Shiloh L. Deitz

Assistant Professor of Sociology at Saint Louis University. I study infrastructural inequality and its uneven effects, applying disability justice theory to reimagine data science, AI, and GIS.

My research applies disability justice theory to interrogate how data, AI, and geographic information systems reproduce — and can potentially redress — infrastructural inequality. A through line of this work asks who gets counted in public data systems, who gets erased, and what it would take to build more just alternatives. I draw on spatial analysis, GeoAI, ethnography, and cartography to connect systemic injustice to lived experience.

Critical Disability GIS and GeoAI Ethics

Cities are built for some bodies and not others. This project examines how missing and biased environmental data shapes the lives of disabled people in urban spaces — and what it would mean to reimagine GIS and AI through a disability justice framework. A core contribution of this work is developing the emerging field of Disability GIS: a theoretical and methodological approach that centers disabled experience, challenges ableist assumptions in spatial data science, and builds toward more just geographic tools.

A related thread interrogates the ethics of GeoAI — asking what epistemological commitments underlie AI classification systems, how bias operates in training data, and what replicability and relevance mean in geographic AI research.

Publications

Selected Talks

  • “Missing Ramps, Missing Data: What AI Can and Can’t Tell Us About Getting Around.” Geosaurus Unleashed, T-Rex Innovation Center, St. Louis. March 2026. (invited)
  • “What Were You Thinking? Replicability and Relevance in AI Research.” Technology & Society Conference, Washington University. 2025. (invited)
  • “Disability GIS.” Spatial Data Science Symposium. 2024.

Public Infrastructure and Data Justice

Public infrastructures — housing, water, transportation — are not neutral. They reflect and reproduce racial, economic, and spatial inequalities. This body of work examines how infrastructure data systems make some inequities legible and others invisible, and what more just data practices might look like.

Housing security is the active focus of this thread. Working with the New Jersey State of Affordable Rental Housing project at Rutgers University (funded by the Robert Wood Johnson Foundation), I examined the administrative data systems we rely on to understand affordable housing — asking not just what the data shows, but what histories and inaccuracies are built into it.

Plumbing poverty was the foundational work that established this conceptual lens. Mapping hot spots of racial and geographic inequality in U.S. household water insecurity, this earlier project revealed stark disparities — particularly among Native American communities — that challenge assumptions about baseline infrastructure access in the United States. It remains widely cited in geography, public health, and policy literatures.

Publications

  • Deitz, Shiloh, Will Payne, Eric Seymour, Kathe Newman, and Lauren Nolan. 2024. “Local Landscapes of Assisted Housing: Reconciling Layered and Imprecise Administrative Data for Research Purposes.” Cityscape.
  • Deitz, Shiloh, and Katie Meehan. 2019. “Plumbing Poverty: Mapping Hot Spots of Racial and Geographic Inequality in U.S. Household Water Insecurity.” Annals of the American Association of Geographers. https://doi.org/10.1080/24694452.2018.1530587

Selected Talks

  • “Beyond the Numbers: Examining Administrative Histories and Inaccuracies in U.S. Federally Assisted Housing Data.” AAG Annual Meeting, Detroit. 2025.
  • “Cycles of Investment/Disinvestment and Their Impact on Neighborhoods.” Urban Affairs Association, Vancouver. 2025.
  • “Our House…In the Middle of the Street?” Chertok Lecture Series, Eastern Washington University. 2023. (invited)

Media Coverage