events = importfile("StormEvents_2017_finalProject.csv");
% Put months in correct order
monthOrder = ["January", "February", "March", "April", "May", "June", "July",...
"August", "September", "October", "November", "December"];
events.Month = reordercats(events.Month, monthOrder);
% Set missing Property and Crop Cost to $0
events.Property_Cost(ismissing(events.Property_Cost)) = 0;
events.Crop_Cost(ismissing(events.Crop_Cost)) = 0;
% Add total damage to the table
events.Total_Damage = events.Property_Cost + events.Crop_Cost;
harveyEvents = events(events.Begin_Date_Time >= "2017-08-17 00:00:00" & events.End_Date_Time < "2017-09-04 00:00:00", :);
head(harveyEvents)
| EpisodeID | Event_ID | State | Year | Month | Event_Type | CZ_Name | Begin_Date_Time | Timezone | End_Date_Time | Injuries_Direct | Injuries_Indirect | Deaths_Direct | Deaths_Indirect | Damage_Property | Property_Cost | Damage_Crops | Crop_Cost | Begin_Lat | Begin_Lon | End_Lat | End_Lon | Episode_Narrative | Event_Narrative | Total_Damage | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 119542 | 726661 | IOWA | 2017 | August | Tornado | STORY | 2017-08-21 16:58:00 | -6 | 2017-08-21 16:59:00 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3000 | 41.9496 | -93.5614 | 41.9378 | -93.5475 | With the help of a c... | "Tornado captured by... | 3000 |
| 2 | 119542 | 726659 | IOWA | 2017 | August | Tornado | BOONE | 2017-08-21 16:52:00 | -6 | 2017-08-21 16:54:00 | 0 | 0 | 0 | 0 | 5 | 5000 | 5 | 5000 | 41.9490 | -93.7256 | 41.9304 | -93.6981 | With the help of a c... | "This tornado was fo... | 10000 |
| 3 | 119542 | 726660 | IOWA | 2017 | August | Tornado | STORY | 2017-08-21 16:54:00 | -6 | 2017-08-21 16:55:00 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4000 | 41.9304 | -93.6981 | 41.9223 | -93.6739 | With the help of a c... | "Tornado found in hi... | 4000 |
| 4 | 119542 | 717362 | IOWA | 2017 | August | Heavy Rain | AUDUBON | 2017-08-20 22:30:00 | -6 | 2017-08-21 08:13:00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41.7200 | -94.9200 | 41.7200 | -94.9200 | With the help of a c... | "Local fire departme... | 0 |
| 5 | 119542 | 717363 | IOWA | 2017 | August | Heavy Rain | POLK | 2017-08-20 23:00:00 | -6 | 2017-08-21 15:00:00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41.7200 | -93.7900 | 41.7200 | -93.7900 | With the help of a c... | "KCCI relayed a view... | 0 |
| 6 | 119542 | 717364 | IOWA | 2017 | August | Thunderstorm Wind | POWESHIEK | 2017-08-21 18:05:00 | -6 | 2017-08-21 18:05:00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41.6959 | -92.4452 | 41.6959 | -92.4452 | With the help of a c... | "Emergency manager r... | 0 |
| 7 | 120232 | 720731 | VIRGINIA | 2017 | August | Heavy Rain | NORFOLK (C) | 2017-08-29 08:30:00 | -5 | 2017-08-29 08:30:00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36.8700 | -76.2700 | 36.8700 | -76.2700 | Low pressure moving ... | "Rainfall total of 3... | 0 |
| 8 | 120232 | 720732 | VIRGINIA | 2017 | August | Heavy Rain | NORTHAMPTON | 2017-08-29 17:21:00 | -5 | 2017-08-29 17:21:00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37.2800 | -75.9982 | 37.2800 | -75.9982 | Low pressure moving ... | "Rainfall total of 6... | 0 |
groupsummary(harveyEvents,"State","sum","Total_Damage");
ans = sortrows(ans,'sum_Total_Damage','descend');
head(ans)
| State | GroupCount | sum_Total_Damage | |
|---|---|---|---|
| 1 | TEXAS | 272 | 7.7493e+10 |
| 2 | LOUISIANA | 85 | 75277000 |
| 3 | NEBRASKA | 62 | 16154000 |
| 4 | NORTH CAROLINA | 59 | 12338500 |
| 5 | WASHINGTON | 2 | 4000000 |
| 6 | FLORIDA | 68 | 2237000 |
| 7 | MINNESOTA | 24 | 1625000 |
| 8 | MISSISSIPPI | 39 | 915000 |
So the most impacted states are Texas and Louisiana.
