Hurricanes and the Bermuda Triangle
Is there a correlation between high intensity hurricanes and the bermuda triangle?
Introduction
The Bermuda Triangle is a region in the northwestern Atlantic Ocean that is notorious for
ship and plane disappearences. Many reasons for the dangers in this legendary region
have been proposed, some natural and some otherworldy. For this project, the question
"is there any correlation between high intensity hurricanes and the Bermuda Triangle?"
was asked.
Methods
The purpose will be to quantify this relationship using ArcGIS and spatial statistic tools.
This will be achieved by three main analyses:
B) Spatial Autocorrelation (Global Moran's 1): Determines
clustering by location and value. In this instance the intensity
value of the hurricane point shapefiles was used.
3) Determining if high-intensity wind values of hurricanes are clustered in the Bermuda
Triangle region and extrapolating the z-score results to a raster.
The average nearest neighbor index gave a z-score of -17.92. This means that there is less than a 1% chance
that that this clustered pattern could be the result of random chance.
For this method, the hurricane point shapefiles were merged
into one shapefile. That shapefile was run through two
different types of cluster analysis:
The hypothesis was that there is a correlation between high intensity hurricanes and
the Bermuda Triangle. It is common knowledge that hurricanes often follow an
east-to-west track through the latitudes where the bermuda triangle is located.
2) Comparing the relationship between the area covered by the area of all hurricanes
relative the area of the bermuda triangle and the area of the Atlantic Ocean hurricane
extent.
1) Determining if the hurricanes are clustered, random, or dispersed.
1) Determining if hurricanes are clustered, random, or dispersed.
A) Average Nearest Neighbor: Calculates where each point's
nearest neighbor should be if the distribution was random and
gives a z-score based on the actual points. The hurricane
point shapefile was used.
1) Determining if hurricanes are clustered or randomnly dispersed using the spatial statistic
tools Average Nearest Neighbor and Spatial Autocorrelation.
Results
The spatial autocorrelation tool gave a z-score of 54.94. This means that there is a less than 1% likelihood that
this clustered pattern could be the result of random chance.
2) Computing % of hurricane area relative to Bermuda Triangle and
hurricane extent areas.
The total area of the buffered hurricanes within the total hurricane extent is 5,949,960 square miles.
The total area of the total hurricane extent is 26,200,000 square miles.
Thus, there is 5,949,960/26.200,000 = 0.227 × 100 = 22.7% of area covered by hurricanes within the
total hurricane extent.
The total area of the buffered hurricanes within the Bermuda Triangle is 357,459 square miles.
2) Computing % of hurricane area relative to bermuda triangle and
hurricane extent areas.
The total area of the Bermuda Triangle is 554,968 square miles.
The hurricane points were buffered 50 miles, which is the average radius of a hurricane. These buffered points
were clipped into two different extents: the extent of where hurricanes traveled in the time frame, and the extent
of the Bermuda Triangle. These files were then dissolved to prevent overcounting of the areas.
The area covered by the hurricanes was compared to the areas of both extents. The area of all files was
calculated with the calculate geometry function in ArcGIS.
Thus, there is 357,459/554,968=0.644 × 100 = 64.4% of area covered by hurricanes within the Bermuda Triangle.
Therefore, the area covered by hurricanes in the Bermuda Triangle is more than twice as great as that covered in the
total hurricane extent.
3) Determining if high-intensity hurricanes are clustered in the Bermuda
Triangle region and extrapolating the z-score results to a raster.
Hurricane Extent
Data
Hurricane Intensity Clustering
Z-score Value
Bermuda Triangle
Buffered Hurricane Points
High : 9.86225
Low : -7.36511
Hurricane Points
Wind Intensity
Bermuda Triangle
15 - 30
A red value means that there is a high
clustering of high intensity wind values.
A blue value means that there is a high
clustering of low-intensity wind values.
31 - 45
46 - 55
¯
56 - 70
71 - 85
86 - 100
101 - 115
0
1,000 2,000
4,000
Miles
In this map the hurricane points have been buffered to
represent an average hurricane's radius of 50 miles. The
orange box is the extent of hurricane coverage in 2008-2011
and the black triangle is the Bermuda Triangle.
