Problems with quantitative categorization: An argument for qualitative approaches

Abstract

As data science gains traction, it often brings quantitative approaches and positivist epistemologies. While these can generate powerful insights, we argue for methodological hybridity in modern data science. We demonstrate the power of complementary qualitative approaches and flexible ontologies. Using an example of classifying segments™ on Strava, neither quantitative nor qualitative approaches alone were adequate to meaningfully classify segments, but together allowed accurate, useful, and intuitive categories to emerge. Drawing on this experience, we discuss qualitative data science and argue the ontological discussions within Critical GIS from the 1990s and 2000s are increasingly relevant and informative amidst our platial paradigms.

A cloud‐based solution for trustless indigenous data sovereignty: Protecting Māori biodiversity management data in Aotearoa New Zealand

“…In mapping, there is the additional burden of learning to understand the world from above. The god‐trick (Haraway, 1988; Harley, 1989; Monmonier, 2018) offer good examples and frameworks for understanding how knowledge and power are embedded into maps, and there are several examples of how modern technologies and algorithms continue this trend (Kitchin & Dodge, 2014; Martin et al., 2023; Martin & Schuurman, 2017, 2020). In the specific case of indigenous knowledge, we recognize there are additional barriers to technological adoption with emergent technologies, similar to those reported by Tudge (2011) and Sieber et al.…”