Heat Waves and Their Impacts on Human
Health in Urban Areas of Central Oklahoma
Emma
1
Fagan ,
1School
Kyle
Jessica
1
Voveris ,
Jeffrey
1
Basara ,
and Heather
2
Basara
of Meteorology, University of Oklahoma, 2Department of Geography, University of Oklahoma
1. Introduction
• Heat waves are common occurrences around the world and
have been projected to increase in frequency, intensity, and
longevity in the future (Luber and McGeehin 2008), and
therefore a better understanding of these events and their
impacts on human health is needed.
• Heat Waves are especially dangerous within urban
environments due to the phenomenon known as the Urban
Heat Island (Bornstein, 1968).
• The Urban Heat Island in Oklahoma City has been well
documented (Basara et al. 2008), and has shown that urban
areas can often be several degrees warmer than in rural areas
outside the city.
• Coupled with the increased temperatures in urban
environments, several demographic attributes can also play a
role in an individual’s vulnerability to extreme heat events,
such as income and age (Reid et al., 2009).
• In the summer of 2008 from July
31st to August 7th the Oklahoma
Mesonet (McPherson et al., 2007) and
the Oklahoma City Micronet (Basara
et al. 2011) documented a heat wave
that impacted central Oklahoma
(Basara et al., 2010).
2. Methodology
1
Thiem ,
Figure 1. Locations of the Oklahoma Mesonet and
Oklahoma City Micronet stations that were used.
• Using the spatial resolution provided by the Oklahoma City
Micronet (Figure 1), U.S. Census information, and ArcGIS
software this project aims at determining heat vulnerability at a
neighborhood level by examining four different attributes
(cluster data, population density, maximum temperature, and
minimum temperature), thus providing the means for better
mitigating against the risk of extreme heat waves.
• Census Tracts throughout Oklahoma City (defined by the
U.S. Census Bureau) are used for risk determination.
• The minimum and maximum temperature plots were
created by calculating the maximum and minimum
temperature of each Mesonet & Micronet station for each day,
interpolating using the Kriging
method, and then
Figure 2. Model created for data
calculating
processing in ArcMap10
the statistical
mean temperature for each census tract (Figure 2).
• Census tracts were organized into “Clusters” based off of multiple
demographic characteristics using a
Self-Organizing Map algorithm by Hall & Basara
(2010) (Figure 3).
• The area of each census tract
was determined by using The
‘Calculate Geometry’ tool in
ArcMap. Then population
density was calculated using the
Figure 3. Characteristics and locations of Clusters defined by the Self-Organizing
Map used by Hall & Basara 2010
population of each census tract as
determined by the 2000 U.S. Census Bureau (Figure 4).
• The maximum & minimum temperatures from each day of each
census tract were then averaged throughout the time of the study period
to determine the average maximum & minimum temperature of each
census tract (Figure 5).
Figure 6. Vulnerability plots for each for (in reading order) Maximum Temperature, Minimum
Temperature, Population Density, & Cluster Demographics
Figure 4. Population density of census
tracts in Oklahoma City
Figure 5. Average maximum & minimum temperatures of each census tract during the
study period.
3. Results & Conclusions
• The vulnerability of maximum & minimum temperature was given
a value corresponding to each degree Celsius the census tracts were
over the climatological mean (32.76ºC & 20.56ºC, respectively), then
Figure 7. Overall Vulnerability from 0-1 during the 2008 central Oklahoma heat wave
the scale was normalized to a 0-1 scale, where 1 represents a value of • This study has shown risk assessment is possible on the
10ºC above the climatological mean (Figure 6).
neighborhood scale and may one day be used for heat risk
• The population density vulnerability was given a value of 1 for
mitigation.
each 500 persons/km2, and then normalized to a 0-1 scale (Figure 6). 4. References
• Age, education, and income in each cluster were given a value of Bornstein, R. D., 1968: Observations of the urban heat island effect in New York City. Jour.
Metr, 7, 575-582.
1-5 based off of the conclusions of Hall and Basara, 2010. The 3 Reid,of C.Appl.
E., M. S. O’Neill, C. Gronlund, S. J. Brines, D. G. Brown, A. V. Diez-Roux, J.
values were then
Schwartz, 2009: Mapping Community Determinants of Heat Vulnerability. Environ.
Health Pers., doi: 10.1289/ehp.0900683 [Available online at http://dx.doi/.org]
averaged, and normalized
Meehl, G. A. and C. Tebaldi, 2004: More intense, more frequent, and longer lasting heat
to a 0-1 scale (Table 1 &
waves in the 21st Century. Science, 305, 994-997.
McPherson, R. A., C. A. Fiebrich, K. C. Crawford, R. L. Elliot, J. R. Kilby, D. L. Grimsley, J.
Table 1. Table of vulnerability levels of each cluster.
Figure 6).
E. Martinez, J. B. Basara, B. G. Illston, D. A. Morris, K. A. Kloesel, S. J. Stadler, A. D.
Melvin, A. J. Sutherland, J. Shrivastava, J. D. Carlson, J. M. Wolfinbarger, J. P. Bostic,
• The results were then averaged to find the average vulnerability for
and D. B. Demko, 2007: Statewide monitoring of the mesoscale environment: a technical
the 2008 central Oklahoma heat wave (Figure 7)
update on the Oklahoma Mesonet. Jour. of Atmos. and Oceanic Tech., 24, 301-321.
• Our results show the most at risk areas during this particular heat Hall, J. and H. Basara, 2010: Mapping Vulnerability in Oklahoma City: An Examination of
Connections between Demography and Location in an Urban Context. Population
wave were south central and central Oklahoma City, eastern
Association of America: 2010 Annual Meeting, 15-17 April 2010, Dallas, Tx. [Abstract
available at http://paa2010.princeton.edu/abstracts/101434]
Woodland Park, and The Village.
Cluster #
Vulnerability Age
Vulnerability Education
Vulnerability Income
1
2
3
4
5
2
3
3
3
5
2
2
5
4
1
4
2
5
5
1
Average
Vulnerability
Level
2.66
2.33
4.33
4.00
2.33
Normalized
Vulnerability
Level
0.415
0.333
0.833
0.750
0.333
5. Acknowledgements
• We would like to thank the Oklahoma Climatological Survey for the allowing us the use
of both Micronet and Mesonet data for this project.