Heat Waves and Their Impacts on Human
Health in Urban Areas of Central Oklahoma
---------------------------------------------------------Emma Fagan, Kyle Thiem, and Jessica Voveris
University of Oklahoma, School of Meteorology
Heather Basara
Assistant Professor, University of Oklahoma
Department of Geography
Jeffrey Basara
Associate Professor, University of Oklahoma
School of Meteorology
Abstract
Heat waves occur throughout the world every year and have substantial impacts on human
populations. Mortality and morbidity rates have been known to increase during these extreme heat events.
Due to the increasing trend in urbanization and the probability of extreme heat waves increasing in
intensity and frequency, adaptation and mitigation techniques are needed now more than ever. The
authors chose to look at the 2008 summer heat wave in Oklahoma City, Oklahoma to determine which
populations, based on a census tract level, were considered most at risk during such events. The purpose
of this project was to demonstrate vulnerability mapping using a geographic information system (GIS) on
a community level scale that could aid emergency managers in preparing and responding to disasters,
such as extreme heat events. Individual census tracts were given a vulnerability level according to four
different attributes (cluster data, population density, daily maximum temperature, and daily minimum
temperature), which will allow for easy determination of where emergency aid should be sent in future
events. Within the cluster data, demographic information such as education, income, and age were
analyzed. A final analysis included combining these factors to determine which census tracts within
Oklahoma City were considered most at risk.
1) Introduction
Heat waves occur throughout the world and have substantial impacts on human populations,
especially within urban environments. Higher rates of mortality and morbidity are often associated with
heat waves due to the high temperatures and increased air pollution. Events such as the Chicago Heat
Wave of 1995 (Krunkel et al. 1996), the European Heat Wave of 2003 (Garcia-Herrera et al. 2010), and
the Russian Heat Wave of 2010 (Grumm 2011) resulted in numerous fatalities and call attention to the
existing relationship between heat waves, urban environments, and human health. This particular field of
research regarding heat waves still requires further understanding of the Urban Heat Island (UHI) Effect
coupled with heat waves (Basara et al. 2010) in addition to temperature and other air pollutant impacts on
the human body (Filleul et al. 2006; Zvyagintsev et al. 2011). The importance in understanding these
features increases greatly when considering shifts in natural climate variability and the trend toward
increasing urbanization since heat waves are likely to increase in intensity, longevity, and frequency in
the future due to global climate change (Luber and McGeehin 2008; Meehl and Tebaldi 2004).
The purpose of this study was to further expand the knowledge about heat waves and their
impacts on both temperature and human health within urban environments and to introduce new
vulnerability mapping techniques for urban cities. Due to the increasing trend in urban population and the
probability of extreme heat waves increasing in intensity and frequency throughout the world, adaptation
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and mitigation techniques are necessary in order to decrease the rate of heat-related mortality. The authors
wish that in the future, the vulnerability mapping techniques introduced through this project can one day
serve emergency managers in predicting where and when they need to focus their emergency aid after, or
even before, a major heat wave event strikes an area.
2) Background
Since heat waves are a common occurrence across the world and have the ability to impact a
large amount of people, a further understanding of these events is essential. Heat waves have the potential
to be extremely deadly within urban environments due to how they interact with the urban settings. A
phenomenon known as the Urban Heat Island (UHI) Effect has been observed and studied in several
urban cities across the world (Bornstein 1968; Kim 1992; Hung et al. 2006; Basara et al. 2008; Gaffin et
al. 2008). Due to the UHI Effect, urban cities tend to be warmer than their rural counterparts, especially
during the nighttime hours. Basara et al. (2008) performed a study to explore the urban heat island of
Oklahoma City, Oklahoma. Utilizing the Oklahoma Mesonet stations and Oklahoma City Micronet
stations, they compared the urban environment with the surrounding rural and suburban areas. The UHI
Effect is more pronounced at night than during the day for both the 2-meter and 9-meter temperature
recordings (Figure 1). The urban environment was also noted to be warmer overall at 9 meters but slightly
cooler during the day at 2 meters during the time period from June 28 to July 31, 2003. Basara et al.
