Correlation - Hurricane analysis, part 3/3

This is the third part to a three part set of notebooks that process and analyze historic hurricane tracks. In the previous notebooks we saw:

Part 1

  1. Downloading historic hurricane datasets using Python
  2. Cleaning and merging hurricane observations using DASK
  3. Aggregating point observations into hurricane tracks using ArcGIS GeoAnalytics server

Part 2

  1. Visualize the locations of hurricane tracks
  2. Different basins and the number of hurricanes per basin
  3. Number of hurricanes over time
  4. Seasonality in occurrence of hurricanes
  5. Places where hurricanes make landfall and the people are affected

In this notebook you will analyze the aggregated tracks to answer important questions about hurricane severity and how they correlate over time.

Import the libraries necessary for this notebook.

# import ArcGIS Libraries
from arcgis.gis import GIS
from arcgis.geometry import filters
from arcgis.geocoding import geocode
from arcgis.features.manage_data import overlay_layers
from arcgis.geoenrichment import enrich

# import Pandas for data exploration
import pandas as pd
import numpy as np
from scipy import stats

# import plotting libraries
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

# import display tools
from pprint import pprint
from IPython.display import display

# import system libs
from sys import getsizeof

# Miscellaneous imports
import warnings
gis = GIS('', 'arcgis_python', 'amazing_arcgis_123')

Access aggregated hurricane data

Below, we access the tracks aggregated using GeoAnalytics in the previous notebook.

hurricane_tracks_item ='title:hurricane_tracks_aggregated_ga', item_type="feature layer")[0]
hurricane_fl = hurricane_tracks_item.layers[0]

The GeoAnalytics step calculated summary statistics of all numeric fields. However, only a few of the columns are of interest to us.

pprint([f['name'] for f in], compact=True, width=80)
['objectid', 'serial_num', 'count', 'count_col_1', 'sum_col_1', 'min_col_1',
 'max_col_1', 'mean_col_1', 'range_col_1', 'sd_col_1', 'var_col_1',
 'count_season', 'sum_season', 'min_season', 'max_season', 'mean_season',
 'range_season', 'sd_season', 'var_season', 'count_num', 'sum_num', 'min_num',
 'max_num', 'mean_num', 'range_num', 'sd_num', 'var_num', 'count_basin',
 'any_basin', 'count_sub_basin', 'any_sub_basin', 'count_name', 'any_name',
 'count_iso_time', 'any_iso_time', 'count_nature', 'any_nature', 'count_center',
 'any_center', 'count_track_type', 'any_track_type', 'count_current_basin',
 'any_current_basin', 'count_latitude_merged', 'sum_latitude_merged',
 'min_latitude_merged', 'max_latitude_merged', 'mean_latitude_merged',
 'range_latitude_merged', 'sd_latitude_merged', 'var_latitude_merged',
 'count_longitude_merged', 'sum_longitude_merged', 'min_longitude_merged',
 'max_longitude_merged', 'mean_longitude_merged', 'range_longitude_merged',
 'sd_longitude_merged', 'var_longitude_merged', 'count_wind_merged',
 'sum_wind_merged', 'min_wind_merged', 'max_wind_merged', 'mean_wind_merged',
 'range_wind_merged', 'sd_wind_merged', 'var_wind_merged',
 'count_pressure_merged', 'sum_pressure_merged', 'min_pressure_merged',
 'max_pressure_merged', 'mean_pressure_merged', 'range_pressure_merged',
 'sd_pressure_merged', 'var_pressure_merged', 'count_grade_merged',
 'sum_grade_merged', 'min_grade_merged', 'max_grade_merged',
 'mean_grade_merged', 'range_grade_merged', 'sd_grade_merged',
 'var_grade_merged', 'count_eye_dia_merged', 'sum_eye_dia_merged',
 'min_eye_dia_merged', 'max_eye_dia_merged', 'mean_eye_dia_merged',
 'range_eye_dia_merged', 'sd_eye_dia_merged', 'var_eye_dia_merged',
 'track_duration', 'end_datetime', 'start_datetime']

Below we select the following fields for the rest of this analysis.

