
Closer than they appear: A Bayesian perspective on individuallevel heterogeneity in risk assessment
Risk assessment instruments are used across the criminal justice system ...
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Irrational Exuberance: Correcting Bias in Probability Estimates
We consider the common setting where one observes probability estimates ...
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Imprecise Probability for Multiparty Session Types in Process Algebra
In this paper we introduce imprecise probability for session types. More...
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On The RadonNikodym Spectral Approach With Optimal Clustering
Problems of interpolation, classification, and clustering are considered...
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Pixelate to communicate: visualising uncertainty in maps of disease risk and other spatial continua
Maps have long been been used to visualise estimates of spatial variable...
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Pignistic Probability Transforms for Mixes of Low and HighProbability Events
In some real world information fusion situations, time critical decision...
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Presenting the Probabilities of Different Effect Sizes: Towards a Better Understanding and Communication of Statistical Uncertainty
How should social scientists understand and communicate the uncertainty ...
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The FLOod Probability Interpolation Tool (FLOPIT): Improving Spatial Flood Probability Quantification and Communication Through Higher Resolution Mapping
Understanding flood probabilities is essential to making sound decisions about floodrisk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling realestate, floodproofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example the 1 in 100 or 1 in 500year flood zones) to communicate flooding probabilities. However, this choice of communication format can miss important details and lead to biased risk assessments. Here we develop, test, and demonstrate the FLOod Probability Interpolation Tool (FLOPIT). FLOPIT interpolates flood probabilities between water surface elevation to produce continuous floodprobability maps. We show that FLOPIT can be relatively easily applied to existing datasets used to create flood zones. Using publicly available data from the Federal Emergency Management Agency (FEMA) flood risk databases as well as state and national datasets, we produce continuous floodprobability maps at three example locations in the United States: Houston (TX), Muncy (PA), and Selinsgrove (PA). We find that the discrete flood zones generally communicate substantially lower flood probabilities than the continuous estimates.
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