(2008) noted the slightly cooler temperatures at 2 meters could have been attributed to the placement of
measurement sites into areas where the density distribution of buildings was at its greatest, among other
factors, which could have impacted the amount of solar radiation measured.
Figure 1: Mean diurnal temperature differences at 9 meters and 2 meters in Oklahoma City, Oklahoma from the Oklahoma City
Micronet stations during the time period from June 28 to July 31, 2003 (Basara et al. 2008).
Previous research (Kunkel et al. 1996; Basara et al. 2010) shows that heat wave effects tend to be
enhanced in urban heat islands. The UHI effect has a tendency to raise air temperatures in an urban area
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approximately 0.5° C during the day to as much as 2° C during the nighttime period compared to rural
areas, which can be devastating during periods of intense heat waves (Basara et al. 2008; Basara et al.
2010). Rural areas experience rapid cooling at night compared to the urban areas due to the lack of
thermal storage and anthropogenic heating sources (e.g. exhaust from vehicles). These two factors result
in the overall warming experienced in the urban environment seen at the 9-meter level in Figure 1.
As city populations continue to grow and city sizes expand, this temperature difference between
urban and rural environments created as a result of the UHI effect during major heat waves can essentially
increase to an even larger gradient (Hung et al. 2006). The combined effects of heat waves and UHI can
potentially increase the air temperature in urban zones to the point where the risks for heat-related
illnesses amplify for a particular population compared to rural populations (Basara et al. 2010). For
extremely hot temperatures, these health impacts include upper respiratory diseases, heat strokes, and heat
exhaustion, and cardiovascular illnesses (Kilbourne 1997; Barnett et al. 2007; Lin et al. 2009). It was
noted that when the core body temperature exceeds 103° F severe heat strokes can occur resulting in
damaged organs and/or death. Previous research has also mentioned since heart related illnesses are
driven by blood pressure, these illnesses might occur as a result of decreasing blood pressure as
temperature increases.
Semenza et al. (1996) performed a study regarding the heat related deaths in the 1995 Chicago
Heat Wave. They noted living conditions and social contacts played an important role in determining the
death risk someone would have during a heat wave. People who had access to air conditioners had a much
lower risk of death compared to those who did not. Having access to a working air conditioner resulted in
an 80% reduction in the risk of death associated with cardiovascular diseases. Also, the authors noted that
people who lived alone and who had preexisting medical conditions were more susceptible to death
during these extreme heat events. The term “harvesting” has been used to describe the large amount of
deaths that typically occur at the beginning of a heat wave (Pattenden et al. 2003). This particular round
of deaths during heat waves is usually attributable to preexisting medical conditions.
Air pollutants during heat waves tend to be increased greatly within urban cities due to the air
circulation becoming stagnant under a persistent upper-level ridge and surface high pressure that lasts for
a long period of time. These pollutants impact the human body and result in an increased risk of
respiratory diseases. Several deaths in the European Heat Wave of 2003 (Filleul et al. 2006) and the
Russian Heat Wave of 2010 (Zvyagintsev et al. 2011) are attributable to the increased air pollution (e.g.
ozone). However, the risk of air pollutants may be minimal if the duration of the extreme heat event is
short lived, such as the Chicago Heat Wave of 1995.
Kovats and Hajat (2008) noted that one reason why so many deaths occurred in the 2003
European Heat Wave was the lack of an emergency plan for heat waves in the cities. There also seemed to
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be a lack of coordination between the social services and health agencies. This project aims to help create
an emergency plan by allowing emergency managers the knowledge of where the most vulnerable
populations exist within their respective cities. With this information, emergency managers would have
the potential to save several lives by sending aid to the most at risk populations before an increase in
mortality can occur.
