A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement Metadata
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- Main Title A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement
Author: Jimenez, TamaraCreator Type: PersonalCreator Info: University of North Texas
Author: Mikler, Armin R.Creator Type: PersonalCreator Info: University of North Texas
Author: Tiwari, ChetanCreator Type: PersonalCreator Info: University of North Texas
Name: Institute of Electrical and Electronics EngineersPlace of Publication: [New York, New York]
- Creation: 2011-03
- Content Description: This article discusses a novel space partitioning algorithm to improve current practices in facility placement.
- Physical Description: 15 p.
- Keyword: epidemics
- Keyword: pandemics
- Keyword: biological threats
- Keyword: public health preparedness
- Keyword: response analysis
- Keyword: optimization
- Keyword: algorithm
- Keyword: algorithmic optimization
- Journal: IEEE Transactions on System, Man, And Cybernetics Part A, 2011, New York: Institute of Electrical and Electronics Engineers
- Publication Title: IEEE Transactions on System, Man, and Cybernetics Part A
- Volume: 42
- Issue: 5
- Page Start: 1194
- Page End: 1205
- Peer Reviewed: True
Name: UNT Scholarly WorksCode: UNTSW
Name: UNT College of EngineeringCode: UNTCOE
- Rights Access: public
- Archival Resource Key: ark:/67531/metadc132975
- Academic Department: Computer Science and Engineering
- Academic Department: Center of Computational Epidemiology and Response Analysis
- Display Note: © 2011 IEEE. Reprinted, with permission, from [Tamara Jimenez, Armin R. Mikler, and Chetan Tiwari, A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement, IEEE Transactions on System, Man, and Cybernetics Part A, March 2011]. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of North Texas' products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to firstname.lastname@example.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Display Note: Abstract: In the presence of naturally occurring and man-made public health threats, the feasibility of regional bio-emergency contingency plans plays a crucial role in the mitigation of such emergencies. While the analysis of in-place response scenarios provides a measure of quality for a given plan, it involves human judgement to identify improvements in plans that are otherwise likely to fail. Since resource constraints and government mandates limit the availability of service provided in case of an emergency, computational techniques can determine optimal locations for providing emergency response assuming that the uniform distribution of demand across homogeneous resources will yield and optimal service outcome. This paper presents an algorithm that recursively partitions the geographic space into sub-regions while equally distributing the population across the partitions. For this method, the authors have proven the existence of an upper bound on the deviation from the optimal population size for sub-regions.