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Multiscale Spatial Density Smoothing: An Application to Large-Scale Radiological Survey and Anomaly Detection
by
Tansey, Wesley
, Scott, James G.
, Reinhart, Alex
, Athey, Alex
in
algorithms
/ Applications and Case Studies
/ Bayesian nonparametrics
/ data collection
/ Density estimation
/ energy
/ equations
/ Fused lasso
/ gamma radiation
/ medical facilities
/ military lands
/ public health
/ Radiological survey
/ Spatial statistics
/ statistics
/ surveys
/ Total-variation denoising
/ wavelet
2017
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Multiscale Spatial Density Smoothing: An Application to Large-Scale Radiological Survey and Anomaly Detection
by
Tansey, Wesley
, Scott, James G.
, Reinhart, Alex
, Athey, Alex
in
algorithms
/ Applications and Case Studies
/ Bayesian nonparametrics
/ data collection
/ Density estimation
/ energy
/ equations
/ Fused lasso
/ gamma radiation
/ medical facilities
/ military lands
/ public health
/ Radiological survey
/ Spatial statistics
/ statistics
/ surveys
/ Total-variation denoising
/ wavelet
2017
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Do you wish to request the book?
Multiscale Spatial Density Smoothing: An Application to Large-Scale Radiological Survey and Anomaly Detection
by
Tansey, Wesley
, Scott, James G.
, Reinhart, Alex
, Athey, Alex
in
algorithms
/ Applications and Case Studies
/ Bayesian nonparametrics
/ data collection
/ Density estimation
/ energy
/ equations
/ Fused lasso
/ gamma radiation
/ medical facilities
/ military lands
/ public health
/ Radiological survey
/ Spatial statistics
/ statistics
/ surveys
/ Total-variation denoising
/ wavelet
2017
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Multiscale Spatial Density Smoothing: An Application to Large-Scale Radiological Survey and Anomaly Detection
Journal Article
Multiscale Spatial Density Smoothing: An Application to Large-Scale Radiological Survey and Anomaly Detection
2017
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Overview
We consider the problem of estimating a spatially varying density function, motivated by problems that arise in large-scale radiological survey and anomaly detection. In this context, the density functions to be estimated are the background gamma-ray energy spectra at sites spread across a large geographical area, such as nuclear production and waste-storage sites, military bases, medical facilities, university campuses, or the downtown of a city. Several challenges combine to make this a difficult problem. First, the spectral density at any given spatial location may have both smooth and nonsmooth features. Second, the spatial correlation in these density functions is neither stationary nor locally isotropic. Finally, at some spatial locations, there are very little data. We present a method called multiscale spatial density smoothing that successfully addresses these challenges. The method is based on recursive dyadic partition of the sample space, and therefore shares much in common with other multiscale methods, such as wavelets and Pólya-tree priors. We describe an efficient algorithm for finding a maximum a posteriori (MAP) estimate that leverages recent advances in convex optimization for nonsmooth functions.
We apply multiscale spatial density smoothing to real data collected on the background gamma-ray spectra at locations across a large university campus. The method exhibits state-of-the-art performance for spatial smoothing in density estimation, and it leads to substantial improvements in power when used in conjunction with existing methods for detecting the kinds of radiological anomalies that may have important consequences for public health and safety.
Publisher
Taylor & Francis,Taylor & Francis Group,LLC
Subject
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