116 - 135
Hurricane Tracks
Bermuda Triangle
3) Determining if high-intensity hurricanes are clustered in the Bermuda
Triangle region and extrapolating the z-score results to a raster.
To perform this analysis, hurricane point shapefiles where merged into one shapefile of all north Atlantic
hurricanes from 2008 to 2011. Then the spatial statistic tool, Hot Spot Analysis with Rendering, was run of that
shapefile with the attribute "intensity" as the focus. This tool allows us to determine hot spots, where there is a high
clustering of high intensity values, and to determine cold spots, where there is a high clutstering of low
values.
¯
0
600
1,200
Also performed was a linear mean direction tool on the merged Hurricane track shapefiles. This gave the mean
direction of travel of the hurricanes.
2,400
Miles
In this map, the Bermuda Triangle is displayed as a yellow triangle and hurricanes are displayed at
six-hour intervals along their track. Darker red colors mean higher intensity winds.
The hurricane data was obtained from the National Hurricane Center (http://www.nhc.noaa.gov/).
The data spanned the years 2008 to 2011. Each hurricane has a line shapefile of the track it traveled
as well as a point shapefile that has points at six-hour intervals as a hurricane traveled. These points have an
intensity attribute that described the wind speed and was used in many situations.
1.0 to 2.0
> 2.0
Bermuda Triangle
Project created by Bill Vosti for the
2012 National Geospatial Technology Skills Competition
6/1/2012
2,000
Miles
Based upon the three analyses, it can be concluded that there is a strong correlation between the Bermuda
Triangle and high-intensity hurricanes. Hurricanes in the north Atlantic are clustered; hurricanes cover
a greater area in the Triangle than the surrounding hurricane extent area; and the high-intensity wind values
cluster strongly near and in the Triangle.
The ocean basemap was obtained from ArcGis Online.
All the data uses the geographic coordinate system GCS_WGS_1984, as this is the coordinate system for the
hurricane files.
1,000
Conclusions, Issues and Future Research
-1.0 to 1.0
The hurricane shapefiles were combined into one large shapefile for most of the analyses.
The Bermuda Triangle polygon was created by myself using the following end points: Miami, Florida, Bermuda,
and San Juan, Puerto Rico.
500
In this final map, we can see by the raster interpolation of the high and low z-scores that there is a clear pattern of high
clustering of high intensity wind values for hurricanes, displayed as the colors go warmer, in the Bermuda Triangle area.
As well, the linear mean direction tells us that the hurricanes travel in a mean direction directly towards the Triangle.
Hot Spot Analysis
GiZScore
!
( < -2.0
! -2.0 to -1.0
(
!
(
!
(
!
(
¯
0
¯
0
650
1,300
2,600
Miles
In this map, the red points represent high clustering of
high-intensity wind values for hurricanes. The blue points
represent high clustering of high intensity wind values
for hurricanes.
This analysis gave a z-score result. High positive z-scores meant a high clustering of high-intensity winds;
high negative z-scores meant a high clustering of low-intensity winds. These z-scores were then interpolated to
a raster using a the inverse-weighted-distance tool, which is displayed in the final map in the right column.
It cannot be said from this analysis that the reason for the Triangle's dastardly reputation is solely because of
hurricanes, but one can reason that it could be one of the causes of various shipwrecks and plane crashes
in the region. Certainly before modern weather predictions, an often traveled trade route such as in the
Triangle's area could have wreaked havoc on ships and early aviators as they encountered incredibly strong
storms out of the blue.
An issue to take into consideration is that when calculating the area of the hurricanes relative to the Triangle
and hurricane extent is that hurricanes are not counted if they are overlapped. This can be avoided by
double-counting the area of hurricanes that overlap, but then you will have a percentage value above 100%..
For future research, more data would be key. Having a greater database of hurricane and of Triangle incidents
could lead to a greater statistical signifigance in these findings. It would also be prudent to find a way to
quantify the clustering within the Triangle compared to the entire hurricane extent, rather than relying on
the eye test.