The use of data visualization for human rights defenders has become a common strategy to effectively convey messages to their audiences, but attempts to engage and educate their audience about their data has posed challenges. Specifically, the data they use and how to formulate it into compelling, but objective, information. Equally as challenging, can be the process of converting data into a visualization that is easy to read and understand. Proper use of such tools is necessary to keep data truthful and safe.

In this conversation, experts were asked to discuss the benefits of using data visualization, as well as, shortcomings experienced. Further, experts were asked of possible methods to optimize the impact of data visualizations, and the ideal process for creating them. Respondents also weighed in on the use of data visualization when attempting to protect sensitive populations or information.

Strategic Use of Data Visualization

The first round of questions were centered around the usefulness of data visualization in human right organizations and the best methods to make a more effective impact. Joanna Boehnert, who is a research fellow of Graphic Design at the Centre for Research and Education in Arts and Media (CREAM) of the University of Westminster stated that data visualization seemed particularly useful in situations where there are “abuses of power” because of the accessibility to data necessary to construct a visualization. Boehnert goes on to mention that data visualization “enables instantaneous communication” allowing audiences to understand and absorb information easily. She then poses a question to other respondents who are participating, about the limitations of such a method rather than the usefulness of the strategy.

Another Respondent, Margaret Satterthwaite, a faculty director of the Center for Human Rights and Global Justice at NYU Law, suggests that “tailoring” visualizations to target audiences can be effective in cases where their are “polarized attitudes” towards a topic. Satterthwaite cites “an experiment carried out by an NYU research team” who found that the use of charts rather than tables, were more persuasive to neutral audiences while tables appeared to persuade the negative audience toward the conveyed message.

The rest of the respondents focused heavily on the quality of data chosen when attempting to create data visualization. Brian Root, mentions the legitimacy of the “data collected”, were the methods used upon collection objective and sound? Secondly, Root mentions the difficulty of trying to “quantify” data in the field of human rights violations. These challenges shape how the information will be presented and the overall message’s effectiveness. He emphasizes that proper analysis of the data is necessary to create a powerful and appropriate visualization, since human rights organizations strive to produce true fact.

Another point was made by respondent who mentioned the limitations of data visualization. Boehnert states again that data visualization is “powerful”, and can “warrant authority” that is undeserving. She points to the idea of “Fake News” today, explaining that verification of legitimacy is important in understanding truth and unbiased truth as well.

Concerns and Considerations

The second round of the online conversation asked the panel to discuss visualization in regards to “sensitive data and populations”. Including designing visualizations to produce impacts and how to “visualize qualitative data”.

John Emerson. who is  is a researcher at the Center for Human Rights and Global Justice, states that looking at the target audience is the best way to create an effective impact. “Once defining your target audiences”, begin to “drill into their motivations”, look at what matters to them, what will appeal to them most when creating this visualization?

Martin Törnros breaks down two ways to visualize data. Using an “Exploratory visualization” or  an “Explanatory” one. Törnros explains the an exploratory visualization allows the audience to “explore the data and find their own patterns”, while the explanatory approach analyzes the data and explains it to the targeted audience, “leaving less room for the user to play around with the data”.  Root again emphasizes the necessity for “analysis expertise” to create a complete visualization that remains unbiased and covers the data fully.

Catherine D'Ignazio, who is an assistant professor of Civic Media and Data Visualization at Emerson College, answers the question of protection for vulnerable populations by sharing her own experience. While working on a project, a “Tohono O’odham (Native American)” activist she knew mentioned that “their land straddles the US-Mexico border”, when there was construction of the fence, the government had requested locations of burial sites in order to not disturb or destroy the areas. But in O'odham’s culture, rules prevent them from sharing such information “with outsiders”, since it is sacred. D’Ignazio asks other respondents to share their own thoughts and experiences of similar situations.

Emerson again touches on quality of visualization by noting different features used for data visualization, such as “maps or timelines” for instance. Looking at different templates that can hold data and which ones seem to engage the audience most. Emerson mentions that machine learning would be able to draw out patterns of such visualizations producing an “automated analysis” for the viewer.

Many respondents looking at the concept of self-determination. One respondent mentions filling in the “blank spaces” of data, citing an example of the LGBTQ community of the undetermined populations that identify differently from the categorized data being collected. Creating gaps in data, that change the information used.

Concluding the online conversation were many respondents sharing examples, works and tools they’ve used for data visualization. A respondent who helped develop a data visualization tool made for beginners was praised for her effort in building data literacy. Her tools are four-fold, all touching on different areas of data visualization with a guide to learn how to construct them. Further others shared their own spreadsheets with lists of NGO’s and their own data visualized graphics/infographics that have been produced. Depicting the way these organizations are sharing their information.

Emerson pointed out challenges when trying to view visualization in graphics, such as few interactive graphics are phone friendly. Other do not print correctly, some do not have time stamps or proper citations. Graphics are not created in other languages other than mostly English All things to think about when beginning to seriously utilize this tool. Lastly, he emphasizes the necessity of making these visualizations accessible, easy, and understandable in hopes of reaching the target audience.

Data Visualization Examples Shared