texasLouisianaevents = harveyEvents(ismember(harveyEvents.State,{'LOUISIANA','TEXAS'}),:);
head(texasLouisianaevents)
| EpisodeID | Event_ID | State | Year | Month | Event_Type | CZ_Name | Begin_Date_Time | Timezone | End_Date_Time | Injuries_Direct | Injuries_Indirect | Deaths_Direct | Deaths_Indirect | Damage_Property | Property_Cost | Damage_Crops | Crop_Cost | Begin_Lat | Begin_Lon | End_Lat | End_Lon | Episode_Narrative | Event_Narrative | Total_Damage | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 119753 | 723472 | TEXAS | 2017 | August | Tropical Storm | MONTGOMERY | 2017-08-25 12:00:00 | -6 | 2017-08-30 00:00:00 | 0 | 0 | 3 | 1 | 7 | 7.0000e+09 | NaN | 0 | NaN | NaN | NaN | NaN | Harvey made landfall... | "Tropical Storm Harv... | 7.0000e+09 |
| 2 | 119753 | 723473 | TEXAS | 2017 | August | Tropical Storm | FORT BEND | 2017-08-26 00:00:00 | -6 | 2017-08-30 00:00:00 | 0 | 0 | 3 | 0 | 8 | 8.0000e+09 | NaN | 0 | NaN | NaN | NaN | NaN | Harvey made landfall... | "Harvey made landfal... | 8.0000e+09 |
| 3 | 119753 | 723449 | TEXAS | 2017 | August | Tropical Storm | GALVESTON | 2017-08-25 12:00:00 | -6 | 2017-08-30 00:00:00 | 0 | 0 | 3 | 3 | 10 | 1.0000e+10 | NaN | 0 | NaN | NaN | NaN | NaN | Harvey made landfall... | "Galveston County ex... | 1.0000e+10 |
| 4 | 119753 | 723474 | TEXAS | 2017 | August | Tropical Storm | SAN JACINTO | 2017-08-25 12:00:00 | -6 | 2017-08-30 00:00:00 | 0 | 0 | 3 | 0 | 350 | 350000000 | NaN | 0 | NaN | NaN | NaN | NaN | Harvey made landfall... | "Slow moving Tropica... | 350000000 |
| 5 | 119753 | 723475 | TEXAS | 2017 | August | Tropical Storm | WALKER | 2017-08-25 12:00:00 | -6 | 2017-08-30 00:00:00 | 0 | 0 | 1 | 0 | 600 | 600000000 | NaN | 0 | NaN | NaN | NaN | NaN | Harvey made landfall... | "Slow moving Tropica... | 600000000 |
| 6 | 119753 | 723648 | TEXAS | 2017 | August | Tropical Storm | POLK | 2017-08-25 12:00:00 | -6 | 2017-08-30 00:00:00 | 0 | 0 | 0 | 0 | 300 | 300000000 | NaN | 0 | NaN | NaN | NaN | NaN | Harvey made landfall... | "Slow moving Tropica... | 300000000 |
| 7 | 120011 | 719146 | TEXAS | 2017 | August | Flash Flood | EL PASO | 2017-08-23 16:15:00 | -7 | 2017-08-23 17:15:00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31.5285 | -106.1346 | 31.5183 | -106.1176 | A surface low was lo... | "Water was about 6 i... | 0 |
| 8 | 120012 | 719147 | TEXAS | 2017 | August | Thunderstorm Wind | EL PASO | 2017-08-25 18:10:00 | -7 | 2017-08-25 18:10:00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31.7715 | -106.5028 | 31.7715 | -106.5028 | An upper high was lo... | "" | 0 |
We will use Histograms, Pie Charts and Geographic plots.
% Histogram of Event Types distribution due to the Hurricane - Harvey,
% in all of USA
histogram(harveyEvents.Event_Type)
xlabel('event types')
ylabel('no. of Occurances')
% Histogram of Event Types distribution due to the Hurricane - Harvey,
% in the states of Texas and Louisiana
histogram(texasLouisianaevents.Event_Type)
xlabel('event types')
ylabel('no. of Occurances')
% Pie Chart on the distribution of event types due to Hurricane - Harvey,
% in all of USA
pie(harveyEvents.Event_Type)
The overall effect of the hurricane must have been thunderstorms and winds.
