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ANALYSIS OF NOAA STORM DATA
Data Analysisand VisualizationTerm Project
Louis Hill
Kalen Cherry
Duncan McNair
Grady Bourguignon
Royce “Trey” Duncan
Analysisof NOAA StormData
P a g e 1 | 9
Contents
Executive Analysis...............................................................................................................................2
Data Analysis......................................................................................................................................3
Total Damage Cost by State Graph (Omitting Texas) .........................................................................3
Total Damage Cost by Counties in Texas...........................................................................................4
Forecasted Event Data....................................................................................................................5
Event Type Power Map ...................................................................................................................6
Power View of Injuries vs. Property Damage.....................................................................................7
Death Vs. Injury Graphs...................................................................................................................8
Conclusion .........................................................................................................................................9
Analysisof NOAA StormData
P a g e 2 | 9
Executive Analysis
From our analysis of the 2016 NOAA storm data we drew several recommendations and
conclusions that could be pushed forth to geographical areas within the United States to help
reduce damage costs, potential injuries, and even help to save lives. This data stands to help
future generations better understand certain risks and implications of the types of weather
events that hit the United States, where they hit, and what times of year they impact us.
The questions we answered:
 The types of storm events that happen, when and where.
 The extensive property and crop damage, and where it happened.
 Break down of what events caused the most injuries and property damage.
 Death and Injury broken down on a state by state basis with event type.
 Forecasted data models showing us what types of storms are likely to happen in the
future.
We recommend that people living in the mid-west purchase hail insurance, and install tornado
shelters on their property. If you live in the southern portion of the country you should
purchase flood insurance, more so the closer you are to the coast. The northern eastern portion
of the country should also be aware of coastal flooding and the potential for blizzard like
conditions. Wind conditions in the western portion of the country can also produce violent
debilitating dust storms. We also recommend increasing your life insurance policy if you choose
to live in Louisiana. If you move to Texas, you should spend a lot extra on personal property
insurance.
Analysisof NOAA StormData
P a g e 3 | 9
Data Analysis
In this portion of the report we will give an break down of the data we analyzed. The data set
we captured included several points that we had to clean up so that we could accurately query
the power views that we wanted. We began by splitting the data into a few sets so that it would
be easier to answer our independent questions.
Total Damage Cost by State Graph (Omitting Texas)1
This visualization shows the breakdown of every state, omitting Texas. The lowest cost’s
happened in Alaska and Delaware, only suffering $11,000 in reported property damage in 2016.
Inversely, however, West Virginia and Louisiana broke the $100,000,000 mark. Louisiana alone
accounted for 21% of all the countries property damage with West Virginia at 17%; omitting
Texas. Texas on the other hand would have contributed over half of the total damage, for this
reason we decided to split it into its own visualization, broken down by counties.
1 This visualization omitted Texas becauseof the extensive property damage produced outlier data points.
Analysisof NOAA StormData
P a g e 4 | 9
Total Damage Cost by Counties in Texas
Looking at the numbers for Texas counties it’s clear why we initially recommended Texans
purchase property damage insurance. For people living in Falls county, they can consider
themselves very lucky at only $10,000 in property damage in 2016. However, for those in Collin
and Tarrant counties they weren’t as lucky. Tarrant county was
hit with $601,144,100 in property damage but even they were
lucky compared to Collin residents. Collin residents suffered
$874,043,480 alone, that’s more property damage in one county
than the rest of the United States. Texas alone accounted for a
grand total of $1,893,761,640 of property damage in 2016, that is
Figure 1: Hover over for % of state
damage
Analysisof NOAA StormData
P a g e 5 | 9
almost two-billion dollars. Texas storms accounted for a grand total of 71% of the United States
property damage.
Forecasted Event Data
Storm Type Storm Code
DebrisFlow 1
Dust Devil 2
FlashFlood 3
Flood 4
Funnel Cloud 5
Hail 6
HeavyRain 7
Lightning 8
Marine Hail 9
Marine High Wind 10
Marine Strong Wind 11
ThunderstormWind 12
Tornado 13
Waterspout 14
From the forecast, we concluded that August and September tend to be lightning storms
producing heavy rain, hail, marine hail, and several very strong winds with a high frequency of
tornadic and waterspout activity. However, not a lot of dust devil, debris flow, flooding, and
flash flooding seemto happen this time of year.
For the next visualization, we created a
storm event forecast. Due to storm data
type not having a numeric variable we
created a handy chart that allowed us to
model this data. As the data moves to a
certain point on the vertical axis it correlates
to the type of storm that could be produced.
Analysisof NOAA StormData
P a g e 6 | 9
Event Type Power Map
With this visualization, we wanted to show what types of storms are happening, where they are
happening, and how severe they are. We also managed to add a timeline in so that we can have
working movies that visualize the data as it happens over the 2016 time line. As you can see
from the data the west coast suffered from heavy rains, while the center of the country got
pummeled by heavy hail, and the eastern portion got slammed by Debris flow and
thunderstorm winds. The coastal areas encountered a lot of marine thunder storm winds, it is
also possible to see where Alaska had some flooding issues as well.
