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Visualization of Simulated Urban Spaces: Inferring Parameterized Generation of Streets, Parcels, and Aerial Imagery
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Abstract
Urban simulation models and their visualization are used to help regional planning agencies evaluate alternative
transportation investments, land use regulations, and environmental protection policies. Typical urban simulations provide
spatially distributed data about number of inhabitants, land prices, traffic, and other variables. In this article, we build on a
synergy of urban simulation, urban visualization, and computer graphics to automatically infer an urban layout for any time step
of the simulation sequence. In addition to standard visualization tools, our method gathers data of the original street network,
parcels, and aerial imagery and uses the available simulation results to infer changes to the original urban layout. Our method
produces a new and plausible layout for the simulation results. In contrast with previous work, our approach automatically
updates the layout based on changes in the simulation data and thus can scale to a large simulation over many years. The
method in this article offers a substantial step forward in building integrated visualization and behavioral simulation systems for
use in community visioning, planning, and policy analysis. We demonstrate our method on several real cases using a 200 GB
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database for a 16,300 km area surrounding Seattle, Washington.
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