3) Data and Methodology
This project focuses on the 2008 summer heat wave of central Oklahoma. The authors defined the
2008-summer heat wave to last from July 31, 2008 through August 7, 2008. This study utilized both
Oklahoma Mesonet data and Oklahoma City Micronet data in addition to demographic data obtained from
the Census Bureau and ozone data obtained form the Oklahoma Department of Environmental Quality.
All data was inserted into the ArcGIS program for further analysis, which was subsequently followed by
the assignment of vulnerability levels for individual census tracts.
3a) Oklahoma Mesonet Data
This network consists of ~120 remote sensing stations located in rural areas across the state of
Oklahoma (McPherson et al. 2007). Each station measures numerous types of data including the data
utilized for this project, 2-meter air temperature. These stations transmit measured data every 5 minutes to
a central location for quality assurance and archival. This particular study used 11 stations near the
Oklahoma City metropolitan area to aid in the interpolation of temperature data for the Oklahoma City
census tracts that were on the edge of our study area.
3b) Oklahoma Micronet Data
This network consists of ~36 stations mounted on traffic signals within Oklahoma City,
Oklahoma (Basara et al. 2011). Each station measured numerous types of data including the data utilized
for this project, 9-meter air temperature. These stations are not currently transmitting data; therefore data
from the 2011 and 2012 heat waves were unavailable for this research project. Measured data was
transmitted every minute to a central location for quality assurance and archival, similar to that of the
Oklahoma Mesonet. The locations of all Oklahoma Mesonet and Oklahoma City Micronet stations used
throughout this study are shown in Figure 2.
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Figure 2: Oklahoma Mesonet and Oklahoma City Micronet station locations around the Oklahoma City metropolitan area.
3c) Census Bureau Data and Ozone Data
This project utilized census tract data obtained from the Census Bureau, which contained several
population attributes including population density, age, income, and education. Hall and Basara (2010)
combined the latter three attributes to form 5 clusters in an attempt to aid with simplifying the data
(Figure 3). This study also explored ozone concentration data across the census tracts; however, according
to the Environmental Protection Agency Air Quality Index, ozone concentration did not reach significant
health concern levels during this event. Therefore, the authors decided not to assign vulnerability levels
based on ozone concentration for this specific event.
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Figure 3: Cluster analysis data as defined by Hall and Basara (2010).
3d) Geographic Information System (GIS)
The geographic information system used primarily in this project was ArcGIS 10. Graphics were
created for each variable with the census tract outlines overlaid. Ozone concentration and the cluster
analysis data did not require any further calculations prior to creating GIS graphics. Population density
was calculated to be in units of persons per square kilometer for each individual census tract using
analysis tools within ArcGIS 10. Maximum daily temperature and minimum daily temperature were
obtained from all Mesonet and Micronet stations being utilized in this research project throughout the
time period being investigated. Maximum daily temperature was defined to occur between 1300 UTC and
0100 UTC, while minimum daily temperature was defined to occur between 0100 UTC and 1300 UTC.
Due to the poor spatial resolution of Mesonet and Micronet locations on the periphery of the Oklahoma
City limits, the authors used the kriging interpolation method (Oliver and Webster 1990) in order to
interpolate the point temperature data from point to raster format. Once the temperature data was
interpolated, the authors calculated the mean temperatures throughout the spatial area of each census tract
in order to assign each tract a daily minimum and daily maximum temperature for each day during the
defined event (Figure 4). This particular field was also averaged over the duration of the event to allow
for the assignment of vulnerability levels on both a daily timescale and event-long timescale.
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Figure 4: Model used in ArcMap 10 to interpolate temperature and calculate the statistical mean for each census tract.