% Pie Chart on the distribution of event types due to Hurricane - Harvey,
% in the states of Texas and Louisiana
pie(texasLouisianaevents.Event_Type)
So most of the damage in Texas and Louisiana must have been due to floods.
% Geographic plot of the event START locations
% due to the Hurricane - Harvey,
% in all of USA
geobubble(harveyEvents.Begin_Lat,harveyEvents.Begin_Lon,harveyEvents.Total_Damage,harveyEvents.State);
The eastern coastal states have had more effect than the rest of USA.
% Geographic plot of the event START locations
% due to the Hurricane - Harvey,
% in the states of Texas and Louisiana
geobubble(texasLouisianaevents.Begin_Lat,texasLouisianaevents.Begin_Lon,texasLouisianaevents.Total_Damage,texasLouisianaevents.State);
And inside the most affected states, their eastern coast have had the most casualities.
We will use tables to analyse the follwing.
texasEvents = texasLouisianaevents(texasLouisianaevents.State ~= 'LOUISIANA',:);
groupsummary(texasEvents,"CZ_Name");
ans = sortrows(ans,'GroupCount','descend');
head(ans)
| CZ_Name | GroupCount | |
|---|---|---|
| 1 | HARRIS | 21 |
| 2 | GALVESTON | 17 |
| 3 | FORT BEND | 13 |
| 4 | ANGELINA | 12 |
| 5 | BRAZORIA | 12 |
| 6 | SABINE | 12 |
| 7 | BASTROP | 9 |
| 8 | CHAMBERS | 8 |
Harris, Galveston, Fort Bend have had the most events in Texas due to the Hurricane - Harvey.
louisianaEvents = texasLouisianaevents(texasLouisianaevents.State ~= 'TEXAS',:);
groupsummary(louisianaEvents,"CZ_Name");
ans = sortrows(ans,'GroupCount','descend');
head(ans)
| CZ_Name | GroupCount | |
|---|---|---|
| 1 | NATCHITOCHES | 21 |
| 2 | SABINE | 15 |
| 3 | RED RIVER | 9 |
| 4 | WINN | 6 |
| 5 | VERMILION | 4 |
| 6 | CAMERON | 3 |
| 7 | DE SOTO | 3 |
| 8 | UNION | 2 |
Natchitoches, Sabine and Red River have had the most events in Louisiana due to the Hurricane - Harvey.
groupsummary(texasEvents,"CZ_Name","sum","Property_Cost");
ans = sortrows(ans,'sum_Property_Cost','descend');
head(ans)
| CZ_Name | GroupCount | sum_Property_Cost | |
|---|---|---|---|
| 1 | GALVESTON | 17 | 2.0000e+10 |
| 2 | FORT BEND | 13 | 1.6004e+10 |
| 3 | MONTGOMERY | 6 | 1.4000e+10 |
| 4 | HARRIS | 21 | 1.0001e+10 |
| 5 | JEFFERSON | 4 | 3.0000e+09 |
| 6 | BRAZORIA | 12 | 2.0008e+09 |
| 7 | ARANSAS | 2 | 1.9500e+09 |
| 8 | ORANGE | 2 | 1.5000e+09 |
Galveston, Fort Bend and Montgomery have had the highest reported property costs in the state of Texas due to the Hurricane - Harvey. The costs being 16004M and $14B respectively.
groupsummary(louisianaEvents,"CZ_Name","sum","Property_Cost");
ans = sortrows(ans,'sum_Property_Cost','descend');
head(ans)
| CZ_Name | GroupCount | sum_Property_Cost | |
|---|---|---|---|
| 1 | CALCASIEU | 1 | 60000000 |
| 2 | BEAUREGARD | 1 | 15000000 |
| 3 | ACADIA | 1 | 200000 |
| 4 | CAMERON | 3 | 72000 |
| 5 | VERMILION | 4 | 5000 |
| 6 | BIENVILLE | 1 | 0 |
| 7 | BOSSIER | 1 | 0 |
| 8 | CADDO | 1 | 0 |
Calcasieu, Beauregard and Acadia have had the highest reported property costs in the state of Louisiana due to the Hurricane - Harvey. The costs being 15M and $200K respectively.
Hence from the above analysis we can say that the south eastern coast has had the maximum casualities due to the Hurricane - Harvey. The resources can be allocated to the countries of Galveston, Fort Bend and Montgomery in Texas, and to the countries of Calcasieu, Beauregard and Acadia in Louisiana in top priority. And then to Harris in Texas and Natchitoches, Sabine and Red River in Louisiana next. There after to other parts of these two states and other states.
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