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Week 8: Assignment 4 Data Analysis Report
July 11, 2022
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Table of Contents
Data Visualization ......................................................................................................................................................................................................... 3
Data Visualization 1 – Geospatial Analysis ....................................................................................................................................................... 4
Data Visualization 2 – Time Series Graph ........................................................................................................................................................ 6
Word Cloud ........................................................................................................................................................................................................... 9
Proposed Visualizations ...................................................................................................................................................................................... 10
References ............................................................................................................................................................................................................ 12
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Data Visualization
Data visualization can be defined as the representation of data in the form of visual elements such as graphical representations, like
charts, figures, etc. In the project relating to storm casualties, data visualization has been used to present the statistical information
in a simple and understandable manner. A total of three kinds of data visualizations have been used in the study. Geospatial analysis
is the first visualization that has been used to capture the total number of casualties in every state of the U.S. in 2017. The time
series graph is the second data visualization that has been used to highlight the storm fatalities that occurred in a monthly as well
as daily basis. The third data visualization is a word cloud (Midway, 2020). With the help of the visualization technique, the 60
most frequently used words have been identified in the context of storm narration. The role and contribution of each of the three
data visualizations is of high value in the research context.
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Data Visualization 1 – Geospatial Analysis
The geospatial map captures the total number of fatalities including direct and indirect that occurred in the U.S. in 2017. A key
highlight of the visualization is that it has helped to identify the ‘location’ factor which is paramount importance in case of a storm
event. Location can influence the type of storm that occurs. For example, coastal regions are more prone to storms as compared to
interior regions.
The map of the country of United States and its states have been captured in the visualization. States with lower fatalities are
colored green, whereas states with higher mortalities have been colored red. For making the visualization Tableau was used. The
storm data was imported in addition to the location database, and they were integrated together.
‘Total deaths’ has been newly created by combining both direct as well as indirect deaths. By creating this field, it was possible
to get a comprehensive insight into storm-related deaths (Midway, 2020). A key observation that is made is that Texas has maximum
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casualties with 190 deaths. The reason for the same is Hurricane Harvey. It has also been observed that in Nevada there are
numerous storm-related mortalities. 122 people died due to storms in the region during the year 2017.
The pie chart shows the storm types that lead to casualties in the states of Texas and Florida. Tropical storms caused the death of
49 individuals and hurricanes caused the death of 25 people in Florida. Most deaths in Texas can be contributed to tropical storms
or flash floods.
A key visualization element that changes the scope of the analysis is the location factor. For example, Texas has a high risk of
storms, but the possibility of death is low. Similarly, in Great Plains, the storm-related fatalities are low. The storm-related deaths
could fluctuate significantly in a year. So, a more detailed study must be conducted by considering the facts and data for at least a
decade (Midway, 2020).
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Data Visualization 2 – Time Series Graph
The second visualization in the study is the time series graph. The details that have been presented help to identify storm-related
death at a micro level since the day-wise and month-wide information has been captured. The top graph highlights the deaths that
occurred every day in 2017, whereas the bottom graph shows deaths that occurred each month of the year.
Both direct and indirect deaths have been integrated to get a complete image of the storm-related causalities. The visualization is
valuable since it sheds light on the exact time of the year when a storm becomes life-threatening. It is necessary to examine every
kind of storm that takes place in a year so that it will be possible to identify the riskiest time of the year when a storm could
strike and risk the lives of people (Midway, 2020).
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The specific visualization has been categorized into two sections. In the first part, the information is presented in the form of a
trend line. It can be observed that each day when storm occurred there was at least one fatality. A bar graph has been used to
present the information on a monthly basis. The exact number of deaths that took place each month have been presented in the
graph. It can be considered to be simplified version of the storm-related details that have been captured in the top graph.
For making the trend series graph, Tableau was used. Both storm details as well as location datasets were used for the data
preparation purpose. As original dataset versions have been used, there exist outliers. For example, the high death toll related to
Hurricane Harvey is an outlier in the dataset. Outliers were not manipulated or eliminated since it could distort the correct depiction
of the storm events in the specific year.
Several vital observations have been made by using the visual representation. The first observation is that in August there are
maximum storm-related deaths. Most of the fatalities took place on August 26, as a result of torrential rainfall that accompanied
Hurricane Harvey (Midway, 2020). The dates that have been considered in the visualization are the beginning and end dates. Thus,
for the severe weather events that have spanned for over a day, the exact date of the death might not be entirely accurate.
Another observation is the low count of storm-related fatalities between March and June months. The death range that took place
within the period is between 45 to 70. Hence this phase can be considered to be safer as compared to the period between July and
September.
A bar graph has also been created for determining whether storm-related casualties are low or high.
