Counter‐mapping data science

Abstract

Counter‐mapping is a combination of critical ideas and practices for social change that offers a productive and promising approach for grassroots data science initiatives. Current information technologies collect, store, and analyze data with new degrees of size, speed, heterogeneity, and detail. While much work utilizing data science technologies is dedicated to generating profit or to national security, some data science projects explicitly attempt to facilitate new social relations, though with inconsistent results and consequences. This paper reviews counter‐mapping's particular combination of theory and practice as a potential point of reference for such initiatives. Counter‐mapping takes the tools of institutional map‐making at government agencies and corporations and applies them in situated, bottom‐up ways. Moreover, counter‐mapping's multiple theoretical approaches and polyglot practices offer a variety of inspirations and avenues for future work in identifying and realizing alternative, ideally better, possibilities. This paper defines counter‐mapping; outlines its multiple theorizations; briefly describes three relevant case studies, The Detroit Geographical Expedition and Institute, Mapping Police Violence, and the Counter‐Cartographies Collective; and concludes with a few hard‐learned considerations from counter‐mapping that are directly pertinent for data‐oriented projects focused on change.