2e) Assigning Vulnerability Levels
The assignment of vulnerability levels was quite subjective due to the nature of this research
field. No previously peer-reviewed research has explored vulnerability mapping for extreme heat events
on a community scale with the use of GIS, and only very little research has been done assigning
vulnerability levels to differing populations based on demographic data in addition to temperature
extremes. There has been previous research done regarding vulnerability due to extreme temperature
thresholds alone, however (Armstrong 2006; Lin et al. 2009). Additionally, Reid et al. (2009) studied
vulnerability mapping based solely on ten demographic variables with each variable assumed to have a
linear relationship with vulnerability; therefore, for this study the authors chose to assume a linear
relationship between all demographic variables (population density, age, education, and income) and
vulnerability. The authors used a scale from 1 to 5 with 1 representing the lowest relative risk and 5
representing the highest relative risk for these demographic variables.
Hajat and Kosatky (2010) discovered an increasing trend of vulnerability with increasing age,
especially for those above 65 years old. For this reason, the cluster with the oldest population was given
the highest vulnerability level, with the middle age groups receiving a vulnerability level of 3 and the
young adults receiving a vulnerability level of 2. Education and income were determined to have separate
vulnerability levels based on the work done by Reid et al. (2009). The authors decided to assume higher
education and higher income would lead to decreased vulnerability. Research shows that high income
drastically reduces vulnerability to heat waves simply due to the fact that people can afford air
conditioning in their homes. As mentioned before, Semenza et al. (1996) noted a significant decrease in
mortality when people were able to access air conditioning. In regards to the cluster data, since age,
education, and income were combined to form five different clusters, vulnerability levels for each cluster
were obtained by averaging the vulnerability levels given to each of the three demographic attributes
(Table 1). The authors then normalized the averaged vulnerability levels to 1 in order to aid in the
comparison of one census tract to another.
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Table1: Assignment of vulnerability levels to the cluster data with 1 representing the lowest risk and 5 representing the highest
risk. Similar methods of vulnerability assignments were used with the population density and temperature data.
Cluster #
Vulnerability Age
Vulnerability Education
Vulnerability Income
Average
Vulnerability
Level
Normalized
Vulnerability
Level
1
2
2
4
2.66
0.415
2
3
2
2
2.33
0.333
3
3
5
5
4.33
0.833
4
3
4
5
4.00
0.750
5
5
1
1
2.33
0.333
Similar tables were also created for the population density and temperature data for each
individual census tract. For population density, research demonstrates that higher population densities
tend to result in larger heat effects on humans (Medina-Ramon and Schwartz 2007). The authors first took
the range of population densities and divided it into 5 equal groupings. Vulnerability levels were then
determined so that a vulnerability level of 1 corresponds to the lowest population densities and a
vulnerability level of 5 corresponds to the highest population densities. These vulnerability levels were
also normalized to 1 for comparison purposes.
Hajat and Kosatky (2010) also discovered that most heat slopes within literature show that for
every 1ºC increase past a certain temperature threshold, a subsequent increase of 1-3% in human
mortality occurs. This research allowed the authors to assume a linear relationship and assign a
vulnerability level based on the number of degrees Celsius past a certain threshold for both daytime and
nighttime temperatures. The thresholds were determined to be the maximum average summertime (June,
July, August) temperature for the daytime temperature and the minimum average summertime
temperature for the nighttime temperature due to the work assessed in Hajat and Kosatky (2010). These
temperatures were calculated using the 1971 to 2000 climate normals data for Oklahoma County from the
Oklahoma Climatological Survey. The daytime temperature threshold value used for this project was
32.76ºC (90.97ºF) and the nighttime temperature threshold value used for this project was 20.56ºC
(69.0ºF). Vulnerability levels for temperature were assessed on a scale of 1 to 10, with 1 representing 1ºC
above the climatological average, 2 representing 2ºC above the climatological average, and so on, up to
10. All vulnerability levels were again normalized to 1 for comparison purposes. Hajat and Kosatky
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(2010) also discussed how observed temperature and apparent temperature are essentially interchangeable
in regards to the heat slope mentioned above.
