@article{ref1,
title="Robust abnormality detection methods for spatial search of radioactive materials",
journal="Transactions in GIS",
year="2019",
author="Jeong, Myeong-Hun and Sullivan, Clair J. and Gao, Yizhao and Wang, Shaowen",
volume="23",
number="4",
pages="860-877",
abstract="Radiological dirty bombs and improvised nuclear devices pose a significant threat to both public health and national security. Growing networks of radiation sensors have been deployed by a number of offices within the U.S. and international agencies. Detecting such threats while minimizing false alarm rates presents a considerable challenge to homeland security and public health. This research aims to achieve a higher probability of detection with a lower probability of false alarms. It focuses on the local spatial instability of radiation levels in order to detect radioactive materials based on robust outlier detection methods. Our approach includes a three-step abnormality detection method consisting of one-dimensional robust outlier detection for all gamma-ray counts, a density-based clustering analysis, and a two-dimensional robust outlier detection method using a bagplot, based on spatial associations. The effectiveness of the method proposed is demonstrated through a case study, wherein radioactive materials are detected in urban environments, and its performance is compared with alternative methods employing a k-sigma approach, local Getis-Ord () statistic, and the goodness of fit of the Poisson distribution.<p /> <p>Language: en</p>",
language="en",
issn="1361-1682",
doi="10.1111/tgis.12533",
url="http://dx.doi.org/10.1111/tgis.12533"
}