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Title: Spatial Estimation of Populations at Risk from Radiological Dispersion Device Terrorism Incidents

Abstract

Delineation of the location and size of the population potentially at risk of exposure to ionizing radiation is one of the key analytical challenges in estimating accurately the severity of the potential health effects associated with a radiological terrorism incident. Regardless of spatial scale, the geographical units for which population data commonly are collected rarely coincide with the geographical scale necessary for effective incident management and medical response. This paper identifies major government and commercial open sources of U.S. population data and presents a GIS-based approach for allocating publicly available population data, including age distributions, to geographical units appropriate for planning and implementing incident management and medical response strategies. In summary: The gravity model offers a straight-forward, empirical tool for estimating population flows, especially when geographical areas are relatively well-defined in terms of accessibility and spatial separation. This is particularly important for several reasons. First, the spatial scale for the area impacted by a RDD terrorism event is unlikely to match fully the spatial scale of available population data. That is, the plume spread typically will not uniformly overlay the impacted area. Second, the number of people within the impacted area varies as a function whether an attack occurs duringmore » the day or night. For example, the population of a central business district or industrial area typically is larger during the day while predominately residential areas have larger night time populations. As a result, interpolation techniques that link population data to geographical units and allocate those data based on time-frame at a spatial scale that is relevant to enhancing preparedness and response. The gravity model's main advantage is that it efficiently allocates readily available, open source population data to geographical units appropriate for planning and implementing incident management and medical monitoring strategies. The importance of being able to link population estimates to geographic areas during the course of an RDD incident can be understood intuitively: - The spatial distribution of actual total dose equivalents of ionizing radiation is likely to vary due to changes in meteorological parameters as an event evolves over time; - The size of the geographical area affected also is likely to vary as a function of the actual release scenario; - The ability to identify the location and size of the populations that may be exposed to doses of ionizing radiation is critical to carrying out appropriate treatment and post-event medical monitoring; - Once a spatial interaction model has been validated for a city or a region, it can then be used for simulation and prediction purposes to assess the possible human health consequences of different release scenarios. (authors)« less

Authors:
;  [1]
  1. Center for Biosecurity Research, University of Oklahoma Health Sciences Center, Oklahoma City, OK (United States)
Publication Date:
Research Org.:
WM Symposia, 1628 E. Southern Avenue, Suite 9 - 332, Tempe, AZ 85282 (United States)
OSTI Identifier:
21326153
Report Number(s):
INIS-US-10-WM-08408
TRN: US10V0595067518
Resource Type:
Conference
Resource Relation:
Conference: WM'08: Waste Management Symposium 2008 - HLW, TRU, LLW/ILW, Mixed, Hazardous Wastes and Environmental Management - Phoenix Rising: Moving Forward in Waste Management, Phoenix, AZ (United States), 24-28 Feb 2008; Other Information: Country of input: France; 41 refs
Country of Publication:
United States
Language:
English
Subject:
61 RADIATION PROTECTION AND DOSIMETRY; ACCIDENTS; BIOLOGICAL RADIATION EFFECTS; CRIME; DOSE EQUIVALENTS; GEOGRAPHIC INFORMATION SYSTEMS; HAZARDS; IONIZING RADIATIONS; MEN; MONITORING; PUBLIC HEALTH; RADIATION DOSES; RADIOLOGICAL DISPERSAL DEVICES; SIMULATION

Citation Formats

Regens, J L, and Gunter, J T. Spatial Estimation of Populations at Risk from Radiological Dispersion Device Terrorism Incidents. United States: N. p., 2008. Web.
Regens, J L, & Gunter, J T. Spatial Estimation of Populations at Risk from Radiological Dispersion Device Terrorism Incidents. United States.
Regens, J L, and Gunter, J T. 2008. "Spatial Estimation of Populations at Risk from Radiological Dispersion Device Terrorism Incidents". United States.
@article{osti_21326153,
title = {Spatial Estimation of Populations at Risk from Radiological Dispersion Device Terrorism Incidents},
author = {Regens, J L and Gunter, J T},
abstractNote = {Delineation of the location and size of the population potentially at risk of exposure to ionizing radiation is one of the key analytical challenges in estimating accurately the severity of the potential health effects associated with a radiological terrorism incident. Regardless of spatial scale, the geographical units for which population data commonly are collected rarely coincide with the geographical scale necessary for effective incident management and medical response. This paper identifies major government and commercial open sources of U.S. population data and presents a GIS-based approach for allocating publicly available population data, including age distributions, to geographical units appropriate for planning and implementing incident management and medical response strategies. In summary: The gravity model offers a straight-forward, empirical tool for estimating population flows, especially when geographical areas are relatively well-defined in terms of accessibility and spatial separation. This is particularly important for several reasons. First, the spatial scale for the area impacted by a RDD terrorism event is unlikely to match fully the spatial scale of available population data. That is, the plume spread typically will not uniformly overlay the impacted area. Second, the number of people within the impacted area varies as a function whether an attack occurs during the day or night. For example, the population of a central business district or industrial area typically is larger during the day while predominately residential areas have larger night time populations. As a result, interpolation techniques that link population data to geographical units and allocate those data based on time-frame at a spatial scale that is relevant to enhancing preparedness and response. The gravity model's main advantage is that it efficiently allocates readily available, open source population data to geographical units appropriate for planning and implementing incident management and medical monitoring strategies. The importance of being able to link population estimates to geographic areas during the course of an RDD incident can be understood intuitively: - The spatial distribution of actual total dose equivalents of ionizing radiation is likely to vary due to changes in meteorological parameters as an event evolves over time; - The size of the geographical area affected also is likely to vary as a function of the actual release scenario; - The ability to identify the location and size of the populations that may be exposed to doses of ionizing radiation is critical to carrying out appropriate treatment and post-event medical monitoring; - Once a spatial interaction model has been validated for a city or a region, it can then be used for simulation and prediction purposes to assess the possible human health consequences of different release scenarios. (authors)},
doi = {},
url = {https://www.osti.gov/biblio/21326153}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jul 01 00:00:00 EDT 2008},
month = {Tue Jul 01 00:00:00 EDT 2008}
}

Conference:
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