Assessing and integrating uncertainty into land-use forecasting

Loading...
Thumbnail Image

Persistent link to this item

Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Assessing and integrating uncertainty into land-use forecasting

Published Date

2015

Publisher

Journal of Transport and Land Use

Type

Article

Abstract

Uncertainty in land use and transportation modeling has received increasing attention in the past few years. However, methods for quantifying uncertainty in such models are usually developed in an academic environment and in most cases do not reach users of official forecasts, such as planners and policymakers. In this paper, we describe the practical application of a methodology called Bayesian melding and its integration into the land-use forecast published by the Puget Sound Regional Council, a metropolitan planning organization. The method allows practitioners to assess uncertainty about forecasted quantities, such as households, population, and jobs, for each geographic unit. Users are provided with probability intervals around forecasts, which add value to model validation, scenario comparison, and external review and comment procedures. Practical issues such as how many runs to use or assessing uncertainty for aggregated regions are also discussed.

Description

Related to

Replaces

License

Series/Report Number

Funding information

Isbn identifier

Doi identifier

10.5198/jtlu.2015.614

Previously Published Citation

Suggested citation

Ševčíková, Hana; Simonson, Mark; Jensen, Michael. (2015). Assessing and integrating uncertainty into land-use forecasting. Retrieved from the University Digital Conservancy, 10.5198/jtlu.2015.614.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.