Urban environmental monitoring campaigns depend on expertise from city agencies, residents, and researchers. Deployment efforts rarely include all three stakeholders, typically leading to initiatives that struggle to produce credible, actionable data. We describe the implementation of a large-scale, long-term air quality sensing network in Chicago Illinois; detail stakeholder interviews and meetings; and present three interfaces—–a website accessible via in-situ QR codes, APIs, and a mobile, mixed-media experience. We show how a collaborative approach created a more equitable sensor distribution compared to crowdsourced or regulatory designs. We highlight shared goals of education, engagement, and empowerment despite the diversity of tool and analytics needs across stakeholder groups. Reflecting on our work, we develop a “three-legged stool” framework representing the criticality of balanced participation from three key stakeholder groups—city, community, and research—in deploying novel urban technologies. This approach can help HCI researchers facilitate more democratic technology deployments in urban spaces.

Lead poisoning is a major public health problem that affects hundreds of thousands of children in the United States every year. A common approach to identifying lead hazards is to test all children for elevated blood lead levels and then investigate and remediate the homes of children with elevated tests. This can prevent exposure to lead of future residents, but only after a child has been poisoned. This paper describes joint work with the Chicago Department of Public Health (CDPH) in which we build a model that predicts the risk of a child to being poisoned so that an intervention can take place before that happens. Using two decades of blood lead level tests, home lead inspections, property value assessments, and census data, our model allows inspectors to prioritize houses on an intractably long list of potential hazards and identify children who are at the highest risk. This work has been described by CDPH as pioneering in the use of machine learning and predictive analytics in public health and has the potential to have a significant impact on both health and economic outcomes for communities across the US.