Session: Racial Bias in Ecological Citizen Science
A racial bias is a spatial bias: The impact of majority white participation in citizen science projects
Monday, August 2, 2021
Link To Share This Presentation: https://cdmcd.co/MpQWMJ
Chris Hawn, Geography & Environmental Systems, University of Maryland, Baltimore, MD, Caren Cooper, Forestry & Environmental Resources, NC State University, Raleigh, NC, Sacoby Wilson, Maryland Institute for Applied Environmental Health, University of Maryland, College Park, MD, Erica H. Henry, Applied Ecology, North Carolina State University, Raleigh, NC and Dillon Mahmoudi, Geography and Environmental Systems, University of Maryland, Baltimore County, Baltimore, MD
University of Maryland Baltimore, Maryland, United States
Background/Question/Methods Citizen science harnesses the power of nonscientist observations, oftentimes resulting in a vast network of data that far surpass the capacity of a single researcher. While it has the potential to be a democratic form of science by involving people outside of the ivory tower, the majority of participants very much resemble their academic counterparts in that they are mostly white, affluent, and well-educated. There are many reasons to change this dynamic, however most published examples relate to missed educational benefits of the participants. In this presentation, we investigate the spatial and social gaps present in the unequal participation of citizen science projects in ways that may inhibit the scientific process for scientists and negatively impact public engagement in science. To understand the impact of skewed participation, we examined the census tracts of data collected by two large-scale citizen science projects, the precipitation monitoring project Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) and Falling Fruit, a project dedicated to mapping edible plants in urban areas. To assess the demographics of each sample location, we used the population data from the American Community Survey 2011-2015 and compared that to the location of 46,519 active participant rain gauges collected in the US by CoCoRaHs for 2018. To assess Falling Fruit, we compared the 14 edible fruit trees identified in the project in Baltimore, MD with those same species in Baltimore City’s street trees database. Results/Conclusions We show that these citizen science projects that are known to have predominantly white participants collect data almost exclusively in white dominated landscapes. CoCoRaHS data was overwhelmingly collected in majority white census tracts in both urban and rural environments. Baltimore City data showed fruit trees throughout the city, but Falling Fruit identified edible trees mostly in majority white neighborhoods. The trees that Falling Fruit participants identified in majority Black neighborhoods were in census tracts with green spaces. The implications for the white-centered collection of data are paramount in a country with a deep legacy of racial segregation. The oppression by race and class has produced functionally different ecologies like the luxury effects of increased biodiversity in affluent neighborhoods. The use of racially skewed data for managing natural resources and climate resilience can further deepen the burden of environmental injustices experienced by oppressed groups. Centering marginalized communities across all spatially explicit ecological citizen science projects may be necessary to manage resources equitably in the future.