NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Data Visualization > Visualizing Hurricane Data with Shiny

Visualizing Hurricane Data with Shiny

Sean Justice
Posted on Dec 12, 2018
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Motivation for Project

Around the time that I was selecting a topic for this project, my parents and my hometown found themselves in the path of a Category 1 hurricane. Thankfully, everyone was ok, and there was only minor damage to their property. But this event made me think about how long it had been since the last time my hometown had been in the path of a Category 1 hurricane. I also wanted to study data trends in hurricane intensity over time to see if it corresponds to the popular impression that storms have grown stronger  storms over the past few years.

The Dataset

The dataset that I used for this project was provided by the National Hurricane Center. It includes historical data on storms in the Atlantic Ocean. Though this dataset has measurements for hurricanes dating all the way back to 1851, the data from the earlier years is incomplete.

The measurements included in the dataset are of the longitude and latitude of the storm, the maximum sustained wind speed, the minimum pressure, and the time of the measurement. Using the maximum wind speed, the storms are categorized with the Saffir-Simpson scale, which divides the storms between Category 1 and Category 5. The National Hurricane Center also provides data on tropical storms and tropical depressions because they can grow in intensity to hurricane level storms. Visualizing Hurricane Data with Shiny

Objective

I wanted to use R and the shiny framework to create a shiny app for exploring the hurricane data and examining the trends of storm intensity to see if there has been any increase in intensity.

Challenges

Visualizing Hurricane Data with Shiny

Unfortunately, the early tracking data is not complete since it relied on observations that were collected either by ships that encountered one of these storms while at sea or coastal towns that were in the path of a hurricane and happened to have the necessary equipment to collect the desired data. 1851 is the first year with recorded measurements, and there are only six storms that were tracked. There are a couple of the 1851 storms that only have a single data entry point. In comparison, the data for 2017, the latest year available, has 18 storms that were tracked throughout the lifespan of the storm.

I had to make a decision on where to set a cutoff in the data if I wanted to do an analysis of the trends over the years. I considered a few starting points. The year 1950 was when meteorologists started giving names to hurricane level storms, and in 1953 they started the custom of assigning human names that varied from year to year. I ended up choosing 1967 because that is the first hurricane season in which data was collected using weather tracking satellites for all the storms. I still kept the pre-1967 data available for analysis, but the only trend I explored within those years was the peak intensity for each storm.

Mapping the Storms

With the data containing the longitude and latitude for each storm at the time that they were measured, it is possible to recreate the path for each storm. For each path, I colored the location of the measurement according to the maximum wind speed at the time of the measurement. Tropical depressions are yellow, tropical storms are orange, and all hurricane level storms are red.

I also added additional information for each storm in a tooltip; clicking on a point shows the stormโ€™s name, maximum wind speed, minimum pressure, and the time that the storm was measured. This makes it possible to view the path for an individual storm or all of the storms during a year.

Analyzing Storm Data

In the shiny app, I implemented two ways to explore the hurricane data, by year and by storm name (storm number for pre-1950 storms). When exploring by name, you get an analysis of the storm  by the amount of time that it was classified in each category.

For exploring by year, I categorized each storm by the peak wind speed measured over the lifespan of the storm. Because the plots begin to get crowded when two years are compared, I started aggregating the storms by peak intensity in each year. This makes it possible to compare the type of storms that occurred each year.

While working with the data, I found that the best way to compare the intensities of the storms per year was to look at the average length of time for each category during the hurricane season. This makes it easier to compare the composition of the hurricane season.

A  proportional comparison is useful because there can be a large variation in the number of storms per year. For example, comparing the 2008 season against 2009 reveals that 2008 had above average hurricane activity in the Atlantic while 2009โ€™s activity was below average. However, looking at the proportional composition of each season shows that even though there was a large difference in the total number of storms, categorically the years were very similar.

Analyzing Hurricane Data Trends

When viewing the post-1967 only data, I added an option to view all years available for analysis, and selecting this option displays information from all the storms that occured between 1967 and 2017. The count on the y axis is a measure of the number of storms that had a peak intensity for each category. An increase in the intensity of storms is noticeable in that the number of storms that peaked as a tropical depression has steadily dropped over this timeframe while there has been an increase in the number of storms that peaked as tropical storms.

This increasing trend in intensity is more apparent when looking at the proportional composition of all hurricane seasons since 1967. With this view, I can show that the amount of time that storms spend in the tropical depression stage has decreased while the time as a tropical storm has increased. While this plot makes it possible to make the argument that there has been an increase in the overall intensity of the storms since 1967, there are problems with this conclusion.

One of the main issues with this conclusion is that the trend doesnโ€™t include data related to climate change, such as the increase in ocean temperature. Incorporating this additional data would be helpful in making a more conclusive argument. Ultimately more data is required to arrive at an informed conclusion.

Ideas for Future Development

One thing that I learned while analyzing this data is that the Saffir-Simpson scale is a very limited way to categorize storms since it only uses a single variable --maximum wind speed-- to make a classification. I believe that a better metric for intensity would take into account other attributes of a storm like the minimum pressure or area.

Starting in 2004, the National Hurricane Center began taking measurements of the size of the storm in addition to the other measurements that had been taken in the past. I believe that using the area of the storm would be a better way track the intensity of the storm than the Saffir-Simpson scale. Adding this metric along with additional information on ocean temperatures would create a more robust analysis of the historical data.

Another improvement that I would like to make is to implement a way to use the map view to see the progression of a storm from when it was first formed until its final measurement. This would be useful for seeing how a storm develops over time, as well as the ways that the intensity increases and decreases over time and location

About Author

Sean Justice

Data scientist with a background in computer engineering and over a decade of experience working in a team environment to solve challenging problems. Interested in deep learning, especially computer vision and natural language processing.
View all posts by Sean Justice >

Related Articles

Capstone
Catching Fraud in the Healthcare System
Data Analysis
Car Sales Report R Shiny App
Data Analysis
Injury Analysis of Soccer Players with Python
Capstone
The Convenience Factor: How Grocery Stores Impact Property Values
Capstone
Acquisition Due Dilligence Automation for Smaller Firms

Leave a Comment

Cancel reply

You must be logged in to post a comment.

John January 15, 2019
Sean, Awesome article! I use steam for my favorite pass-times. I have a few questions if you have a chance to speak with me. Please let me know how I can reach you and when you are available to chat.
Maรซlle Salmon December 17, 2018
Nice! Just in case you might ignore there's an R package giving access to that hurricane data, https://github.com/ropensci/rrricanes

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application