The period of time following the November 2016 presidential election has seen an increase in hate crimes. Researchers, policymakers, and concerned agencies need to continue to collect more evidence to develop policies and programs in response and to inform the public. Although the FBI encourages victims and communities to work with the agency and local law enforcement to collect the incidence of hate crimes, especially in the aftermath of a racially-charged and misogynistic election campaign, the next hate crime report will not be issued for many months. The lag time is one of many shortcomings of federal hate crimes data collection. But these constraints are (re)creating opportunities for news organizations, civil rights organizations (e.g., Asian Americans Advancing Justice), academics, and designers. They are developing new projects that are invested in data visualization, and utilizing sources such as news reports, social media, etc. to record, monitor, and analyze this period of violence. Furthermore, because of continued innovations in data visualization, designing and representing the data collected can be quite impactful, telling a visually compelling narrative that can appeal to policymakers and the public.
On November 29, 2016, just a little over a week after the U.S. presidential elections concluded, the Editorial Board of The New York Times began a series entitled, “This Week in Hate.” Citing the work of the Southern Poverty Law Center and other allied organizations, their purpose was to track hate crimes and harassment since the election of Donald Trump. The series presented a “selection of incidents to show the scope of the problem” as well as its explicit relationship to the caustic presidential rhetoric that not only sanctioned, but is also believed to have intensified the rise and frequency of incidents of anti-Semitism, Islamophobia, xenophobia, racism, and transphobia.
Around the same time, ProPublica, an independent, nonprofit news agency in partnership with numerous agencies, universities, and civil rights organizations, produced Documenting Hate, an investigation into hate crimes in America. Using data analysis, social media newsgathering, and storytelling, their work stems from the fact that “there is simply no reliable national data on crimes” and “no government agency documents lower-level incidents of harassment and intimidation, such as online or real-life bullying.”
They assert a number of limitations that result in the absence of a complex account of the nature and full picture of hate violence in the United States. Such limitations include the categories of crime and a limited number of biases that are considered. For example, although intimidation is included, the report does not disaggregate the category into subgroups such as physical or online bullying. However, over the years, there have been improvements to collection categories such as the addition of gender identity and several religious identities. Yet little else is required of the FBI but to issue the annual report. Moreover, the FBI under the direction of the U.S. Attorney General is not required to issue any report during the year that may be of relevance to the public or community. The Hate Crime Statistics report has been critiqued here, here, here, here, and here. As sociologist Ryken Grattet rightly concludes, the best information (still) comes from local rather than national sources. The Uniform Crime Reporting Program Hate Crime Statistics report paints a broad picture of hate crimes in America helping policymakers, researchers, law enforcement, and the general public track general trends and longitudinal changes.
In the 1970s through the 1980s, national civil rights and community organizations collected and organized their own reports using local sources (e.g., news clips, police reports, personal memoirs, testimonies, etc.), developed their own categories of incidents, published newsletters and alerts notifying the community if a hate crime had occurred in their area, and developed their own policy agendas specific to their needs. These reports are significant reminders of how data collection and anti-hate projects were originally grounded within the community’s interests (Here is a small sample of publications from the 1980s, but the work is archived online and in local libraries: the archives of newsletters of Committee Against Anti-Asian Violence in New York City, 1985 Audit of Anti-Semitic Incidents by the Anti-Defamation League, June 1988 Executive Summary of Violence and Discrimination Against Lesbian and Gay People in Philadelphia, and October 1987 Report Responding to Anti-Asian Violence in Boston). The early uses of data visualization in this period were basic graphical representations depicting the trends in hate violence. But as the technology developed, the designs became more sophisticated and the visualization of data more savvy. As a result, the narrative of what the data told became more compelling, complex and emotional.
A side note about data visualization: there are great readings about what is data visualization and its methods. Check out: Nathan Yau’s Visualize This: The FlowingData Guide to Design, Visualization, and Statistics (2011), and Stephen Few’s Now You See It: Simple Visualization Techniques for Quantitative Analysis (2009). Edward R. Tufte’s work – especially The Visual Display of Quantitative Information (1992) – has been the most impactful for me, and I would highly recommend it. He is one of the few truly interdisciplinary scholars in design and political science who understands the enormous complexity of taking massively large datasets and designing a representation that not only makes visual sense, but more more importantly, but also tells a persuasive narrative to constituents who need to make a decision.
What’s this “Good” in “Good Visualizations”?
There are several principles that constitute “good visualization” though I do not wish to debate what “good” is nor what belongs on the comprehensive list of elements and principles that go into visualization and design. I do wish to convey some generally agreed upon methods and principles of what to look for and why I am making certain judgments. For an accessible but substantive reading, try Scott Berinato’s “Visualizations That Really Work” (June 2016).
According to Scott Berinato, good everyday data visualization is one that comprises a few variables and communicates a simple message. He continues, “the goals [are] affirming and setting context.” Therefore, the elements of simplicity, clarity, and consistency make everyday visualizations most effective.
