Geo Data Visualization: Hurricanes and Tropical Storm

Introduction

The Hurricanes and Tropical Storm dataset was obtained from GitHub. It's published by the National Hurricane Center (NHC) for the purpose of tracking and analyzing tropical cyclones. It includes the wind information at a six-hourly level with the Geo location (Latitude and Longtitude). Here I am using this dataset to demonstrate the power of data visualization using python. For data aggregation and visualization using pyplot, please check out my other page here.

Columns and Metadata

• The dataset here is a combination of both Atlantic and Pacific dataset. 

• Below are the headers:

Data Manipulation

The dataset will need some data manipulation before we can start visualize the information easily. I focused on below:

1. extracting the Year information from the Date. The Date column is integer in the format of YYYYMMDD. Dividing it by 10000 and drop decimals will do the work.

df['Year'] = df.apply(lambda x: round(x['Date']/10000), axis = 1)

2. converting Lat and Long to float with both + and - numbers. 

In this dataset, the Lat and Long were given as strings, ending with E or W for Long, and N or S for Lat. I used where function in numpy to set up criteria. Then, I used regular expression to identify the letters in my string and remove them. Lastly, if the letter is W (for Long) or S (for Lat), I made them the opposite. Below is the example code:

dfmp['Latitude'] = np.where(dfmp['Latitude'].str.contains('N'), dfmp['Latitude'].replace({'[A-Z]': ''}, regex = True).astype(float), dfmp['Latitude'].replace({'[A-Z]': ''}, regex = True).astype(float) * -1) 

3. Data aggregation and subsetting.

This dataset contains data from 1851, however, for my visualization purpose I'd like to be able to focus on one year of my own interest, and maybe limited to the data points where maximum wind were over, let's say, 90 mph. To make my life easier, I used python to construct the data aggregation and subsetting function, where I can pass my criteria as arguments. I named my function as data_filter(). Then I told my function to subset the aggregated data for my for only the year of 2005 and only for those whose max wind were 90 or higher.

data_filter(data, year = 2005, min_wind = 90)