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Allison Payenski
Mr. Pisarra
AP Human Geography
27 March 2011
I. Introduction
Across the United States, high school cross country runners compete on 5k courses in many
different climates, and therefore, the times they may run are greatly affected. There are various
factors that play into a good race: physical preparation, mental preparation, and the climate. To
evaluate various types of physical preparation, I would have had to reach out to many runners.
From speaking with many athletes from the Northeast, I discovered we have very similar training
methods, and I had doubts that other regions of the country would be much different. For the
purpose of this project, I planned to limit my views to the climatic factors of running based on
data collection and analysis. There are so many various types of weather, altitude ranges, and
temperature changes that can both benefit and hinder runners. The goal of this assignment was to
try to pinpoint a few factors and research their effects on runners.
Specifically, my hypothesis is that if runners live in a location with a relatively
warmer climate and more variations in altitude, they will have faster times than others.
This is because runners perform better in ideal environmental conditions that vary throughout the
United States. Of course, there is no one factor that determines the speed of a runner, but these
variables seem to be very important, and may have a correlation when they are compared
relatively to a large sample of runners.
II.
Lit Review
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The first reading I found is called “Environmental Preference Migration: A Review” by
Larry M. Svart. His discussion was that people tend to migrate towards favorable areas for many
different reasons. For instance, there is less stress in the south, more Vitamin D, and longer hours
of daylight. This is a benefit for people, especially after it was proven that extreme hours of
darkness cause behavior shifts and that cloudy weather may cause depression (Svart). In the
surveys proposed in the article about perception of US regions, the Midwest and Northeast were
frowned upon, and the “South’s humidity was attractive” (Svart 316). The author figured that
climate is one of the main factors that influence internal migration.
The second reading I found to be helpful is “An Evaluation of the Tourist Potential of the
Climate in Catalonia” by Martin Gomez. The author’s research method was similar to the
approach I took for the project, but he took daily weather samples in an area and decided on six
main factors of the environment; these included temperature, altitude, humidity, wind velocity,
sunlight hours, and the thermo-anemometric index. He tried to see if more tourists visit an area
when the weather is more favorable, and he thought this was what led to most vacation decisions.
In the end, he stated that “seasonal distribution of demand also sdepends on many other factors
that are possibly more important” than the weather at the time of the vacation, such as work
schedules and breaks from school. So, his conclusion was that there are multiple factors and it
cannot be only limited to the climate.
III.
Data Collection
I researched two types of data that in the end led me to my conclusion. First of all, I had to
somehow combine athletes’ performances to compare them in a uniform way, rather than taking
data from different races, which would be like comparing “apples to oranges” and would have no
meaning. Instead, I took my data from a major race that took place after states were over. Nike
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Cross Nationals brings the entire country together—the best vs. the best. But to qualify for this
major event, regional races took place across the country over a period of two weekends in
November. The winners of these races would run in the National race in Portland Oregon.
There are eight regions in the
country that had races of their
own, and I made a map of these
regions. All these are represented
on the map to the right which is
also reprinted before the Works
Cited page. The only race
location that has 2 regions is in
the Northeast. New York has its separate region because of the high number of talented New
York athletes. Now, the two are split, but they take place at the same event. California does not
have a region that it fits into, and it does not have its own. Yet, people from the state are
represented at the National meet, but I could not find any statistics to explain this. For this
reason, California is not in my results.
For both the boys’ and girls’ races, I compared the top twenty times in an excel spreadsheet
that is also at the back of the paper. I assigned a color to a fifteen-second time range and marked
each resulting time in that color. This is also listed in my data, so I will not reprint this
information at this time. For girls, the fastest times came from the Southwest, Midwest,
Southeast, and Heartland. For the boys, the fastest times came from the Midwest, Southeast, and
Southwest. The top locations were consistent between both genders, but there was a slightly
different order of ranking. I represented the data for all the regions in a map, marked by color
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and the number of runners that hit times in certain ranges. The higher the number inside the
orange circle, for instance, represents a higher number of faster athletes. If there are more people
in the green range, they have slower times relative to other regions.
