A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement Page: 14
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IEEE TRANSACTIONS ON SYSTEM, MAN, AND CYBERNETICS PART A, VOL. X, NO. X, MARCH 2011
solution with the given resources. In this paper, an algorithm
to determine a POD placement based on available resources is
presented. The feasibility of the generated response scenarios
is examined based on their population distribution across the
different catchment areas.
The continuous partitioning method can be based in the area
of continuous location science, since the POD locations can
be placed anywhere in the geographic space. The algorithm
creates sub-regions within the geographic region for each of
the PODs, while maintaining a uniform population distribu-
tion. The maximum deviation from the arithmetic average of
the population is bounded by the maximum census block size.
The algorithm has been demonstrated authentic census
data from Denton County. Results show that the partitioning
method creates catchment areas with uniform population dis-
tributions. The POD locations are determined by calculating
the geographic centroids of the catchment areas. A comparison
to catchment areas calculated via the Voronoi-approach on the
same set of PODs show that the catchment areas determined
by the algorithm provide better population distributions than
the Voronoi regions. These results suggest that public health
experts may need to consider partitions of the geographic
space into catchment areas, that do not resemble Voronoi
tessellations. Such an alternate assignment of the population
to the service facilities can provide a balanced distribution of
resources throughout the target region.
This research has been funded by NIH grant NIH
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~ ~ ~ 'D ~
Tamara Jimenez received the Diplom in Infor-
matics from the University of Passau, Germany in
2007 and the Ph.D. in Computer Science from the
University of North Texas, United States in 2010.
After working as a post-doctoral research associate
with the Center of Computational Epidemiology and
Response Analysis (CeCERA) at the University of
North Texas, Dr. Jimenez is currently employed as
a Lecturer in Theoretical Computer Science at the
University of North Texas. Her research includes
the design of algorithms for the analysis and op-
timization of bio-emergency response plans, as well as the development of
methodology for generation of synthetic geographic regions.
at UNT and leads the
Armin R. Mikler received his Diploma in Infor-
matics from Fachhochschule Darmstadt, Germany in
1988. He received a Fulbright Scholarship in 1986
and completed his MS and PhD in Computer Science
at Iowa State University in 1990 and 1995 respec-
tively. Since 1997, Dr. Mikler is a faculty member in
Computer Science at the University of North Texas
(UNT) with joint appointment in the Department
of Biological Sciences. Dr. Mikler has established
and is the director of the Center for Computational
Epidemiology and Response Analysis (CeCERA)
Computational Epidemiology Research Laboratory
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Jimenez, Tamara; Mikler, Armin R. & Tiwari, Chetan. A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement, article, March 2011; [New York, New York]. (digital.library.unt.edu/ark:/67531/metadc132975/m1/14/: accessed June 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.