fields_to_query = ['objectid', 'count', 'min_season', 'any_basin', 'any_sub_basin',
                   'any_name', 'mean_latitude_merged', 'mean_longitude_merged',
                   'max_wind_merged', 'range_wind_merged', 'min_pressure_merged',
                   'range_pressure_merged', 'max_eye_dia_merged', 'track_duration',
                   'end_datetime', 'start_datetime']

Query hurricane tracks into a Spatially Enabled DataFrame

all_hurricanes_df = hurricane_fl.query(out_fields=','.join(fields_to_query), as_df=True)
CPU times: user 1.12 s, sys: 318 ms, total: 1.43 s
Wall time: 4.5 s
(12362, 17)

There are 12,362 hurricanes identified by GeoAnalytics aggregate tracks tool. To get an idea about this aggregated dataset, call the head() method.

0{"paths": [[[59.60000000000002, -17.6000000000...SINOT NAMEDMM7.01854-02-10 18:00:00NaNNaN-19.31857160.639286NaN1854.01NaNNaN1854-02-08 06:00:001.296000e+08
1{"paths": [[[-23.5, 12.5], [-24.19999999999999...NANOT NAMEDNA9.01859-08-26 12:00:00NaN45.014.000000-26.222222NaN1859.02NaN10.01859-08-24 12:00:001.728000e+08
2{"paths": [[[-23.19999999999999, 12.1000000000...NAUNNAMEDNA50.01853-09-12 18:00:00NaN130.026.982000-51.776000924.01853.0353.090.01853-08-30 00:00:001.058400e+09
3{"paths": [[[59.80000000000001, -15.5], [59.49...SIXXXX856017MM13.01856-04-05 18:00:00NaNNaN-20.18538559.573077NaN1856.04NaNNaN1856-04-02 18:00:002.592000e+08
4{"paths": [[[99.60000000000002, -11.5], [98.30...SINOT NAMEDWA13.01861-03-15 18:00:00NaNNaN-12.94076994.183846NaN1861.05NaNNaN1861-03-12 18:00:002.592000e+08

To better analyze this data set, the date columns need to be changed to a format that Pandas understands better. This is accomplished by calling to_datetime() method and passing the appropriate time columns.

all_hurricanes_df['start_datetime'] = pd.to_datetime(all_hurricanes_df['start_datetime'])
all_hurricanes_df['end_datetime'] = pd.to_datetime(all_hurricanes_df['end_datetime'])
all_hurricanes_df.index = all_hurricanes_df['start_datetime']
1854-02-08 06:00:00{"paths": [[[59.60000000000002, -17.6000000000...SINOT NAMEDMM7.01854-02-10 18:00:00NaNNaN-19.31857160.639286NaN1854.01NaNNaN1854-02-08 06:00:001.296000e+08
1859-08-24 12:00:00{"paths": [[[-23.5, 12.5], [-24.19999999999999...NANOT NAMEDNA9.01859-08-26 12:00:00NaN45.014.000000-26.222222NaN1859.02NaN10.01859-08-24 12:00:001.728000e+08
1853-08-30 00:00:00{"paths": [[[-23.19999999999999, 12.1000000000...NAUNNAMEDNA50.01853-09-12 18:00:00NaN130.026.982000-51.776000924.01853.0353.090.01853-08-30 00:00:001.058400e+09
1856-04-02 18:00:00{"paths": [[[59.80000000000001, -15.5], [59.49...SIXXXX856017MM13.01856-04-05 18:00:00NaNNaN-20.18538559.573077NaN1856.04NaNNaN1856-04-02 18:00:002.592000e+08
1861-03-12 18:00:00{"paths": [[[99.60000000000002, -11.5], [98.30...SINOT NAMEDWA13.01861-03-15 18:00:00NaNNaN-12.94076994.183846NaN1861.05NaNNaN1861-03-12 18:00:002.592000e+08

The track duration and length columns need to be projected to units (days, hours, miles) that are meaningful for analysis.

all_hurricanes_df['track_duration_hrs'] = all_hurricanes_df['track_duration'] / 3600000
all_hurricanes_df['track_duration_days'] = all_hurricanes_df['track_duration'] / (3600000*24)

Query landfall tracks layer into a Spatially Enabled DataFrame

We query the landfall tracks layer created in the pervious notebook into a DataFrame.

