Phillip Hendrickson
May 2011

Introduction
Ice storm events are a common occurrence in North America, particularly in the northern
United States. These events are characterized by a large amount of freezing rain which manifests typically between the months of November and April when warm and cold air masses mix. The most frequent events occur in early and late winter when low temperatures allow for persistent glazing and build up on surfaces (NaturalHazards.org, 2011). Ice storm events result in dangerous conditions and can devastate local economies as well as infrastructures by reducing traction on road surfaces thereby increasing the risk of vehicle collisions; overloading telephone wires and tree branches causing them to break and fall; and last but not least, ice storm events create icy surfaces which cause injuries through slips and falls. In most cases, these events do little harm, but occasionally severe events can hit like the widely publicized “Storm of the
Century” which occurred in January of 1998, lasted for many days leaving four million people without electricity, and causing over $5 billion in damages (NaturalHazards.org, 2011). This project specifically explores the vulnerability of counties within the New England region through the collection of storm data as well as the use of mapping and statistical packages to perform an exploratory multivariate spatial and directional cluster analysis. The goal of this project was to gain knowledge and understanding on a general level of where ice storms are most frequent in
New England based on reliable storm data that spans a 16 year period. Furthermore this analysis produced results that show, on a local level, where ice storms have the potential to occur in the future and has the implication to provide useful information to help in the development of storm warning policy, detection, and risk mitigation.
Study Area
The New England region was chosen for this analysis based on information from NOAA,
NaturalHazards.org, and the work by (Changnon 2002). The six states, Connecticut, Maine,
Massachusetts, New Hampshire, Rhode Island, and Vermont define the New England region and as depicted in figure 2, are partially attributed with the highest number of ice storm catastrophes during 1949 – 2000.
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Fig.1 – ArcMap depiction of CONUS with New England region highlighted.
Fig.2 – The number of ice-storm catastrophes in each climate region during 1949–2000. Values in parentheses are those catastrophes that only occurred within the region. (Changnon 2002)
This region is prone to Nor’easters as well which bring heavy snow to the area and because the
Appalachians trap the cold air in the valleys and low elevations of the region, warm air can advance over-top and mix creating the meteorological conditions necessary for ice storms to occur (Magnus, 1998). This analysis looked at 65 of the 67 counties within the New England states for spatial clustering and as an additional procedure, directional clustering is performed for the 16 counties of Maine using mean aspects.
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Data and Analysis
Data for this analysis was extracted from several sources. A 16 year record of ice storm events dating from 1993 to 2009 was provided by NOAA and filtered through their online database so that only ice storm events or glazing events could be processed for each county in the New England region. Extensive data mining and filtering was performed using Microsoft
Excel to successfully match each storm event for each county which culminated in the creation of a pivot table showing the total number of events per county over the 16 year period. These values were combined with census information provided by the US census Bureau. To get a visual representation of the storm events, ArcMap was used. The features of this mapping software allowed the pivot table from Excel to be spatially joined to the county shapefiles by
FIPS code. Following these preprocessing steps, a cluster analysis was performed in JMP, a freeware statistical package provided by the University of Delaware. The following variables were examined for multivariate cluster analysis:
Latitude (in decimal degrees of county centroids)
Longitude (in decimal degrees of county centroids)
Age65up
Population density
Poverty population
Number of households
Total number of events
To perform this clustering, Ward’s method was chosen as the hierarchical agglomerative. The clusters were saved to the main file containing all calculations to that point. This database file was then reloaded into ArcMap and the clusters were joined to the New England county attribute table. Finally, the clusters were categorically displayed by their vulnerability rankings.
Further Analysis
To explore how this clustering changed when directional data was introduced, a second analysis was conducted. A 30x30 meter DEM provided by GTOPO (created by the USGS) was
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loaded into ArcMap and a spatial extent for data was defined using the New England region as a mask. The aspects from the DEM were calculated and a raster to point procedure was used so that the aspect values could be spatially joined to the counties attribute table. These values were processed in Excel to produce mean aspects for each county in Maine. A final cluster analysis was performed in JMP using only the data values for Maine. The results were combined in
ArcMap and displayed.