In order to determine the most at risk areas for any duration of the extreme heat event, the authors
combined the defined normalized vulnerability levels for all attributes (cluster data, population density,
maximum temperature, and minimum temperature) using the following equation (1) for each census tract.
The temperatures used for the event total risk assessment were the averaged maximum and minimum
temperatures of the event and the temperatures used for the daily total risk assessment were the daily
maximum and minimum temperatures. Every attribute was assumed to hold equal weighting for the
determination of total risk (Reid et al. 2009). After total risk was calculated for each census tract, the
values were once again normalized to 1.
𝑇𝑜𝑡𝑎𝑙 𝑅𝑖𝑠𝑘 = 0.25 ∗ 𝑇𝑚𝑎𝑥 + 0.25 ∗ 𝑇𝑚𝑖𝑛 + 0.25 ∗ 𝑃𝑜𝑝. 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 + 0.25 ∗ 𝐶𝑙𝑢𝑠𝑡𝑒𝑟 𝐷𝑎𝑡𝑎
(Eq. 1)
4) Results and Discussion
Figure 5 displays the outlines of all census tracts utilized with the color fill representing its
designated cluster based on the cluster data defined in Hall and Basara (2010) (cluster data shown in
Figure 3). Based on the cluster data alone, the authors hypothesized that census tracts within clusters 3
and 4 would have the highest potential risk during an extreme heat event. Figure 6 shows census tract
vulnerability based on the cluster data. Table 1 displayed the detailed vulnerability levels given to each
cluster, in which clusters 3 and 4 did indeed have the highest vulnerability levels with 0.833 and 0.750,
respectfully. These two clusters were determined to have the lowest income and lowest education levels
of all the clusters; these two factors played a key role in the clusters’ high vulnerability levels since these
two groups would most likely not be able to have access to air conditioning. Cluster 1 had a moderate risk
during the heat wave event of 0.415, which was primarily due to the low to middle income level. Clusters
2 and 5 were determined to have the lowest risk in heat waves, primarily due to their higher income and
higher education levels.
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Figure 5: Census tracts within the Oklahoma City area defined by their cluster.
Figure 6: Census tract vulnerability based solely on the cluster data defined by Hall and Basara (2010).
Population density in units of persons per square kilometer is shown below in Figure 7. It can be
easily be seen that the highest densities lie within the most urbanized regions of the Oklahoma City
Metropolitan area and the lowest densities lie within the rural areas on the outer regions of the urbanized
portion of the city. These features are extremely important in regards to the urban heat island
phenomenon, in which the urbanized areas can experience temperatures several degrees above their rural
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counterparts during both the daytime and nighttime hours. This phenomenon, as stated before, tends to be
most prominent during the nighttime hours with temperatures as much as 2ºC higher within the urbanized
areas due to the thermal storage and anthropogenic heating. Higher vulnerability levels tend to exist
where the population densities are highest. Figure 8 displays the vulnerability levels associated with this
population density. Indeed, the highest vulnerability levels of 0.6 and greater are within the most
urbanized areas of the Oklahoma City Metropolitan area. These results are to be expected due to the linear
relationship assumed for this particular project. When comparing these values to the cluster data
vulnerability (Figure 5), it can be seen that several census tracts within cluster 3 also have the highest
vulnerability levels for population density. These census tracts not only experience a 0.833 vulnerability
level due to cluster data but also 0.8 and greater vulnerability levels due to the population density data.
Figure 7: Population density map of the Oklahoma City Metropolitan area.
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Figure 8: Population density vulnerability map of the Oklahoma City Metropolitan area.
