship programme that is supported by the Marie Skłodowska-Curie actions under the European Union's Horizon 2020 (Grant agreement no 754513), and the Aarhus University Research Foundation, and thanks Lincoln Colling for insightful statistical discussions. KW is funded by the Alan Turing Institute under the EPSRC grant EP/N510129/1. RAK is supported by the Wellcome Trust (grant number 107392/Z/15/Z).

"The era of Big Data as arrived, and most organizations are woefully unprepared. Slowly, many are discovering that stalwarts like Excel spreadsheets, KPIs, standard reports, and even traditional business intelligence tools aren't sufficient. These old standbys can't begin to handle today's increasing streams, volumes, and types of data. Amidst all of the chaos, though, a new type of organization is emerging. In The Visual Organization, award-winning author and technology expert Phil Simon looks at how an increasingly number of organizations are embracing new dataviz tools and, more important, a new mind-set based upon data discovery and exploration. Simon adroitly shows how Amazon, Apple, Facebook, Google, Twitter, and other tech heavyweights use powerful data visualization tools to garner fascinating insights into their businesses. But make no mistake: these companies are hardly alone. Organizations of all types, industries, sizes are representing their data in new and amazing ways. As a result, they are asking better questions and making better business decisions. Rife with real-world examples and case studies, The Visual Organization is a full-color tour-de-force"--

Data visualization is important for understanding the enormous amount of data generated daily. The education domain generates and owns huge amounts of data. Presentation of these data in a way that gives users quick and meaningful insights is very important. One of the biggest challenges in education is school dropouts, which is observed from basic education levels to colleges and universities. This paper presents a web-based data visualization tool for school dropouts in Tanzania targeting primary and secondary schools, together with the users' feedback regarding the developed tool. We collected data from the United Republic of Tanzania Government Open Data Portal and the President's Office - Regional Administration and Local Government (PO-RALG). Python was then used to preprocess the data, and finally, with JavaScript, a web-based tool was developed for data visualization. User acceptance testing was conducted and the majority agreed that data visualization is very helpful for quickly understanding data, reporting, and decision making. It was also noted that the developed tool could be useful not only in the education domain but it could also be adopted by other departments and organizations of the government.

Catherine D'Ignazio is a scholar, artist/designer and software developer who focuses on data literacy, feminist technology and civic art. She has run breastpump hackathons, created award-winning water quality sculptures that talk and tweet, and led walking data visualizations to envision the future of sea level rise. Her research at the intersection of gender, technology and the humanities has been published in the Journal of Peer Production, the Journal of Community Informatics, and the proceedings of Human Factors in Computing Systems (ACM SIGCHI). D'Ignazio is an Assistant Professor of Civic Media and Data Visualization at Emerson College, a faculty director of the Engagement Lab and a research affiliate at the MIT Center for Civic Media.

Nowadays, managers and policymakers are overwhelmed by the volume of information coming from databases and the Internet. The ability to zero in on significant data and form reliable information sources is even more difficult. Choosing, collecting, analyzing, and managing data about global problems such as carbon emissions, earthquake warnings, climate change, or pandemics, for example, is no easy task. In addition, the wasted amount of data available from different kinds of sources make the job of interpreting events and anticipating changes more complicated still. Business managers, analysts, and policymakers need to deal with this issue by looking to new tools or models that were not available until now. This article explores how modern data and network visualization methods can help policymakers understand an ongoing situation in a relatively short time, as well as help predict future events by interacting with it. Examples of interactive visualization and network analysis are discussed.

This contribution discusses the potential and challenges of maps' data visualization as a practice suitable to enhance social and legal accountability in the governance of environmental hazards affecting public health. The focus is on the 'Global South' and, specifically, on the haze pollution that Southeast Asia is experiencing due to illegal forest fires. The haze is discussed as a pressing public health concern for the region (Koplitz, S.N. et al., 2016). It is shown how efforts to tackle the haze have been hindered by a lack of reliable evidence on the fires' exact location and on land ownership, worsened by an uncooperative attitude of governments and companies. This status-quo characterized by a lack of data, in certain instances, or by a denied access to the available data, in other cases, is the problem this contribution addresses. The chapter zooms in on creative solutions that arose from the bottom level, namely that of non-state haze mapping initiatives that 'bypassed' the institutional system. Desk researches on the haze impacts and on theories on the use of bottom-up produced evidence in environmental risk governance are combined with web analysis and with qualitative research based on: observations performed at Greenpeace International, and targeted communications with stakeholders and organizations on the ground. This contribution inspects the potential of such maps to 'make visible politically masked risks' by filling institutional gaps, to enhance social accountability by triggering social agency at the individual and collective levels, and to even promote legal accountability. Such arguments are supported by NGOs' reports (e.g. Greenpeace, 2016) and by authoritative legal documents granting relevant rights, as the right to access environmental information, and by academic literature vocal on the topic (e.g. Lee, 2005). The knowledge gap this contribution aims at filling is identified in the scarce understanding of the real potential of these bottom-up haze maps, whose social utility and even legal admissibility is not plain. This discussion contributes to the debate triggered by interest groups, NGOs and local communities on the need for alternative and more transparent ways for tackling the haze in Southeast Asia. In the conclusion, recommendations are formulated on how to address the challenges posed by these alternative mapping methods and release the full potential of these bottom-up produced maps as an invaluable tools for promoting a more accountable governance of the haze risk.

IntroductionData visualization is a valuable means for reporting and interpreting large datasets. It allows users to present key messages from the data in a simple way. Although many industries have adopted and embedded data visualization within their analytic teams, healthcare has only recently begun to realize this potential.
Objectives and ApproachThe objective of this interactive presentation is to introduce Tableau (visualization software) and to provide quick, impactful examples of its use. Visualizations will be created using a free version of Tableau Desktop; available to students and academics. Two blinded datasets encompassing hospital discharges in Alberta from April 2014 to March 2016 will be used. The first dataset will contain approximately 50,000 patient experience surveys, as completed by patients within 6 weeks of their hospital discharge. The second dataset will contain inpatient records from the Discharge Abstract Database (DAD). Visualizations will be created using the individual and combined datasets.
ResultsFollowing a brief description of each dataset and its respective elements, a variety of interactive visualizations will be created in real-time. From the patient experience dataset, we will be able to quickly determine which hospitals have the highest overall rating from their patients. We will then display the results from all survey questions from a single hospital; allowing for a determination of areas where care is delivered well, and to provide opportunities for improvements. From the DAD, we will highlight hospital length of stay, and its relation with gender, age group, geography, and clinical condition. In the final portion of the presentation, both datasets will be linked to examine the relationships between survey responses, patient demographics, and clinical characteristics.
Conclusion/ImplicationsData visualization has great potential in healthcare. From this presentation, attendees will receive an introduction to its use using practical, real-world examples. The dynamic visualizations in this presentation will be created in mere minutes; a small fraction of the time typically spent by analysts to create static, paper-based reports.