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The ten most deadly storms that occurred in the year have been identified. The storms with the maximum deaths have been presented
to the left whereas the weather event with the minimum number of deaths has been placed in the right side. 40 people lost their
lives as a result of tornadoes.
A key finding that has an implication on the research results is that most storm-related deaths occurred in the summer season. The
months when most deaths occurred are July, August, and September. This finding was aligned with my assumption. I had also
assumed that there will lower deaths during the fall and winter. The data showed that during these seasons there were lower deaths.
Only in the month of January there were storm-related casualties. The results affect the scope of the project analysis since it
challenges the belief that a smaller number of fatal storms occur in winter.
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Word Cloud
Word Cloud is the final visualization that has been used in the project. This text mining analysis tool has helped to identify the
texts that are commonly used while narrating deadly storms. The bag of words or tokens method was used while using the
visualization. It involved the dividing of a document into a set of words and investigating the frequency of terms. This approach
helps in recognizing the most frequently used words and understand the importance of certain words in a text. It is useful since it
gives a detailed insight into a text document (Midway, 2020).
In the project relating to storm-related casualties, the word cloud visualization is of paramount importance since it can help in
locating the factors that are related to storm-related deaths. It has served as an ideal visualization tool. However, a key limitation
of Word Cloud is offers restricted context because it mainly emphasizes only keywords that have been separated from the entire
sentences. Moreover, on certain occasions it is difficult to accurately quantify the specific frequency of certain terms. While other
visualization techniques such as pie charts and bar graphs can capture the exact frequency of a variable, Word Cloud does not do
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so. In the specific Word Cloud that was created, a total of six colors have been used. The colors are lime green, orange, yellow,
magenta, bluish-green and blue. Each color denotes a different meaning. Yellow implies words with the maximum frequency whereas
bluish green has been used for the words with the lowest frequency, in the context of the storm-related casualties project. A total
of 60 words were identified using the visualization technique (Midway, 2020).
R has been used for creating the Word Cloud. A number of preprocessing steps were carried out while working on the specific
visualization. For example, punctuation marks, numbers and special characters had to be removed and the narratives relating to storm
events were arranged in a corpus. The words that were irrelevant or unnecessary in the project context were also eliminated. The
four most frequently used words that have been identified include road, home, damage, and flood. The high frequency of the term
‘flood’ indicates that the occurrence of floods was high in comparison to tornadoes. Similarly, the use of terms such as ‘swept’ and
‘drown’ indicate that many people drowned or were swept away as a result of severe weather events.
Certain results of the Word Cloud have an impact on the scope of the analysis. For example, the high frequency of the word
‘damage’ signals that the storms that are destructive in nature are likely to cause higher deaths. Prior to the analysis, I thought that
hurricanes and tornadoes are most dangerous weather events. But according to the visualization, floods are a major source of threat
(Midway, 2020). Another key finding relates to the location of death. The location is a key factor that can come into play and
impact fatalities. For instance, if a person is in a safe location, he might be able to get shelter from a storm and his life might get
saved.
Proposed Visualizations
An additional visualization technique that could be used in the storm casualties project is geospatial map that focuses on property
damage as a result of storms. It could complement the existing geospatial map since it could help to identify whether any correlation
exists between fatalities and property damage or not. An obstacle that may arise while creating the specific visualization relates to
the variable format. As the details are not in numerical format, their conversion would be extremely time-consuming and challenging.
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Another variable that could have been examined in detail while working on the project revolves around storm range. By examining
the variable, it would be possible to uncover whether deadly storms spanning a longer duration leads to higher casualties or not.
For uncovering this specific area, a visualization that I would suggest is the comparison between storm range and deaths relating to
specific storm types. d A series of scatterplots could be used for capturing the range of each storm. It could be potted over the total
number of storm-related deaths. For each kind of extreme weather events such as hurricanes, tornadoes and floods, a separate
scatterplot could be prepared. This visualization would help in making a comparison between storms of the same types. It would
also help in identifying weather the storm range acts as a key factor that can influence the severity and deadly nature of a storm.
Tableau could be used as the tool for making the visualization.
The additional visualizations that have been proposed could add value to the project since they can help to identify the significance
of specific kinds of data that are available in the datasets. I would also recommend a visualization for strengthening the existing
text mining analysis in the study. A correlation plot could be created for identifying whether any association exists between key
terms in storm-related narratives or not. d In order to prepare the plot, lines would be drawn for connecting pairs of correlated terms.
Such a visualization would be useful in the context of the storm casualties project since it would help to understand the context in
which the terms have been used.
The R tool would be used to prepare the visualization. The ‘Rgraphviz’ package can be used because it helps in the implementation
of correlation plots in an effective and simple manner. By using the new visualization, it would be possible to not only identify the
correlation between words but also the strength of the association between the words.
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References
Midway, S. R. (2020). Principles of effective data visualization. Patterns, 1(9), 100141.