Figure 9 displays the recorded daily maximum temperature and Figure 10 shows the recorded
daily minimum temperature for what was defined to be the most impactful day based on temperature
alone. August 4, 2008 had the hottest daytime maximum temperature and the hottest nighttime minimum
temperature throughout the heat wave event. The daily maximum temperature for August 4 across the
Oklahoma City Metropolitan area ranged from approximately 38.8ºC to just above 40ºC, while the
minimum temperature for this day ranged from 24ºC to nearly 27ºC. The urban heat island and the heat
island plume were quite noticeable within these plots with the center of downtown, and northward,
experiencing the hottest temperatures both during the daytime and nighttime. Graphs similar to those
shown for August 4 were also generated for each day and for the duration of the extreme heat event. By
examining the event-long average maximum and minimum temperatures, areas where the temperatures
were relatively high for a longer period of time begin to become more apparent.
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Figure 9: Interpolated daily maximum temperature for August 4, 2008.
Figure 10: Interpolated daily minimum temperature for August 4, 2008.
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The vulnerability associated with the daily maximum and daily minimum temperature graphics
shown above are displayed in Figure 11 and Figure 12, respectfully. In Figure 11, vulnerability levels
across the area range from 0.6 to 0.8. The highest vulnerability levels, associated with the hottest
temperatures, were assessed to be in the most urbanized area of downtown Oklahoma City. Stronger
gradients of the vulnerability levels are seen in Figure 12, where the levels range from 0.4 to 0.8. Due to
the urban heat island and heat island plume, populations within the more urbanized portions of the
Oklahoma City Metropolitan area definitely see a higher potential risk due to nighttime temperature
alone. The hottest temperatures, and subsequently the highest vulnerability levels, are seen in the northcentral region of Oklahoma City, northeast of Lake Hefner. Graphics similar to these were also created
for each day as well. Comparing these vulnerability attributes to the cluster data once more, it can be seen
that cluster 3 again lies within the region of highest vulnerability for both the daytime maximum and
nighttime minimum temperatures. Portions of cluster 1 and 4 are also impacted greatly by the high
nighttime temperatures. However, looking solely at the nighttime temperatures, portions of clusters 2 and
5 actually have the highest vulnerability levels.
Figure 11: Vulnerability levels for each census tract based on daily maximum temperature for August 4, 2008.
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Figure 12: Vulnerability levels for each census tract based on daily minimum temperature for August 4, 2008.
Vulnerability levels associated with temperature were not only generated for each day but also for
the entire duration of the event as well. Figure 13 displays the average maximum temperature
vulnerability and Figure 14 shows the average minimum temperature vulnerability. These vulnerability
levels were assessed on the same conditions as was the daily maximum and daily minimum temperatures,
with the per 1ºC increase of temperature past the given thresholds resulting in an additional vulnerability
level. Vulnerability levels given to each census tract based on the average maximum temperature were
relatively low for the majority of census tracts compared to the levels given based on the average
minimum temperature. Values range from 0.3 to 0.5 for the daytime temperatures while at night the
values range from 0.2 to 0.6. The impact from urban heat island plume is also quite noticeable on the
average minimum temperature vulnerability plot with higher vulnerabilities associated with its existing
location.
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Figure 13: Vulnerability levels for each census tract based on the average maximum temperature from the entire duration of the
extreme heat event.
Figure 14: Vulnerability levels for each census tract based on the average minimum temperature from the entire duration of the
extreme heat event.
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Potential total risk was determined by combining each of the vulnerability factors previously
discussed with equal weighting on both daily and event-long timescales. Figure 15 shows the total risk for
August 4, 2008. This was determined using the population density, cluster data, daily maximum
temperature, and daily minimum temperature. Figure 16 displays the total risk for the entire event.
Population density and cluster data were used in this calculation, in addition to the average maximum
temperature and average minimum temperature of the event. On August 4, 2008 the vulnerability levels
ranged from 0.3 on the outskirts of the city to 0.8 within the city. The highest levels for this day were seen
in south-central Oklahoma City, south of Interstate 40 and north of Interstate 240. Recall that these census
tracts lie within cluster 3, which was known to be the poorest and least educated of the clusters. For the
entire event, vulnerability levels ranged from 0.2 in the outskirts of the city to 0.7 within the city. Similar
patterns are seen in regards to which census tracts are considered to have the highest potential total risk.