inland_tracks ='hurricane_landfall_tracks')[0]

fields_to_query = ['min_season', 'any_basin','any_name', 'max_wind_merged', 
                   'min_pressure_merged', 'track_duration','end_datetime', 
                   'start_datetime', 'analysislength']

landfall_tracks_fl = inland_tracks.layers[0]
landfall_tracks_df = landfall_tracks_fl.query(out_fields=fields_to_query).df
04.376642NANOT NAMED-366342480000095.0965.01853.01-36646992000001.317600e+09{'paths': [[[-74.47272727299998, 24], [-74.463...
1117.097286NAUNNAMED-364517280000070.0NaN1854.02-36454752000002.160000e+08{'paths': [[[-99.13749999999999, 26.5699999999...
2256.909588NAUNNAMED-364517280000070.0NaN1854.03-36454752000002.160000e+08{'paths': [[[-102.21739130399999, 27.686956522...

Manage missing sensor data

Before we can analyze if hurricanes intensify over time, we need to identify and account for missing values in our data. Sensor measurements such as wind speed, atmospheric pressure, eye diameter, generally suffer from missing values and outliers. The reconstruct tracks tool has identified 12,362 individual hurricanes that occurred during the past 169 years.

(12362, 19)

Visualize missing records

An easy way to visualize missing records is to hack the heatmap of seaborn library to display missing records. The snippet below shows missing records in yellow color.

missing_data_viz = all_hurricanes_df.replace(0,np.NaN)
missing_data_viz = missing_data_viz.replace(-9999.0,np.NaN)
missing_data_viz['min_pressure_merged'] = missing_data_viz['min_pressure_merged'].replace(100.0,np.NaN)

missing_data_ax = sns.heatmap(missing_data_viz[['max_wind_merged', 'min_pressure_merged',
                                                'max_eye_dia_merged', 'track_duration']].isnull(),
                              cbar=False, cmap='viridis')
missing_data_ax.set_title('Missing values (yellow) visualized as a heatmap')
Text(0.5,1,'Missing values (yellow) visualized as a heatmap')
<Figure size 720x720 with 1 Axes>

All three observation columns - wind speed, atmospheric pressure and eye diameter, suffer from missing values. In general as technology improved over time, we were able to collect better data with fewer missing observations. In the sections below, we attempt to fill these values using different techniques. We will compare how they fare and pick one of them for rest of the analysis.

Missing value imputation

Technique 1: Drop missing values: An easy way to deal with missing values is to drop those record from analysis. If we were to do that, we lose over a third of the hurricanes.

hurricanes_nona = missing_data_viz.dropna(subset=['max_wind_merged','min_pressure_merged'])
(5857, 19)

Technique 2: Fill using median value: A common technique is to fill using median value (or a different measure of centrality). This technique computes the median of the entire column and applies that to all the missing values.

fill_values = {'max_wind_merged': missing_data_viz['max_wind_merged'].median(),
                'min_pressure_merged': missing_data_viz['min_pressure_merged'].median(),
              'track_duration_hrs': missing_data_viz['track_duration_hrs'].median()}
hurricanes_fillna = missing_data_viz.fillna(value=fill_values)

Technique 3: Fill by interpolating between existing values: A sophisticated approach is to interploate a missing value based on two of its closest observations.

hurricanes_ipl = missing_data_viz
hurricanes_ipl['max_wind_merged'] = hurricanes_ipl['max_wind_merged'].interpolate()
hurricanes_ipl['min_pressure_merged'] = hurricanes_ipl['min_pressure_merged'].interpolate()
hurricanes_ipl['track_duration_hrs'] = hurricanes_ipl['track_duration_hrs'].interpolate()

Visualize all 3 techniques

To compare how each of these techniques fared, we will plot the histogram of wind speed column after managing for missing values.

fig, ax = plt.subplots(1,3, sharex=True, figsize=(15,5))
fig.suptitle('Comparing effects of missing value imputations on Wind speed column', 

hurricanes_nona['max_wind_merged'].plot(kind='hist', ax=ax[0], bins=35, title='Drop null values')
hurricanes_fillna['max_wind_merged'].plot(kind='hist', ax=ax[1], bins=35, title='Impute with median')
hurricanes_ipl['max_wind_merged'].plot(kind='hist', ax=ax[2], bins=35, title='Impute via interpolation')
for a in ax:
    a.set_xlabel('Wind Speed')
<Figure size 1080x360 with 3 Axes>

