Robust abnormality detection methods for spatial search of radioactive materials

Myeong Hun Jeong, Clair J. Sullivan, Yizhao Gao, Shaowen Wang

Research output: Contribution to journalArticle

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 (G * i ) statistic, and the goodness of fit of the Poisson distribution.

Original languageEnglish (US)
JournalTransactions in GIS
DOIs
StatePublished - Jan 1 2019

Fingerprint

detection method
abnormality
outlier
public health
national security
sensor
material
alarm
detection
method
radiation

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Cite this

Robust abnormality detection methods for spatial search of radioactive materials. / Jeong, Myeong Hun; Sullivan, Clair J.; Gao, Yizhao; Wang, Shaowen.

In: Transactions in GIS, 01.01.2019.

Research output: Contribution to journalArticle

@article{818422d2ca2a426b9d89d7207396e822,
title = "Robust abnormality detection methods for spatial search of radioactive materials",
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 (G * i ) statistic, and the goodness of fit of the Poisson distribution.",
author = "Jeong, {Myeong Hun} and Sullivan, {Clair J.} and Yizhao Gao and Shaowen Wang",
year = "2019",
month = "1",
day = "1",
doi = "10.1111/tgis.12533",
language = "English (US)",
journal = "Transactions in GIS",
issn = "1361-1682",
publisher = "Wiley-Blackwell",

}

TY - JOUR

T1 - Robust abnormality detection methods for spatial search of radioactive materials

AU - Jeong, Myeong Hun

AU - Sullivan, Clair J.

AU - Gao, Yizhao

AU - Wang, Shaowen

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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 (G * i ) statistic, and the goodness of fit of the Poisson distribution.

AB - 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 (G * i ) statistic, and the goodness of fit of the Poisson distribution.

UR - http://www.scopus.com/inward/record.url?scp=85065676797&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065676797&partnerID=8YFLogxK

U2 - 10.1111/tgis.12533

DO - 10.1111/tgis.12533

M3 - Article

JO - Transactions in GIS

JF - Transactions in GIS

SN - 1361-1682

ER -