Using data to better understand and improve cities is no longer a revolutionary new idea. Cities across the world now release data publicly and use data internally to drive better services, social scientists increasingly use “big” data and computation to study urban environments, and civic hacking groups create data-driven websites and apps to inform and benefit communities. But in many ways, these are just the low-hanging fruit of urban data science, which remains a young field with more promise than results.


Inside the small wooden box are several tiny sensors, a cellular modem, a battery, and a micro-processor running custom programming code. But the key innovation for Erica Pereira’s “Lane of Things” device might be the laser-printed cut-out design of the outer enclosure: two circles and a square forming a friendly emoji-like face.


In recent years, city and local governments have increasingly used data to discover innovative new ways to improve their operations and serve their citizens. But the spread of these solutions between and within cities has been limited by obstacles including lack of replicability, resources, and technical expertise.


Urban Center for Computation and Data

The Urban Center for Computation and Data unites scientists from the University of Chicago and Argonne National Laboratory with educators, architects and government officials to capitalize upon the growing availability of city datasets and the emergence of urban sensor networks. The interdisciplinary collaboration will analyze and integrate those data sources and build complex computer models that can anticipate the impact of policy decisions, investments, urban development or other interventions on a city and its residents.

Knowledge Lab logo

Knowledge does not arise from the simple accumulation of facts. Rather, it is a complex, dynamic system, and its emergent outcomes - including scientific consensus - are unpredictable. The complexity of knowledge creation has exploded with the growing number of participating scientists and citizens. If human knowledge is to grow efficiently, we need a deeper understanding of the processes by which knowledge is conceived, validated, shared and reinforced. We need to understand the limits of knowledge in relation to these processes. In short, we need knowledge about knowledge.

The Eric & Wendy Schmidt Data Science for Social Good fellowship is a University of Chicago summer program for aspiring data scientists to work on data mining, machine learning, big data, and data science projects with social impact. Working closely with governments and nonprofits, fellows take on real-world problems in education, health, energy, transportation, and more. For three months in Chicago they apply their coding and analytics skills, collaborate in a fast-paced atmosphere, and learn from mentors coming from industry, academia, and the Obama campaign.

Researcher Spotlight