The power-map feature is probably the most intuitive visualization in our whole project, it is
easy to understand the data and what it means at it unfolds in a real-time movie. An
interesting fact we discovered was that no one event type hit the exact same longitude and
latitude coordinates more than ten times in 2016.
Analysisof NOAA StormData
P a g e 7 | 9
The next visualization we created was to compare Injuries from events and property damage
from events. This is interesting because certain events produce a lot of property damage but do
not injure many people, where as some events injure people but do not produce a lot of
property damage. What was interesting was that the major type of event that does extensive
property damage and injures a lot of people is the tornado. As Arkansans, we are vastly familiar
with these types of storms, but so it appears that Louisiana gets hit much harder than we do.
Power View of Injuries vs. Property Damage
Figure 2: Texas hail, causing major damage.
Thunderstorm winds also injured
around 80 people in 2016 and did
an estimated $5,600,000 in
damage. Louisiana alone had
$169,102,480 in property damage
but also due to tornadic events
suffered 99 injuries. New Mexico
suffered 9 injuries to the tune of
$25,140 in damages, while Texas suffered only suffered 21 injuries, but had almost
$2,000,000,000 in damages. Our data suggests that some events do very little property
damage but injure a lot of people, and some events do a lot of property damage but hardly
injure anyone. They true gem of this analysis was that tornadoes are the costliest storm types,
financially and physically.
Analysisof NOAA StormData
P a g e 8 | 9
The last question we answered with visualizations was the break-down of deaths versus
injuries.
Death Vs. Injury Graphs The death versus injury break down is very
interesting as the number of injuries events rise the
number of fatalities also goes up in a linear trend
fashion. The interesting data points are in Texas, and
West Virginia where we see that the typical findings
are inverted. After some investigative work, we
discovered that both massive losses of life were
caused by flash flooding. It seems also that flash
flooding caused much of the deaths in the United
States in 2016.
Analysisof NOAA StormData
P a g e 9 | 9
Conclusion
In conclusion, the purpose of our project was to analyze 2016 NOAA storm data to try and
answer our five research questions. By using the advanced excel techniques we learned this
semester from Dr. Ravi we could expertly create several advanced visualizations that allowed us
to convey the message we wanted to when answering our research questions. Thankfully, we
not only take away a vast knowledge of storm data trends from this project but also skills that
will serve us for the rest of our professional careers.

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DAV Term Project (Team 2)

  • 1. ANALYSIS OF NOAA STORM DATA Data Analysisand VisualizationTerm Project Louis Hill Kalen Cherry Duncan McNair Grady Bourguignon Royce “Trey” Duncan
  • 2. Analysisof NOAA StormData P a g e 1 | 9 Contents Executive Analysis...............................................................................................................................2 Data Analysis......................................................................................................................................3 Total Damage Cost by State Graph (Omitting Texas) .........................................................................3 Total Damage Cost by Counties in Texas...........................................................................................4 Forecasted Event Data....................................................................................................................5 Event Type Power Map ...................................................................................................................6 Power View of Injuries vs. Property Damage.....................................................................................7 Death Vs. Injury Graphs...................................................................................................................8 Conclusion .........................................................................................................................................9
  • 3. Analysisof NOAA StormData P a g e 2 | 9 Executive Analysis From our analysis of the 2016 NOAA storm data we drew several recommendations and conclusions that could be pushed forth to geographical areas within the United States to help reduce damage costs, potential injuries, and even help to save lives. This data stands to help future generations better understand certain risks and implications of the types of weather events that hit the United States, where they hit, and what times of year they impact us. The questions we answered:  The types of storm events that happen, when and where.  The extensive property and crop damage, and where it happened.  Break down of what events caused the most injuries and property damage.  Death and Injury broken down on a state by state basis with event type.  Forecasted data models showing us what types of storms are likely to happen in the future. We recommend that people living in the mid-west purchase hail insurance, and install tornado shelters on their property. If you live in the southern portion of the country you should purchase flood insurance, more so the closer you are to the coast. The northern eastern portion of the country should also be aware of coastal flooding and the potential for blizzard like conditions. Wind conditions in the western portion of the country can also produce violent debilitating dust storms. We also recommend increasing your life insurance policy if you choose to live in Louisiana. If you move to Texas, you should spend a lot extra on personal property insurance.
  • 4. Analysisof NOAA StormData P a g e 3 | 9 Data Analysis In this portion of the report we will give an break down of the data we analyzed. The data set we captured included several points that we had to clean up so that we could accurately query the power views that we wanted. We began by splitting the data into a few sets so that it would be easier to answer our independent questions. Total Damage Cost by State Graph (Omitting Texas)1 This visualization shows the breakdown of every state, omitting Texas. The lowest cost’s happened in Alaska and Delaware, only suffering $11,000 in reported property damage in 2016. Inversely, however, West Virginia and Louisiana broke the $100,000,000 mark. Louisiana alone accounted for 21% of all the countries property damage with West Virginia at 17%; omitting Texas. Texas on the other hand would have contributed over half of the total damage, for this reason we decided to split it into its own visualization, broken down by counties. 1 This visualization omitted Texas becauseof the extensive property damage produced outlier data points.
