The goal
of this exercise was to examine the geography behind severity of Tornadoes in
Oklahoma and Kansas. Through this exercise two time periods were examined,
Tornado Locations and width of tornadoes from 1995-2006 and also from
2007-2012. We were asked the question of should the people that live in these
high tornado prone areas be required to build shelters or would it just be a
waste of resources? Are there areas where you are more prone to having
tornadoes occur? Another aspect of this job was also to analyze if a
significant change has occurred or not between these two time periods in both
location and size of tornado incidences.
The data
that was utilized was point data for the locations of tornadoes in Oklahoma and
Kansas. Each point of data also carried with it the width of the tornado and
for this assignment it was assumed the width was directly related to the
severity, so the larger the width the more severe the tornado. The time period
of this data stretched from 1995 to 2012 and was broken into two separate sets
of data. For calculations it was required
that ARCMAP was utilized, this made short work of the analysis of the main
components of the exercise. From there the maps were exported to Adobe
Illustrator to be cleaned and finished before being exported as a final
product, one of these main components of this analysis was the calculation of
statistics that heavily relate to these data sets such as the standard deviation,
z-score, mean center and the standard distance of the point data. The mean
center of the data is a spatial measure of the central tendency of the data or
where the center of the data would lie if it was a point on a Cartesian plane. This is essentially a way of spatially
visualizing the mean of the data to put meaning to numbers. Something similar
that is examined in this lab is the weighted mean center. Like the mean center
it is a spatial representation of the mean of the data but is weighted by the
frequency of the data. An example of this would be either Figure 1, 2 or 3 where
both the mean center and the weighted mean center are displayed on the same
map. It is easily visible that the two points are located in different positions
on the map. Another one of the main components of this lab was the
visualization of standard distance which is just the way to spatially show the standard
deviation of the data.
From
this analysis of the data sets there is significant argument for the
construction of these severe weather shelter due to many factors. As is visible
in figures 1 and 2
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| Figure 1 |
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| Figure 2 |
the distribution of the tornado location themselves are
fairly evenly distributed. This is echoed by examining the placement of the
mean center on each of these maps which lies fairly central within the two
states. Though for severity of tornado the weighted mean center was utilized.
This weighting of the data draws the mean center further south showing that
more severe tornadoes occur in Oklahoma than Kansas. Another main component to
this exercise was to determine if there had been significant change in severity
and location of tornadoes occurring. In the time period from 1995-2006 most of
the severe tornadoes were located in Oklahoma which when looking at the
weighted mean center is what dragged the point south for that data set. Though
from observing figures 4 and 5 it is
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| Figure 4 |
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| Figure 5 |
evident that even though the mean center
itself is being pulled southeast due to the higher amount of tornadoes occurring
a vast majority of the data still falls within 1 standard distance of the mean
center. From the analysis it can be assumed that if these trends were to continue
then 70% of the time there will be 2 tornadoes that occur (rounded from 1.764)
and 20% of the time 8 will occur (rounded from 7.612). In this exercise another
task was to calculate the z scores for counties and how many tornadoes occur in
them. It was required to calculate this for 3 separate counties, Russel KS,
Cado OK, and Alfalfa OK. The z scores were as follows Russel: 4.8837, Cado:
2.0930 and Alfalfa: 0.2326 these are visible in figure 7
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| Figure 7 |
which is the
chloropleth map of standard deviations for the number of tornadoes occurring in
the Oklahoma and Kansas counties. Russel County here is on the higher end
though is not the highest value in the data set. The Z score being abnormally
high indicates that there is a large amount of tornadoes actually occurring in
this given county.
In this
exercise the task of using the datasets provided to determine whether it would
be rational to require the construction of severe weather shelters in Oklahoma
and Kansas. Though this analysis things such as mean, mean center, standard distance
and Z score were examined to answer this research question. It was clear that
in the first data set the tornado locations were concentrated in south eastern Oklahoma
which drew the weighted mean center south east. But as time went on the amount
and severity of the tornadoes trended towards occurring in Kansas which dragged
the mean center and weighted mean center for that data set north and northwest
respectively. This means that even though
the location and severity of the tornadoes may have shifted the fact that the
amount of tornadoes is still very high remains. Therefore I would suggest that
people would build these severe storm shelters but not make it a requirement. I
would especially suggest this if the citizens lived within 1 standard distance
of the mean center because the likelihood of a tornado occurring there is much
higher.
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| Figure 3 |
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| Figure 7 |
