DAV Term Project (Team 2)

Analysis of NOAA Storm Data

Data Analysis and Visualization Term Project

Louis HillKalen Cherry

Duncan McNairGrady Bourguignon

Royce “Trey” Duncan


ContentsExecutive 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

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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.

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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 because of the extensive property damage produced outlier data points.P a g e 3 | 10


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

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Figure 1: Hover over for % of state damage


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 Debris Flow 1Dust Devil 2Flash Flood 3Flood 4Funnel Cloud 5Hail 6Heavy Rain 7Lightning 8Marine Hail 9Marine High Wind 10Marine Strong Wind 11Thunderstorm Wind 12Tornado 13Waterspout 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 seem to happen this time of year.

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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.


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.

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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 DamageFigure 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.

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The last question we answered with visualizations was the break-down of deaths versus

injuries.

Death Vs. Injury Graphs

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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.


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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Documents

DAV Term Project (Team 2)