Bias and Fairness in Data-Driven Decision-Making
Our work addresses fairness and bias in urban analytics by identifying sources of data and algorithmic bias, providing tools to city policymakers and planners to understand and visualize the spatial and socioeconomic dependence of identified biases, and addressing the fairness of algorithmic decisions with the goal of more just and equitable cities.
Data for Climate Action
Our work enables city leaders and urban policymakers to implement evidenced-based climate-action policies based on rigorous scientific models that can help to achieve long-term sustainability and environmental-justice goals.
The Dynamics of Urban Mobility Behavior
Given the increasing density of and demand for urban space, new methods are needed to quantify real-time, localized populations and to understand how mobility behavior is impacted by exogenous shocks, such as natural disasters and pandemics, and neighborhood change driven by economic, social, and cultural shifts.
The Neighborhood Exposome
Through partnerships with community organizations and city agencies, we harness observational and participatory data to build data-driven models of neighborhood dynamics and support direct engagement with residents for community-led, data-informed planning processes.