Next, we will plot the histogram of atmospheric pressure column after managing for missing values.

fig, ax = plt.subplots(1,3, sharex=True, figsize=(15,5))
fig.suptitle('Comparing effects of missing value imputations on Pressure column', 

hurricanes_nona['min_pressure_merged'].plot(kind='hist', ax=ax[0], title='Drop null values')
hurricanes_fillna['min_pressure_merged'].plot(kind='hist', ax=ax[1], title='Impute with median')
hurricanes_ipl['min_pressure_merged'].plot(kind='hist', ax=ax[2], title='Impute via interpolation')
for a in ax:
    a.set_xlabel('Atmospheric Pressure')
<Figure size 1080x360 with 3 Axes>

Fill using interpolation preserves shape of the original distribution. So it will be used for further anlaysis.

Does intensity of hurricanes increase over time?

This last part of this study analyzes if there a temporal trend in the intensity of hurricanes. A number of studies have concluded that anthropogenic influences in the form of global climate change make hurricanes worse and dangerous. We analyze if such patterns can be noticed from an empirical standpoint.

Does the number of hurricanes increase over time?

ax = all_hurricanes_df['min_season'].hist(bins=50)
ax.set_title('Number of hurricanes per season')
Text(0.5,1,'Number of hurricanes per season')
<Figure size 432x288 with 1 Axes>

From the previous notebook, we noticed the number of hurricanes recorded has been steadily increasing, partly due to advancements in technology. We notice a reduction in number of hurricanes after 1970s. Let us split this up by basin and observe if the trend is similar.

fgrid = sns.FacetGrid(data=all_hurricanes_df, col='any_basin', col_wrap=3,
                     sharex=False, sharey=False), 'min_season', bins=50)
fgrid.set_axis_labels(x_var='Seasons', y_var='Frequency of hurricanes')
<seaborn.axisgrid.FacetGrid at 0x1a35c9f908>
<Figure size 648x648 with 7 Axes>

Plotting the frequency of hurricanes by basin shows a similar trend with the number of hurricanes reducing globally after 1970s. This is consistent with certain studies (1). However, this is only one part of the story. Below, we continue to analyze if the nature of hurricanes itself is changing, while the total number may reduce.

Does hurricane wind speed increase over time?

To understand if wind speed increases over time, we create a scatter plot of min_season against the max_wind_merged column. The seaborn plotting library can enhance this plot with a correlation coefficient and its level of signifance (p value).

jgrid = sns.jointplot(x='min_season', y='max_wind_merged', data=hurricanes_ipl,
             kind='reg', joint_kws={'line_kws':{'color':'green'}}, height=7, space=0.5)
j = jgrid.annotate(stats.pearsonr)
j = jgrid.ax_joint.set_title('Does hurricane wind speed increase over time?')
<Figure size 504x504 with 3 Axes>

From the plot above, we notice a small positive correlation. Wind speeds are observed to increase with time. The small p-value suggests this correlation (albeit small) is statistically significant. The plot above considers hurricanes across all the basins and regresses that against time. To get a finer picture, we need to split the data by basins and observe the correlation.

# since there are not many hurricanes observed over South Atlantic basin (SA), 
# we drop it from analysis
hurricanes_major_basins_df = hurricanes_ipl[hurricanes_ipl['any_basin'].isin(

Define a function that can compute pearson-r correlation coefficient for any two columns across all basins.

def correlate_by_basin(column_a, column_b, df=hurricanes_major_basins_df, 
                       category_column = 'any_basin'):
    # clean data by dropping any NaN values
    df_nona = df.dropna(subset=[column_a, column_b])
    # loop through the basins
    basins = list(df[category_column].unique())
    return_dict = {}
    for basin in basins:
        subset_df = df_nona[df_nona[category_column] == basin]
        r, p = stats.pearsonr(list(subset_df[column_a]), list(subset_df[column_b]))
        return_dict[basin] = [round(r,4), round(p,4)]
    # return correlation coefficient and p-value for each basin as a DataFrame
    return_df = pd.DataFrame(return_dict).T
    return return_df

Analyzing hurricane wind speed over time by basin

Below we plot a grid of scatter plots with linear regression plots overlaid over them. The seaborn library's lmplot() function makes it trivial to accomplish this in a single command.

