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Curated Track: AAG's GeoEthics Initiative and Related Effort

Description:

High-resolution knowledge of population distributions and characteristics is vitally important for many applications: effectively deploying disaster response, efficiently distributing vaccines, establishing development metrics, and equitably allocating political power and resources. Where direct data about populations are not readily available, population modelers have often turned to ancillary data about patterns in the built environment that can be read at a distance, based on the assumption that the visible traces people leave on the surface of the earth – cultivated and impermeable landcover, roads and other infrastructure, nighttime lights – accurately reflect the people themselves (Leyk et al., 2019). Different types of people build and inhabit landscapes differently, the various quotidian rhythms of their lives shaping and reshaping the spaces they produce (Buzar et al., 2005; Lefebvre, 2004; Wardrop et al., 2018). We know empirically that accounting for such localized spatial heterogeneities in land use and neighborhood morphology, for age and gender distinctions, and for statistically measurable relationships between building characteristics and demographic markers produce more accurate and actionable data products at the scale of policy (Alegana et al., 2015; Gething et al., 2015; Szarka & Biljecki, 2022). As remotely sensed data have become more precise, as new socially sensed data have become available, as more efficient algorithms have been developed, and as computing power has increased, we have been able to strive towards the spatial, temporal and demographic scale of everyday life (Moehl et al., 2021; Woods et al., 2022; Yang et al., 2018).

However, while our ability to assess human-environment interactions has evolved, our methodologies for identifying, categorizing, and quantifying subpopulations from their built environments remain susceptible to ontological, epistemological, and ethical concerns. Ever more precise data may introduce noise, hinder our ability to distinguish real change from data artefacts, and lull us into the belief that in being able to draw more detailed patterns we must necessarily improve our understanding of the processes behind them (Bennett et al., 2022; Comber & Wulder, 2019; Rose & Bright, 2014). The need to mind the duplicitous gap between our view across remotely-sensed, algorithmically-modeled distance and contingent, dynamic, contested life on the ground persists (Bennett et al., 2022; Daniels, 1989; Mitchell, 2002). What of the migrants in motion, the homeless sleeping in doorways, the categories of lived experience that we have failed to qualify or quantify, invisible because we have not learned to see their material traces? What can we really know about people by observing the places they build, through satellite imagery, social media, or census tabulations? Our successes in population modeling show that knowledge is indeed power: knowing where to send vaccines or build schools has real impacts on people’s lives. It is imperative to critically assess our methodologies of knowledge production to be able to mobilize this power appropriately and to best effect.

Works Cited:
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Copyright: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Acknowledgement: This material is based upon the work supported by the U.S. Department of Energy under contract no. DE-AC05-00OR22725.

Non-Presenting Participants