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dc.contributor.authorGriffin, Amy L.
dc.contributor.authorSpielman, Seth
dc.contributor.authorJurjevich, Jason
dc.contributor.authorMerrick, Meg
dc.contributor.authorNagle, Nicholas N.
dc.contributor.authorFolch, David
dc.date.accessioned2017-05-27T13:36:07Z
dc.date.available2017-05-27T13:36:07Z
dc.date.issued2015-05-04
dc.identifier.urihttps://hdl.handle.net/1813/50064
dc.description.abstractRecent changes to the US Census have led to more timely updates of demographic statistics that are used in the delivery and planning of many social and environmental programs. However, this timeliness has a tradeoff: increased uncertainty in the estimates for small area geographies such as census blocks and tracts. Although the Census Bureau publishes information about the uncertainty of the estimates, few end users engage with and utilize this information, perhaps because it comes in a difficult to use form; another column in a table with many columns. Many techniques for visualising uncertainty in attribute data have been proposed, but few have been empirically tested, and fewer still with real end users using an ecologically valid task. Here, we report on a broader research program directed to studying the visualisation of attribute uncertainty for ACS data, and report the results of an experiment undertaken with 55 urban planners in which they had to make spatial decisions using uncertain demographic estimates. We compared visualisation methods based on two metaphors for communicating uncertainty: the stoplight and sketchiness. The experimental task is one taken from a context of use study we conducted on urban planning. It required planners to define an area of contiguous census tracts that meets a particular threshold with respect to the attribute in question: percentage of households in poverty. We conclude with some thoughts about how to help urban planners work with uncertainty in ACS data more effectively. (joint work with Jason Jurjevich, Portland State University, Meg Merrick, Portland State University, Seth E Spielman, Colorado University at Boulder, Nicholas N Nagle, University of Tennessee-Knoxville, David C Folch, Florida State University)en_US
dc.description.sponsorshipThis material is based upon work supported by the National Science Foundation under Grant Number 1132008en_US
dc.language.isoenen_US
dc.titleVisualizing Attribute Uncertainty in the ACS: An Empirical Study of Decision-Making with Urban Plannersen_US
dc.typepresentationen_US


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