fgrid = sns.lmplot('min_season', 'max_wind_merged', col='any_basin', 
                   data=hurricanes_major_basins_df, col_wrap=3,
                   sharex=False, sharey=False, line_kws={'color':'green'})
<Figure size 1080x720 with 6 Axes>

From the scatter plots above, we notice the wind speeds in most basins show a slight positive trend, with North Atlantic being an exception. To explore this further, we compute the correlation coefficient and its p-value below.

wind_vs_season = correlate_by_basin('min_season','max_wind_merged')
print('Correlation coefficients for min_season vs max_wind_merged')
Correlation coefficients for min_season vs max_wind_merged

The table above displays the correlation coefficient of hurricane wind speed over time. Hurricanes over the Southern Pacific basin exhibit a positive trend of increasing wind speeds. The r value over the North Atlantic shows a weak negative trend. Since all the p-values are less than 0.05, these correlations are statistically significant.

Analyzing hurricane category over time by basin

Hurricanes are classified on a Saffir-Simpson scale of 1-5 based on their wind speed. Let us compute this column on the dataset and observe if there are temporal aspects to it.

def categorize_hurricanes(row, wind_speed_column='max_wind_merged'):
    wind_speed = row[wind_speed_column] * 1.152  # knots to mph
    if 74 <= wind_speed <= 95:
        return '1'
    elif 96 <= wind_speed <= 110:
        return '2'
    elif 111 <= wind_speed <= 129:
        return '3'
    elif 130 <= wind_speed <= 156:
        return '4'
    elif 157 <= wind_speed <= 500:
        return '5'
hurricanes_major_basins_df['category_str'] = hurricanes_major_basins_df.apply(categorize_hurricanes, 
hurricanes_major_basins_df['category'] = pd.to_numeric(arg=hurricanes_major_basins_df['category_str'],
                                                      errors='coerce', downcast='integer')

1854-02-08 06:00:00{'paths': [[[59.60000000000002, -17.6000000000...SINOT NAMEDMM7.01854-02-10 18:00:00NaNNaN-19.31857160.639286...1854.01NaNNaN1854-02-08 06:00:00129600000.036.01.5NaNNone
1859-08-24 12:00:00{'paths': [[[-23.5, 12.5], [-24.19999999999999...NANOT NAMEDNA9.01859-08-26 12:00:00NaN45.014.000000-26.222222...1859.02NaN10.01859-08-24 12:00:00172800000.048.02.0NaNNone

2 rows × 21 columns

We will create violin and bar plots to visualize the number of hurricane categories over different basins.

fig, ax = plt.subplots(1,2, figsize=(15,6))
vplot = sns.violinplot(x='any_basin', y='category', data=hurricanes_major_basins_df, ax=ax[0])
vplot.set_title('Number of hurricanes per category in each basin')

cplot = sns.countplot(x='any_basin', hue='category_str', data=hurricanes_major_basins_df,
             hue_order=['1','2','3','4','5'], ax=ax[1])
cplot.set_title('Number of hurricanes per category in each basin')
Text(0.5,1,'Number of hurricanes per category in each basin')
<Figure size 1080x432 with 2 Axes>

We notice all basins are capable of generating major hurricanes (over 3). The Eastern Pacific basin appears to have a larger than the proportional number of major hurricanes. Below, we will regress the hurricane category against time to observe if there is a positive trend.

kde_regplot = sns.jointplot(x='min_season', y='category', 
                            data=hurricanes_major_basins_df, kind='kde', 
kde_regplot.ax_joint.set_title('Scatter plot of hurricane categories over seasons')
Text(0.5,1,'Scatter plot of hurricane categories over seasons')
<Figure size 432x432 with 3 Axes>

Even at a global level, we notice a strong positive correlation between hurricane category and seasons. Below, we will split this across basins to observe if the trend holds well.

wgrid = sns.lmplot('min_season', 'category', col='any_basin', 
                   data=hurricanes_major_basins_df, col_wrap=3,
                   sharex=False, sharey=False, line_kws={'color':'green'})
<Figure size 1080x720 with 6 Axes>
category_corr_df = correlate_by_basin('min_season','category', df=hurricanes_major_basins_df)
print('Correlation coefficients for min_season vs hurricane category')
Correlation coefficients for min_season vs hurricane category

Thus, at both global and basin scales, we notice a positive trend in the number of hurricanes of category 4 and higher, while there is a general reduction in the quantity of hurricanes per season. This is along the lines of several studies [1] [2] [3] [4]. Thus while the total number of hurricanes per season may reduce, we notice an increase in the intensity of them.

