Data Literacy for Data Stewards Territorial Stigmatization Workshop

by Bob Gradeck

November 3, 2022

Special thanks to Adena Bowden for co-authoring this post.

This past Friday (October 28, 2022), we continued our twelve-week virtual data literacy series, “Data Literacy for Data Stewards,” with a conversation about stigmatization in data. Our goals for the workshop were to learn about the impacts of territorial stigmatization, and work together to create practices we can adopt to de-stigmatize communities.  

We drew on a NYT article by Tressie McMillan Cottom, “What’s Shame Got to Do With It?” to define and begin our conversation about stigma. She defines stigma as a label imposed on people and communities from the outside. Stigmatization is a way of denying or taking away power by “othering” communities and people associated with them.  

In our workshop, we focused on territorial stigmatization – how people in society interact with one another based on their connection to where their community fits into a spatial hierarchy and how this form of stigmatization often obscures the structural causes of inequality through the institutionalization and perpetuation of racism, classism, settler colonialism, and discrimination. We looked at examples  that perpetuate this form of stigmatization, including maps of historical redlining practices, maps highlighting crime hotspots, and a map of “Featured Neighborhoods,” which excluded communities of color and communities that are disproportionately affected by poverty and environmental pollution.  

The impacts of territorial stigmatization are serious and extensive. Stigma defines how systems interact with people, often rendering people connected to stigmatized places powerless or invisible. Treating communities as persistently inferior legitimizes continued injustice, discrimination, and harm. 

So, how do we stop stigmatizing places? First, we must build empathy and challenge assumptions that we hold about places and the communities that live there. Additionally, we must understand and acknowledge the structural causes and discriminatory practices tied to the issue at hand. After we’ve challenged our assumptions and learned more about structural causes of inequity across communities, we can de-stigmatize maps and other data visualizations (see de-stigmatizing examples in the hyperlinks): 

We asked our participants to work together in breakout rooms and co-create their own list of strategies for de-stigmatizing a particular data visualization. Some of the ideas that participants put together to de-stigmatize the map of vacant and red-flag properties in Chicago included: 

  • Reframing locations as opportunities for investment and revitalization; 
  • Adding layers to maps to show underlying structural causes of the data presented;  
  • Displaying population trends; 
  • Highlighting positive uses of land, like community gardens; 
  • Telling the story in terms of who is responsible for vacant and abandoned properties; 
  • Rethinking language, use of color, legend features, and geographical boundaries; 
  • Specifying the intended audience and considering secondary audiences; 
  • Adding context by including text boxes, overlaying other information, or presenting a different perspective 

In our next workshop, we will talk about the importance of having contextual information about data that we work with.  Without context, it can be difficult to know who is creating data and how data is created. Without context, it can also be tough to know how data may be biased, inaccurate, or incomplete. A lack of context can also reinforce the status-quo.  In this workshop, participants will work together to develop questions that can be asked to better-understand data. We’ll also share ideas in how the process of inquiry can be used to challenge power.  

If you are interested in participating in the next cohort of our Data Literacy for Data Stewards peer learning series starting in the first quarter of 2023, email us at wprdc@pitt.edu and we will let you know when registration is open.