Assignment 2

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

Figure 1

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

Figure 4

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

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.

Figure 3

Figure 7