Does eye pressure decrease over time?

Just like a high wind speed, lower atmospheric pressure increases the intensity of hurricanes. To analyze this, we produce a scatter grid of min_pressure_merged column and regress it against min_season column. We split this by basins.

pgrid = sns.lmplot('min_season', 'min_pressure_merged', col='any_basin', 
                   data=hurricanes_major_basins_df, col_wrap=3,
                   sharex=False, sharey=False, line_kws={'color':'green'})
<Figure size 1080x720 with 6 Axes>
pressure_corr_df = correlate_by_basin('min_season','min_pressure_merged')
print('Correlation coefficients for min_season vs min_pressure_merged')
Correlation coefficients for min_season vs min_pressure_merged

Lower the atmospheric pressure, more intense is the hurricane. Hence we are looking for strong negative correlation between the pressure and season columns. From the charts and table above, we notice South Pacific basin once again tops the list with a mild negative correlation over time. The p-values of Western Pacific and Eastern Pacific is larger than 0.05, so we disregard their correlation coefficients. Over North American and Indian basins, we notice a weak positive correlation.

Do hurricanes linger longer over time?

While wind speed and atmospheric pressure measure two types of intensities, a neverending hurricane can also hurt the communities affected as it inundates the coast with rainfall and storm surge for longer periods of time. In this section we correlate the track duration in days against seasons.

jgrid = sns.jointplot(x='min_season', y='track_duration_days', data=hurricanes_ipl,
             kind='hex', height=7, space=0.5)
j = jgrid.annotate(stats.pearsonr)

sns.regplot(x='min_season', y='track_duration_days', data=hurricanes_ipl, 
            ax=jgrid.ax_joint, color='green',scatter=False)

j = jgrid.ax_joint.set_title('Does hurricane duration increase over time?')
<Figure size 504x504 with 3 Axes>

At a global scale, we notice an increase in the duration of hurricanes. Below we split this up by basins to get a finer look.

lgrid = sns.lmplot('min_season', 'track_duration_days', col='any_basin', 
                   data=hurricanes_major_basins_df, col_wrap=3,
                   sharex=False, sharey=False, line_kws={'color':'green'})
<Figure size 1080x720 with 6 Axes>
linger_time_corr_df = correlate_by_basin('min_season','track_duration_days')
print('Correlation coefficients for min_season vs track_duration_days')
Correlation coefficients for min_season vs track_duration_days

At most basins, we notice a positive trend in hurricane track duration. An exception to this is the North Atlantic basin where the p-value is not significant enough to let us draw any conclusion.

The trend we notice here could be partly due to technological advancements that allow us to identify and track hurricanes at a very early stage. Hence, to complement this, we will analyze if hurricanes travel longer than usual once they make a landfall.

Do hurricanes travel longer inland over time?

Along the lines of track duration, it is relevant for us to investigate whether hurricanes travel longer inland over time. Thus, we correlate track length column of hurricanes that made landfall against seasons.

jgrid = sns.jointplot(x='min_season', y='analysislength', data=landfall_tracks_df,
                      kind='reg', joint_kws={'line_kws':{'color':'green'}}, 
                      height=7, space=0.5, ylim=[0,2000])
j = jgrid.annotate(stats.pearsonr)
j = jgrid.ax_joint.set_title('Do hurricanes travel longer inland over time?')
<Figure size 504x504 with 3 Axes>
lgrid = sns.lmplot('min_season', 'analysislength', col='any_basin', 
                   data=landfall_tracks_df, col_wrap=3,
                   sharex=False, sharey=False, line_kws={'color':'green'})
<Figure size 1080x1080 with 7 Axes>
linger_distance_corr_df = correlate_by_basin('min_season','analysislength', df=landfall_tracks_df)
print('Correlation coefficients for min_season vs inland track length')
Correlation coefficients for min_season vs inland track length

When we correlated inland track length over time, we were able to unravel an interesting observation. At basins where the correlation is statistically significant, it is negative (North Indian, Western Pacific, South Pacific). Thus while the duration of hurricanes continues to increase (due to reasons discussed previously), we notice hurricanes travel shorter distances inland. This could be problematic of communities affected as the hurricane could remain stagnant and produce stronger than usual storm surges and precipitation.