As I previously mentioned, there is a great need to collect data on hate crimes for research, to develop programs and policy responses, and to inform the public. There’s an even greater need for flexibility and specificity, and furthermore to represent such data that is visually compelling and emotionally affective. Mapping Islamophobia is one such project that arose out of a need for more detailed representations of hate crime data focusing on the incidence of Islamophobia.
Mapping Islamophobia was created by Caleb Elfenbein, Professor of Religious Studies, Grinnell College. Elfenbein is the site’s primary investigator, editor, and contributor. Mike Connor, the Digital Liberal Arts Specialist, provides technological guidance and support, and Chloe Briney, a student at Grinnell College, serves as a contributor, performing data entry and offering analysis.
The site was released in January 2017, and overall, their work is stunning. Using data from civil rights organizations, law enforcement agencies, and news sources and searchable databases, their maps are able to visualize the social and political phenomenon of Islamophobia in several convincing ways. I look forward to future evolutions of this project.
The project offers three maps. The first, a “Map of Islamophobia Over Time,” shows the accumulation of incidents from January 3, 2016 to February 24, 2017, indicating when and where spikes of activity occurred. The scroll bar at the bottom of the map allows you to manually move through the time period. You can stop the animation and double-click to zoom into a particular location or incident.
However, when you double-click on an incident, no additional information pops up. I do not know if or whether this feature should be added on. Part of the limitation may be the result of using Carto, the platform upon which the map was used. It may not have a click function so demonstrating not only the growth of incidents, but punctuating specific ones with emotional affect may be useful. For example, if the intent is to show the severity of the incidents, this can be demonstrated by having the bubbles “explode” (e.g., Ellis Act Evictions by the Anti-Eviction Mapping Project). In order to show the spread of the incidents, one could show its mass by its distribution, which would need a larger data set and cover a longer period of time. For example, Growth of Walmart and Sam’s Club by FlowingData is an amazing visualization. It’s like watching a cancerous growth.
Nevertheless, the map still works. It’s visually interesting and has a good color balance. An orange-colored incident against a darkened background functions well, and it darkens to indicate multiple incidents, with their severity indicated by the bar below the map.
The second map, “Interactive Map of Islamophobia (By Year),” shows an orange dot representing an incident of harassment, or vandalism, from national political speeches and elections. A really interesting feature is the ability to click on the dot which opens up a short window describing the event and includes a source for additional reading. You can “grab” the map to move around the country as well view single or multiple years.
Again, two years is a limited dataset compared to the ten years of data compiled by the UCR hate crime statistics, but this second map is by far the most intriguing one because it is so reminiscent of the community reports from the 1980s that documented hate crimes with short descriptions of the incidents. This site has a lot of potential as it expands and grows.
The third and final map entitled, “Interactive Map of Islamophobia (By Type),” has a series of color-coded dots representing Islamophobic incidents. Like the previous map, the incidents cover harassment and vandalism. Clicking on a dot reveals a short description and a source for additional reading.
The incidents can be disaggregated according to category, which includes legislation, public campaigns, public speech, crimes against property, bias-related incident, crimes against people, and others. Such categorizations are useful if you need to quickly identify what types of crimes and incidents have been committed, if there are “hot spots,” if one area of the country or state experiences one type of crime than another, or if a community seems to be particularly vulnerable to one or two kinds of attacks. A shortcoming of this map, which may be useful to those who want quick information, is the absence of a table that shows the total number of incidents according to type that can change when you toggle the selections. Along with showing trends is the need to show raw numbers. Although the map may show a wide variety of colors and incidents, it cannot tell me how many have been committed under each type, which would be useful. It’s a tiny correction, but one that could immensely improve its overall utility.
THE BOTTOM LINE:
Mapping Islamophobia by Caleb Elfenbein, Mike Conner, and Chloe Briney is off to a fantastic start. It is TIMELY and visually compelling. The maps satisfy the basic criteria of a “good visualization” that communicates a simple message: Islamophobia is widespread, intensifies at certain moments, and coalesces in specific regions of the United States. Each individual map affirms and sets a context. Map 1 shows the incidence of Islamophobia across the country over a period of time, spiking in the months of December and February. Map 2 provides a description of the kinds of incidents that had occurred over the past two years. Finally, Map 3 disaggregates incidents by category revealing “hot spots” around the country. In each of these maps, the elements of simplicity, clarity, and consistency are evident in the design of each map from the use of colors for each incident, the dark blue background of the country map, the readability of the font and its size, and the overall functionality. From concept to design, this project is elegant and well-executed.
At an intellectual and political moment when hate crime statistics needs more specificity, flexibility, and independence, Mapping Islamophobia offers solid first steps into how we must do more with data collection on hate crimes, and serves as a model of how much more we can do with the visualization of data.