Citation

Jeong MH, Sullivan CJ, Gao Y, Wang S. Trans. GIS 2019; 23(4): 860-877.

Copyright

(Copyright © 2019, John Wiley and Sons)

DOI

10.1111/tgis.12533

PMID

unavailable

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.

Language: en