Our cities and infrastructure systems provide essential services (energy, transport, water, and communications) that greatly influence social well-being and sustainability.
Canada alone intends to invest around $80 billion into public infrastructure over the next decade. This requires long-term planning to strategically invest in priority areas that enhance economic growth, create jobs, improve well-being and advance environmental sustainability.
Here at the Urban Predictive Analytics Lab in the School of Community and Regional Planning, Faculty of Applied Science, University of British Columbia, we combine computational methods, data analytics and systems-theoretic approaches to model, analyze and simulate interdependency between infrastructure, technology, people and the environment in urban settings. We are particularly interested in understanding how these factors will play out in the emerging smart cities movement. We are driven by the following research challenges and questions:
Who will benefit from new technology and data to ensure inclusiveness, equity and quality of life?
To what extent can we predict future impacts, manage risk, and transition to sustainable cities?
How can we inform decision-making, policy and investment strategies in the face of complexity and uncertainty?
To address these challenges our core areas of competence include:
Data-Driven Scenario Planning - we develop data intensive scenarios to understand the current conditions and possible future trajectories of urban populations, technology and infrastructure.
Predictive Modelling and Simulation - we build advanced management systems to collect, curate and process massive data sets, and apply high performance computing to predict the consequences of future urban trajectories and scenarios.
Evidence-Based Policy Analysis - we use our results to educate technical leaders, engage industry and community stakeholders, and inform infrastructure and technology investment decisions to achieve positive societal and environmental outcomes.