Perkins, Deja, Dillon Mahmoudi, Erica Henry, and Caren Cooper. 2026. “Geography of race and income shape spatial data gaps in two national participatory science projects.” People and Nature, 8(5), 1485–1498. doi.org/10.1002/pan3.70307.
Abstract
- Context and Need: Participatory projects where people contribute geo-referenced biodiversity data, like eBird and iNaturalist, are commonly used tools to enhance the data collection capacity for research, management, and environmental learning. Despite their utility, demographic disparities in participation, demographic patterns of residential segregation, and individual choice of participation may skew data, influencing research outcomes, management decisions, and the distribution of project benefits.
- Approach and Methods: We used hurdle models to analyse the occurrence and abundance of volunteer observations from 2018 to eBird and iNaturalist across the United States (n = 90 million) in relation to the racial composition and median household income levels of Census Tracts (approximately 1200–8000 residents per tract) in 2018. We ran models separately for urban and rural areas to account for different patterns of residential demographic segregation and the geographic concentration of racial and income groups relative to tract size.
- Main Results: The geography of race and income was a significant predictor of eBird and iNaturalist locations, respectively. In urban areas, the eBird locations were significantly less likely to occur and less abundant in tracts that were predominantly Black Indigenous and People of Color (BIPOC). In urban and rural areas, iNaturalist locations were significantly less likely to occur and less abundant in tracts that were predominantly low-income residents.
- Synthesis and Applications: Our analysis explicitly examined whether the geography of people aligns with the geography of contributory science participation, highlighting how socio-spatial processes shape both demographic patterns and data coverage. This unevenness can distort scientific inferences, limit the effectiveness of participatory science in addressing local ecological and environmental management needs and lead to the inequitable distribution of benefits. In addition, areas with less data may have lower attraction for participants, and/or project structures may (inadvertently) exclude people of colour and those with lower income. If these patterns occur in similar participatory projects, which number in the thousands in the US, then contributory projects may inadvertently be creating data-rich and data-poor areas. We recommend future studies disaggregate participation data by specific racial communities because solutions may not be one-size-fits-all.
Summary
This paper shows how the spatial logics of race and income quietly structure where “participatory science” data actually get made, and where they are conspicuously absent. Drawing on more than 90 million eBird and iNaturalist observations across the United States, the paper demonstrates that these marquee biodiversity platforms are not neutral infrastructures but deeply entangled with residential segregation, wealth stratification, and the long geographic afterlives of white supremacy. In doing so, the paper reframes contributory science as part of the broader machinery that can either reproduce or contest environmental injustice, depending on whose places count as data-rich and whose are systematically ignored.
Contributory projects may inadvertently be creating data-rich and data-poor areas, amplifying their perceived value in whiter, more affluent spaces while diminishing attention to less privileged places.
Methodologically, the paper uses hurdle models to assess both the presence and abundance of observations relative to census tract racial composition and median household income, disaggregating results for urban and rural contexts. This design moves beyond participation headcounts to ask whether the geography of people aligns with the geography of data, and how that alignment shifts across different settlement types. The decision to hold race and income together, but analytically distinguish their spatial effects, makes visible how different socio-spatial processes (segregation, deindustrialization, incarceration, and uneven development) converge into patterned data gaps.
Empirically, the paper finds that urban eBird data are significantly less likely to occur, and are less abundant, in tracts that are predominantly Black, Indigenous, and people of color, while both urban and rural iNaturalist data are significantly underrepresented in lower-income tracts. These patterns show that the much-celebrated democratization of biodiversity science rests on a profoundly uneven substrate: contributory projects help produce data-rich and data-poor geographies that map uncomfortably well onto racialized and classed landscapes. Because environmental data operate as a form of social capital—mobilized to justify investments, shape regulations, or support “evidence-based” planning—these gaps risk amplifying existing inequalities in environmental governance.
If contributory science projects are not collectively creating an equitable distribution of data, then they are exacerbating environmental injustice rather than democratizing knowledge production.
Ultimately, we argue in this paper that such data gaps are not incidental noise but the cumulative outcome of white supremacy culture, colonial land-ordering, and project designs that privilege whiter, more affluent spaces as default sites of scientific value. We call for disaggregating participation data by specific minoritized communities, rejecting the flattening effects of “BIPOC” as a catch-all category, and insists that any attempt to “fix” these gaps must be community-specific and historically grounded. In this way, the paper situates eBird and iNaturalist as exemplars of a broader field of contributory projects whose compounded spatial biases constitute an environmental justice problem in their own right.