  • 5. Analysisof NOAA StormData P a g e 4 | 9 Total Damage Cost by Counties in Texas Looking at the numbers for Texas counties it’s clear why we initially recommended Texans purchase property damage insurance. For people living in Falls county, they can consider themselves very lucky at only $10,000 in property damage in 2016. However, for those in Collin and Tarrant counties they weren’t as lucky. Tarrant county was hit with $601,144,100 in property damage but even they were lucky compared to Collin residents. Collin residents suffered $874,043,480 alone, that’s more property damage in one county than the rest of the United States. Texas alone accounted for a grand total of $1,893,761,640 of property damage in 2016, that is Figure 1: Hover over for % of state damage
  • 6. Analysisof NOAA StormData P a g e 5 | 9 almost two-billion dollars. Texas storms accounted for a grand total of 71% of the United States property damage. Forecasted Event Data Storm Type Storm Code DebrisFlow 1 Dust Devil 2 FlashFlood 3 Flood 4 Funnel Cloud 5 Hail 6 HeavyRain 7 Lightning 8 Marine Hail 9 Marine High Wind 10 Marine Strong Wind 11 ThunderstormWind 12 Tornado 13 Waterspout 14 From the forecast, we concluded that August and September tend to be lightning storms producing heavy rain, hail, marine hail, and several very strong winds with a high frequency of tornadic and waterspout activity. However, not a lot of dust devil, debris flow, flooding, and flash flooding seemto happen this time of year. For the next visualization, we created a storm event forecast. Due to storm data type not having a numeric variable we created a handy chart that allowed us to model this data. As the data moves to a certain point on the vertical axis it correlates to the type of storm that could be produced.
  • 7. Analysisof NOAA StormData P a g e 6 | 9 Event Type Power Map With this visualization, we wanted to show what types of storms are happening, where they are happening, and how severe they are. We also managed to add a timeline in so that we can have working movies that visualize the data as it happens over the 2016 time line. As you can see from the data the west coast suffered from heavy rains, while the center of the country got pummeled by heavy hail, and the eastern portion got slammed by Debris flow and thunderstorm winds. The coastal areas encountered a lot of marine thunder storm winds, it is also possible to see where Alaska had some flooding issues as well. The power-map feature is probably the most intuitive visualization in our whole project, it is easy to understand the data and what it means at it unfolds in a real-time movie. An interesting fact we discovered was that no one event type hit the exact same longitude and latitude coordinates more than ten times in 2016.
  • 8. Analysisof NOAA StormData P a g e 7 | 9 The next visualization we created was to compare Injuries from events and property damage from events. This is interesting because certain events produce a lot of property damage but do not injure many people, where as some events injure people but do not produce a lot of property damage. What was interesting was that the major type of event that does extensive property damage and injures a lot of people is the tornado. As Arkansans, we are vastly familiar with these types of storms, but so it appears that Louisiana gets hit much harder than we do. Power View of Injuries vs. Property Damage Figure 2: Texas hail, causing major damage. Thunderstorm winds also injured around 80 people in 2016 and did an estimated $5,600,000 in damage. Louisiana alone had $169,102,480 in property damage but also due to tornadic events suffered 99 injuries. New Mexico suffered 9 injuries to the tune of $25,140 in damages, while Texas suffered only suffered 21 injuries, but had almost $2,000,000,000 in damages. Our data suggests that some events do very little property damage but injure a lot of people, and some events do a lot of property damage but hardly injure anyone. They true gem of this analysis was that tornadoes are the costliest storm types, financially and physically.
  • 9. Analysisof NOAA StormData P a g e 8 | 9 The last question we answered with visualizations was the break-down of deaths versus injuries. Death Vs. Injury Graphs The death versus injury break down is very interesting as the number of injuries events rise the number of fatalities also goes up in a linear trend fashion. The interesting data points are in Texas, and West Virginia where we see that the typical findings are inverted. After some investigative work, we discovered that both massive losses of life were caused by flash flooding. It seems also that flash flooding caused much of the deaths in the United States in 2016.
  • 10. Analysisof NOAA StormData P a g e 9 | 9 Conclusion In conclusion, the purpose of our project was to analyze 2016 NOAA storm data to try and answer our five research questions. By using the advanced excel techniques we learned this semester from Dr. Ravi we could expertly create several advanced visualizations that allowed us to convey the message we wanted to when answering our research questions. Thankfully, we not only take away a vast knowledge of storm data trends from this project but also skills that will serve us for the rest of our professional careers.