The second type of data I found was quantitative as well, and its purpose was to describe the
climates across the eight regions. I found the high and low temperatures for the month of
November, and I looked at the topography of the nation too. I could not find individual altitude
variations within the area, but I found the altitudes of the cities in which the athletes had to run
for the qualifying races. This is also in an excel spreadsheet before the Works Cited page. I
represented this data by shading in a map with colors according to their average temperatures,
and I placed symbols on the map where the highest elevation spots are.
This data has limitations because I only have elevation information for exact cities, which
don’t take into consideration the surrounding area. Also, average temperatures do not tell the
exact degrees for the given day, and it could be significantly off from what the real weather was
on the days of the races.
IV.
Analysis
When I lined up my maps side by side, I expected to see a correlation between warmer
temperatures and faster times, and more mountains and faster times. I studied the times of each
region and tried to find comparisons and draw conclusions from the times alone. Then I tried to
match up the times with the variables in my hypothesis. But I found that the two hottest regions
of country, the South and Southwest, did have both male and female runners hitting faster times,
but the difference is not significant enough to presume anything. The fastest region for both
genders was the Southeast and then the Midwest. These two regions were neither the highest in
elevation nor the warmest for average temperatures. South Dakota had the highest elevation, but
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my resources did not give me the altitude variation, so I do not know how hilly the area was for
the runners. The only consistency was that the northernmost regions had the slowest runners, the
coldest averages, and the lowest elevation.
After analyzing my data, I found some further research from an article on marathons
suggested that 55 degrees F is the ideal temperature, and when the temperature rises above this,
the body begins to sweat more and therefore, it performs less efficiently as it gives off more heat
and uses more energy than it would in colder temperatures (Cheuvront). A main factor, however,
is humidity, because this plays a big role in body temperature regulation and how much a person
sweats.
This fact and my data analysis have led me to reject my hypothesis. No, warmer temperatures
and altitude variations are not two main factors in better performances. They are indeed factors
to runners, but I either had too many inconsistencies and not enough controls, or I chose the
wrong aspects of climate, but there is not enough evidence to prove my hypothesis to be correct.
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Works Cited
Cheuvront, S.N., and E.M. Haymes. "Thermoregulation and Marathon Running: Biological and
Environmental Influences." Sports Medicine 31.10 (2001): 743-762. Academic Search
Premier. EBSCO. Web. 26 Mar. 2011.
Gómez Martín, M. Belén. "An Evaluation of the Tourist Potential of the Climate in Catalonia
(Spain): A Regional Study." Geografiska Annaler. Series A, Physical Geography. Vol.
86. Blackwell, 2004. 249-64. Ser. 3. JSTOR. Web. 26 Mar. 2011.
<http://www.jstor.org/stable/3566122>.
"HAMweather Climate Center - Normal Mean Temperatures for November - Continental US
View." HAMweather Interactive. Web. 27 Mar. 2011.
<http://mapcenter.hamweather.com/normals/mean/11/us.html?points=english>.
"NikeCrossNationals.com - Nike Cross Nationals Official Site - NXN." RunnerSpace.com. Web.
27 Mar. 2011. <http://www.runnerspace.com/nxn>.
"Relative Humidity - CityRating.com." Best Places To Live - CityRating.com. Web. 27 Mar.
2011. <http://www.cityrating.com/relativehumidity.asp>.
Svart, Larry M. "Environmental Preference Migration: A Review." Rev. of American
Geographic Society. Geographical Review July 1976: 314-30. JSTOR. Web. 28 Feb.
2011. <http://www.jstor.org/stable/213888>.
"United States Map - Topographical Map." United States Map - Maps of United States. Web. 27
Mar. 2011. <http://www.united-states-map.com/us402112.htm>.
"US Weather - Average Temperatures and Rainfall." Country Studies. Web. 27 Mar. 2011.
<http://countrystudies.us/united-states/weather/>.