Results and Discussion
From the initial spatial clustering in JMP, an optimal amount of clusters was determined at ten using the elbow method introduced by Robert Thorndike in 1953 which follows that k, the optimal amount of clusters, can be determined as the breaking point where the cost of the solution drops significantly. When represented visually in ArcMap, the ranking of vulnerability was:
5 10 3 6 9 7 1 2 4 8
Where cluster 5 was perceived as the most vulnerable because it had the highest frequency of ice storm events as well as the highest number of households and population sizes per county and 8 was the least vulnerable as it had the lowest frequency of ice storm events during the 16 year study period. Based on this analysis, nine counties in the New England region were partitioned into cluster 5 as seen in figure 4.
Fig.3- Spatial clustering of ice storm vulnerability of the New England region in
ArcMap.
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Fig.6 – Most vulnerable counties in New England represented in ArcMap and based on 16 year storm record from NOAA as well as data from US Census
The secondary analysis incorporated the mean aspects from the counties in Maine. Using the elbow method, k was optimized at three where cluster 1 was considered the most vulnerable. In order to properly compare these results with those from the initial spatial clustering, the number of clusters from the initial cluster analysis needed to be changed to three as well. In table 1 below
“CLUST1” represents the initial spatial clustering performed for just Maine and “CLUST2” represents the clustering when the mean aspect was included. The results from the directional clustering are significantly different than those for the initial analysis which indicates that mean aspect plays a significant role in determining ice storm vulnerability or it really has no correlation to the number of ice storm events which affect a county.
Table 1- Results from cluster analysis with mean aspect using JMP
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CLUST1 CLUST2
Fig.7 – Cluster comparisons of Maine counties where “CLUST1” is the initial spatial clustering, and
“CLUST2” represents the clustering with the introduction of mean aspect.
The latter speculation seems more appropriate simply for the fact that there is no meaningful consistency in the directional clustering regarding event totals. Mean aspect is further dismissed as a vulnerability measure because there is such a large variation in the values when examined against the event totals; where Oxford for example, had a mean aspect of 175.72º with an event total of 16, Somerset County with a mean aspect of 176.18 º had only 9 events. Due to these discrepancies, it therefore seems logical to interpret from this analysis that aspect plays little to no part in ice storm susceptibility.
Conclusion
The devastation caused by severe ice storm events can cripple a local economy and result in numerous injuries and even fatalities. Research has been done to characterize the scope of these storms and even to what degree they have affected regions of the United States (Changnon
2002). This analysis explores the vulnerability of those regions particularly New England, by performing multivariate spatial clustering to categorize the frequency of ice storm events on the county level based on a 16 year study period. It was found through this analysis that 9 counties are at the highest risk for future ice storm events based on their population density, elderly population, frequency of events, and total number of households. When the results were
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compared to those from a directional clustering which introduced mean aspect as a variable, the cluster partitions changed significantly and it was ultimately dismissed as a factor in measuring ice storm vulnerability. Despite having to refute mean aspect as a useful variable in measuring ice storm vulnerability however, this exploratory procedure was successful in its goal to gain knowledge and understanding about the localized vulnerability to ice storm events in New
England.
Acknowledgements - Much thanks go out to Professors David Legates, Michael O’ Neal, Tracy
DeLiberty, and John Mackenzie for the advice, direction, and guidance offered during the course of this analysis and for the information they provided to make it possible.
Bibliography
Changnon, Stanley A., 2003: Characteristics of Ice Storms in the United States. J. Appl.
Meteor.
, 42, 630–639.
Grift, Jon., NaturalHazards.org. http://naturalhazards.org/hazards/ice/index.html. 20110509.
Lund, Ulric, 1999: Cluster Analysis for Directional Data. Commun. Statist.-Simula .,28(4),1001-
1009.
Magnus, Melody, 1998: Winter Storms…the Deceptive Killers. http://www.weather.gov/om/brochures/wntrstm.htm. 20110509.
W. J. Rawls and T. J. Jackson, 1979: Pattern Recognition Analysis of Snowdrifts
Nordic Hydrology ., 10, 251-260.
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