When comparing this total risk to the cluster data, it can be seen that the highest total risk is most likely to
be seen in census tracts that are within clusters 1 and 3, and the lowest total risk is most likely to be seen
in census tracts within cluster 2.
Figure 15: Potential total risk assessed for August 4, 2008.
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Figure 16: Potential total risk assessed for the entire extreme heat event from July 31, 2008 to August 7, 2008.
5) Conclusion
This research served as a pilot project for exploring the plausibility of vulnerability mapping on a
community level scale. Overall, the project proved to be successful and displayed the potentially most at
risk census tracts within the Oklahoma City Metropolitan area on both a daily scale and event-long scale.
Using this information, emergency managers could potentially send aid to the most at risk populations
during extreme heat events and possibly save lives by utilizing a targeted approach. Similar vulnerability
maps could even be produced for other cities as well; however, since these vulnerability maps are
relatively based on the specific city demographics and climatological temperatures the maps cannot be
compared across different cities. Further work to improve this project includes verifying the vulnerability
maps with hospital data and further analysis on weighting the different vulnerability factors. Another step
would be to make this process comparable across cities instead of only comparable within cities.
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6) References
Armstrong, B., 2006: Models for the relationship between ambient temperature and daily mortality.
Epidem, 2006, 624-631.
Barnett, A. G., S. Sans, V. Solomaa, K. Kuulasmaa, and A. J. Dobson, 2007: The effect of temperature on
systolic blood pressure. Blood Press. Monit., 12, 195-203.
Basara, J. B., B. G. Illston, C. A. Fiebrich, P. D. Browder, C. R. Morgan, A. McCombs, J. P. Bostic, R. A.
McPherson, A. J. Schroeder, and K. C. Crawford, 2011: The Oklahoma City Micronet. Metr.
Appl., 18, 252-261.
Basara, J. B., H. G. Basara, B. G. Illston, and K. C. Crawford, 2010: The impact of the urban heat island
during an intense heat wave in Oklahoma City. Adv. in Metr., 2010, doi:10.1155/2010/230365.
Basara, J. B., P. K. Hall Jr., A. J. Schroeder, B. G. Illston, and K. L. Nemunaitis, 2008: Diurnal cycle of
the Oklahoma City urban heat island, Jour. of Geophys. Res., 113, doi:10.1029/2008JD010311.
Bornstein, R. D., 1968: Observations of the urban heat island effect in New York City. Jour. of Appl.
Metr, 7, 575-582.
Filleul, L., S. Cassadou, S. Medina, P. Fabres, A. Lefranc, D. Eilstein, A. L. Tertre, L. Pascal, B.
Chardon, M. Blachard, C. Declercq, J. Jusot, H. Prouvost, and M. Ledrans, 2006: The relations
between temperature, ozone, and mortality in nine French cities during the heat wave of 2003.
Envir. Health Persp., 114, 1344-137.
Gaffin, S. R., C. Rosenzweig, R. Khanbilvardi, L. Parshall, S. Mahani, H. Glickman, R. Goldberg, R.
Blake, R. B. Slosberg, and D. Hillel, 2008: Variations in New York City’s urban heat island
strength over time and space. Theor. Appl. Climatol., 94, 1-11.
Garcia-Herrera, R., J. Diaz, R. M. Trigo, J. Luterbacher, and E. M. Fischer, 2010: A review of the
European summer heat wave of 2003. Crit. Reviews in Envir. Sci. and Tech., 40, 267-306.
Grumm, R. H., 2011: The central European and Russian heat wave of July-August 2010. BAMS, 92,
doi:10.1175/2011BAMS3174.1.
Hajat, S., and T. Kosatky, 2010: Heat-related mortality: a review and exploration of heterogeneity, Jour.
