“Thick Mapping” for Environmental Justice

This chapter introduces an ongoing project which brings United States Environmental Protection Agency (EPA) data into literary space to support the reading of texts through a critical environmental justice lens. By combining natural language processing (NLP) and named-entity recognition (NER), this project maps sample texts using the GIS platform ArcGIS, which are then integrated into the EPA's EJScreen interface to identify overlapping indexes of environmental risk and inequality. This provides greater context for situating both historical and speculative readings within otherwise non-representational geographies. Moreover, it puts empirical information and digital methods at the service of interpretive reason rather than subordinating or displacing authors, readers, and researchers from their role as agents in the collective project to describe and change a deeply unequal world. To this end, this chapter brings together spatial theory and critical environmental justice frameworks and considers how such “thick mapping” can enrich our understanding of the lived and narrated world.