Understanding Cities through Data and Urban Informatics.

The Lab's research uses data-driven models and computational methods to understand the interactions of physical, natural, and social systems in multi-scalar urban environments, from the building to the neighborhood to the city. We are focused on a simple, but significant question: how does the the urban built environment impact human and social behavior, resource consumption, and well-being?

Our research is grounded in the acquisition, integration, and analysis of big data from a diverse range of sources – from urban sensing arrays that we have developed to city administrative records and social media data – across real-world test-beds at the building, community, and city scale. Our current research program extends across three distinct, but inter-related, research areas:

  1. Urban energy dynamics. How do physical infrastructure, socio-economic conditions, local ecology, and human behavior impact energy use and air quality in cities? How can we utilize data-driven, evidenced-based design and planning strategies to maximize energy efficiency at the building and district scale, while reducing air pollution and improving public health?
  2. Urban social-ecological-technical system dynamics. What makes an urban place “successful”? Can we quantify the factors that drive human activity and mobility in diverse urban environments? Why and how does the environment in which you live affect your quality-of-life?
  3. Data-driven city operations and planning. How can cities use data to drive operational and policy decisions and enable a more complete understanding of service demands and resource consumption?

 

 

Recent Projects

Energy Cost Burdens and Housing Affordability for Low-Income Households

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DATA-IN-MOTION: CREATING “DATA DRILLS” TO IMPROVE REAL-TIME INTER-AGENCY EMERGENCY RESPONSE

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Estimating Bias in 311 Service Request Data

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Analyzing the Impacts of Urban Land Use Dynamics

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Benchmarking, Feedback, and Behavior Change for Commercial Office Building Tenants

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Data Quality and Reliability Metrics for Building Energy Disclosure Data

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CommunitySense

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Predicting Building-level Waste Generation and Recycling

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Machine Learning Approaches to Predict and Target Building Energy Retrofits

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THE ‘ENERGY SNAPSHOT’ – USING BIG DATA ANALYTICS TO EVALUATE BUILDING ENERGY PERFORMANCE IN NYC

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Using LiDAR to Analyze the Impact of Urban Morphology on Energy Use

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The Resilience to Emergencies and Disasters Index (REDI)

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The QC Urban Sensing Array

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