Of Epidemiol Comm. Health, 64, 753-760.
20
Hall, J. and H. Basara, 2010: Mapping Vulnerability in Oklahoma City: An Examination of Connections
between Demography and Location in an Urban Context. Population Association of America:
2010 Annual Meeting, 15-17 April 2010, Dallas, Tx. [Abstract available at
http://paa2010.princeton.edu/abstracts/101434]
Hung, T., D. Uchihama, S. Ochi, and Y. Yasuoka, 2006: Assessment with satellite data of the urban heat
island effects in Asian mega cities. Inter. Jour. of Appl. Earth Obs. and Geoinfo., 8, 34-48.
Kilbourne, E. M., 1997: Heat waves and hot environments. The Public Health Consequences of Diasters,
ed. E. Noji, New York: Oxford Univ. Press, 245-269.
Kim, H. H., 1992: Urban heat island. Int. Jour. of Remote Sensing, 13, 2319-2336.
Kovats, R. S., and S. Hajat, 2008: Heat stress and public health: a critical review. Annu. Rev. Public
Health, 2008, 41-55.
Krunkel, K. E., S. A. Changnon, B. C. Reinke, and R. W. Arritt, 1996: The July 1995 heat wave in the
Midwest: a climatic perspective and critical weather factors. BAMS, 77, 1507-1518.
Lin, S., M. Luo, R. J. Walker, X. Liu, S. Hwang, and R. Chinery, 2009: Extreme high temperatures and
hospital admissions for respiratory and cardiovascular diseases. Epidem., 2009, 738–746.
Luber, G., and M. McGeehin, 2008: Climate change and extreme heat events. Amer. Jour. Prev. Med., 20,
429-435.
McPherson, R. A., C. A. Fiebrich, K. C. Crawford, R. L. Elliot, J. R. Kilby, D. L. Grimsley, J. 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, and D. B. Demko,
2007: Statewide monitoring of the mesoscale environment: a technical update on the Oklahoma
Mesonet. Jour. of Atmos. and Oceanic Tech., 24, 301-321.
.Medina-Ramon
M, and J. Schwartz, 2007: Temperature, temperature extremes, and mortality: a study of
acclimatization and effect modification in 50 United States cities. Occup. Environ. Med., 64, 27833.
Meehl, G. A. and C. Tebaldi, 2004: More intense, more frequent, and longer lasting heat waves in the 21st
Century. Science, 305, 994-997.
Oliver, M. A. and R. Webster, 1990: Kriging: a method of interpolation for geographical information
21
systems. International Jour. of GIS, 4, 313-332.
Pattenden, S., B. Nikiforov, and B. G. Armstrong, 2003: Mortality and temperature in Sofia and London.
Jour. Epidem. Comm. Health, 57, 628-633.
Reid, C. E., M. S. O’Neill, C. Gronlund, S. J. Brines, D. G. Brown, A. V. Diez-Roux, J. Schwartz, 2009:
Mapping Community Determinants of Heat Vulnerability. Environ. Health Pers., doi:
10.1289/ehp.0900683 [Available online at http://dx.doi/.org]
Semenza, J. C., C. H. Rubin, K. H. Falter, J. D. Selanikio, W. D. Flanders, H. L. Howe, and J. L.
Wilhelm, 1996: Heat-related deaths during the July 1995 heat wave in Chicago. New England
Jour. of Med., 335, 84-90.
Zvyagintsev, A. M., O. B. Blum, A. A. Glazkova, S. N. Kotel’nikov, I. N. Kuznetsova, V. A. Lapchenko,
E. A. Lezina, E. A. Miller, V. A. Milyaev, A. P. Popikov, E. G. Semutnikova, O. A. Tarasova,
and I. Y. Shalygina, 2011: Air pollution over European Russia and Ukraine under the hot summer
conditions of 2010. Atmos. and Oceanic Phys., 47, 699-707.
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