Correlate observations over time

Let us collect all trend analysis we performed so far into a single DataFrame.

# clean data by dropping any NaN values
subset_cols = ['min_season', 'max_wind_merged',
                'min_pressure_merged', 'track_duration_days', 'category']

df_nona = hurricanes_major_basins_df.dropna(subset=subset_cols)

# loop through the basins
basins = list(df_nona['any_basin'].unique())
return_dict = {}
for basin in basins:
    subset_df =df_nona[df_nona['any_basin'] == basin]
    row_vector = []
    for col in subset_cols[1:]:
        r, p = stats.pearsonr(list(subset_df['min_season']), list(subset_df[col]))
        if p < 0.05:

    return_dict[basin] = row_vector

# return as a DataFrame
return_df = pd.DataFrame.from_dict(return_dict, orient='index', columns=subset_cols[1:])

We can visualize this correlation table as a heat map to appreciate how the hurricane severity indicators correlate over seasons for each basin.

hm_plot = sns.heatmap(return_df, annot=True, linecolor='black', cmap='RdBu_r')
hm_plot.set_title('Heatmap of correlation coefficients of hurricane severity indicators')
Text(0.5,1,'Heatmap of correlation coefficients of hurricane severity indicators')
<Figure size 432x288 with 2 Axes>


In this notebook used the aggregated hurricane tracks produced in part 1 and the landfall tracks layer produced in part 2. We checked for missing sensor observations and imputed those records via interpolation. We then comprehensively analyzed how hurricane severity indicators such as wind speed, atmospheric pressure, track duration, track length inland and category correlate over time (seasons). We noticed the total number of hurricanes have decreased since the 1970s globally and across all basins. While the number of hurricanes is less, we noticed their wind speed, track duration, and category correlate positively, while the atmospheric pressure correlates negatively, against seasons. This aligns with findings from major studies. Thus, we notice a reduction in the number of category 1 and 2, and an increase in the number of category 4 and 5 hurricanes. We noticed the North Indian basin to be highly correlated in this regard.

In this study, through these 3 part notebooks, we saw how to download hurricane data for the past 169 years from NOAA NEIC site over FTP, and how to interact with it initially using Pandas. Since this data is larger than memory for average computers, we learned how to aggregate the columns using the DASK distributed, delayed processing library. We input the result of DASK to the ArcGIS GeoAnalytics server to aggregate these point observations into hurricane tracks. The "reconstruct tracks" tool which performed this aggregation identified 12,362 individual hurricanes across the globe.

In the second notebook, we performed comprehensive visualization and exploratory analysis to understand the geography of hurricanes and the various basins they are categorized into. We observed a strong sinusoidal nature where hurricanes in the northern and southern hemisphere are offset by 6 months. We noticed certain names (Irma, Florence) are more popular than the rest. We overlaid the tracks over land boundary dataset to compute tracks traveled inland and identify places where landfall occur. Through density analysis of the landfall locations, we were able to identify the places that were most affected from a repeat landfall basis. By geo-enriching these places, we learned that China, Hongkong and India are home to the population that is most affected.

Many studies and articles [5] [6] [7] [8] shine light on anthropogenic influences on global Sea Surface Temperature (SST). Higher sea surface temperatures are observed to produce more intense storms; they are attributed to polar ice cap melting and rising sea levels. These combined with increased hurricane intensity can lead to severe storm surges causing increased flooding and damage of the coastal communities. Based on our density and geoenrichment analysis, we noticed these are places along the coast that are densely populated.

This study showcases how a comprehensive spatial data science project can be performed using ArcGIS and open source Python libraries on the ArcGIS Notebook server. The notebook medium makes it convenient to document code, narrative, and graphics in one place. This helps in making research reproducible and approachable. Thus, in this study, we were able to empirically validate the conclusions derived by other studies. In the future, when more hurricane datasets become available, this study can be repeated just by re-running these notebooks.


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