Modeling Uncertainty in Land Use and Transportation Policy Impacts: Statistical Methods, Computational Algorithms, and Stakeholder Interaction
PI: Adrian Elmes Raftery
Sponsor: Modeling Uncertainty in Land Use and Transportation Policy Impacts: Statistical Methods, Computational Algorithms, and Stakeholder Interaction
Project Period: -
In computational statistics, we are developing , analyzing, and validating techniques for representing and propagating uncertainty through a sophisticated modeling system. Our approach uses promising but preliminary results in Bayesian melding. We propose to develop new statistical methods adapted to the challenges posed by UrbanSim (a sophisticated system to model urban development), which include model stochasticity, large effects of measurement and systematic errors, high dimension of model inputs and outputs, and significant running time for the underlying model. In addition to the statistical challenges, however, undertaking this approach makes extreme computational demands; and achieving acceptable performance will require algorithmic advances, as well as sound software engineering. In human computer interaction, among the research challenges, are supporting meaningful stakeholder access to and interaction with complex situations, including representations of uncertainty. Finally, in the emerging area of science and design, and important question is: how can we design and evaluate the system overall, in a principled way, to support such basic values as accurate presentation of results (including their limitations and uncertainties) and transparency? If we succeed in this work, UrbanSim has the potential to significantly aid in public deliberation over major decisions regarding urban sprawl, economic health, sustainability, and other issues. Our system is Open Source and freely available, and has already attracted considerable interest and use. Further, the results in computational statistics should be applicable to a broad range of simulations of economic or environmental processes to inform public policy development and deliberation. Finally, the interaction techniques and findings should be applicable to a range of other stakeholder interactions with complex models and sources of information.