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Abstract A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals^1–6. Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than real-life exposures among people at work, in places of leisure and in home neighbourhoods^7,8. Here we develop a measure of exposure segregation that captures the socioeconomic diversity of these everyday encounters. Using mobile phone mobility data to represent 1.6 billion real-world exposures among 9.6 million people in the United States, we measure exposure segregation across 382 metropolitan statistical areas (MSAs) and 2,829 counties. We find that exposure segregation is 67% higher in the ten largest MSAs than in small MSAs with fewer than 100,000 residents. This means that, contrary to expectations, residents of large cosmopolitan areas have less exposure to a socioeconomically diverse range of individuals. Second, we find that the increased socioeconomic segregation in large cities arises because they offer a greater choice of differentiated spaces targeted to specific socioeconomic groups. Third, we find that this segregation-increasing effect is countered when a city’s hubs (such as shopping centres) are positioned to bridge diverse neighbourhoods and therefore attract people of all socioeconomic statuses. Our findings challenge a long-standing conjecture in human geography and highlight how urban design can both prevent and facilitate encounters among diverse individuals. 

Tribal Constitutions, Citing Slavery, and Petitioning for Freedom are digital legal history projects focused on expressions of sovereignty within tribal constitutions, the remnants of slavery in modern law, and the underexamined role of habeas petitioners in challenging coercion and confinement in the long-nineteenth-century United States. Each project deploys legal databases differently, but with the shared goal of contributing key insights to legal historical scholarship and offering interfaces that appeal to a broad, public audience. 

Mobility restrictions have been a primary intervention for controlling the spread of COVID-19, but they also place a significant economic burden on individuals and businesses. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an interactive dashboard that communicates our model's predictions for thousands of potential policies. 

Despite decades of climate science research, existing climate actions have had limited impacts on mitigating climate change. Efforts to reduce emissions, for example, have yet to spur sufficient action to reduce the most severe effects of climate change. We draw from our experiences as Ojibwe knowledge holders and community members, scientists, and scholars to demonstrate how the lack of recognition of traditional knowledges (TK) within climate science constrains effective climate action and exacerbates climate injustice. Often unrecognized in science and policy arenas, TK generates insights into how justice-driven climate action, rooted in relational interdependencies between humans and older-than-human relatives, can provide new avenues for effectively addressing climate change. We conclude by arguing for a shift toward meaningful and respectful inclusion of plural knowledge systems in climate governance.