In [1]:
import numpy as np
In [4]:
import pandas as pd
import geopandas as gpd
import geopandas as gpd
from shapely.geometry import Point
import matplotlib.pyplot as plt
import seaborn as sns
In [6]:
pip install seaborn

Requirement already satisfied: seaborn in /usr/local/lib/python3.10/site-packages (0.13.2)
Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.10/site-packages (from seaborn) (3.6.2)
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[notice] A new release of pip available: 22.3.1 -> 25.0
[notice] To update, run: python3.10 -m pip install --upgrade pip
Note: you may need to restart the kernel to use updated packages.
In [8]:
import numpy as np
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [9]:
#United_states data
country = gpd.read_file('data_united_states.json')
country.head()
Out[9]:
GEO_ID STATE NAME LSAD CENSUSAREA geometry
0 0400000US01 01 Alabama 50645.326 MULTIPOLYGON (((-88.12466 30.28364, -88.08681 ...
1 0400000US02 02 Alaska 570640.950 MULTIPOLYGON (((-166.10574 53.98861, -166.0752...
2 0400000US04 04 Arizona 113594.084 POLYGON ((-112.53859 37.00067, -112.53454 37.0...
3 0400000US05 05 Arkansas 52035.477 POLYGON ((-94.04296 33.01922, -94.04304 33.079...
4 0400000US06 06 California 155779.220 MULTIPOLYGON (((-122.42144 37.86997, -122.4213...
In [10]:
type(country)
Out[10]:
geopandas.geodataframe.GeoDataFrame
In [11]:
country.plot(figsize=(20,20));
In [16]:
country = country[(country['NAME'] != 'Alaska') & (country['NAME'] != 'Hawaii')]
country.plot(figsize=(10,10), color='#3B3C6E');
In [17]:
#Now, I have the US map, let's analyze florence data:
#hurricane Florence
florence = pd.read_csv('stormhistory.csv')
florence.head()
Out[17]:
AdvisoryNumber Date Lat Long Wind Pres Movement Type Name Received Forecaster
0 1 08/30/2018 11:00 12.9 18.4 30 1007 W at 12 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 10:45 Avila
1 1A 08/30/2018 14:00 12.9 19.0 30 1007 W at 12 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 13:36 Avila
2 2 08/30/2018 17:00 12.9 19.4 30 1007 W at 9 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 16:36 Avila
3 2A 08/30/2018 20:00 13.1 20.4 30 1007 W at 11 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 19:44 Beven
4 3 08/30/2018 23:00 13.2 20.9 35 1007 W at 13 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 22:42 Beven
In [19]:
florence.plot()
Out[19]:
<AxesSubplot: >
In [21]:
#convert this to a geopandas.GeoDataFrame
from shapely.geometry import Point

# Add new-lines for clarity
florence['coordinates'] = [
    Point(-long, lat)
    for long, lat
    in zip(florence['Long'], florence['Lat'])]
In [22]:
florence = gpd.GeoDataFrame(florence, geometry='coordinates')
florence.head()
Out[22]:
AdvisoryNumber Date Lat Long Wind Pres Movement Type Name Received Forecaster coordinates
0 1 08/30/2018 11:00 12.9 18.4 30 1007 W at 12 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 10:45 Avila POINT (-18.4 12.9)
1 1A 08/30/2018 14:00 12.9 19.0 30 1007 W at 12 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 13:36 Avila POINT (-19 12.9)
2 2 08/30/2018 17:00 12.9 19.4 30 1007 W at 9 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 16:36 Avila POINT (-19.4 12.9)
3 2A 08/30/2018 20:00 13.1 20.4 30 1007 W at 11 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 19:44 Beven POINT (-20.4 13.1)
4 3 08/30/2018 23:00 13.2 20.9 35 1007 W at 13 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 22:42 Beven POINT (-20.9 13.2)
In [24]:
florence.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 105 entries, 0 to 104
Data columns (total 12 columns):
 #   Column          Non-Null Count  Dtype
---  ------          --------------  -----
 0   AdvisoryNumber  105 non-null    object
 1   Date            105 non-null    object
 2   Lat             105 non-null    float64
 3   Long            105 non-null    float64
 4   Wind            105 non-null    int64
 5   Pres            105 non-null    int64
 6   Movement        105 non-null    object
 7   Type            105 non-null    object
 8   Name            105 non-null    object
 9   Received        105 non-null    object
 10  Forecaster      104 non-null    object
 11  coordinates     105 non-null    geometry
dtypes: float64(2), geometry(1), int64(2), object(7)
memory usage: 10.0+ KB
In [26]:
florence.describe()
Out[26]:
Lat Long Wind Pres
count 105.000000 105.000000 105.000000 105.000000
mean 25.931429 56.938095 74.428571 981.571429
std 7.975917 20.878865 36.560765 22.780667
min 12.900000 18.400000 25.000000 939.000000
25% 18.900000 41.000000 40.000000 956.000000
50% 25.100000 60.000000 70.000000 989.000000
75% 33.600000 76.400000 105.000000 1002.000000
max 42.600000 82.900000 140.000000 1008.000000
In [29]:
type(florence)
Out[29]:
geopandas.geodataframe.GeoDataFrame
In [30]:
#florence.plot()
florence.plot(figsize=(20,20));
In [ ]:
In [31]:
#plotting the hurricane position on the US map
country.plot()
florence.plot(color='black', markersize=10)
Out[31]:
<AxesSubplot: >
In [33]:
#fig, ax = plt.subplots(1, figsize=(30, 20))

#country.plot(ax=ax)
#florence.plot(color='black', markersize=10, ax=ax)
In [58]:
#fig, ax = plt.subplots(1, figsize=(15, 7))

#country.plot(ax=ax,color='#3B3C6E')
#florence.plot(color='green', markersize=10, ax=ax)
#florence.plot(ax=ax, column='Wind', marker="<", markersize=10, cmap='cool')
#_ = ax.axis('off')

#plt.legend()
#ax.set_title("Hurricane Florence in US Map", fontsize=25);
In [74]:
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.lines import Line2D
import geopandas as gpd

# figure and axis
fig, ax = plt.subplots(1, figsize=(15, 7))

# Ploting US map
country.plot(ax=ax, color='#3B3C6E', edgecolor="white", linewidth=0.5)

# Ploting Hurricane Florence data with color mapping
florence_plot = florence.plot(ax=ax, column='Wind', marker="<", markersize=10, cmap='cool')

# To Show latitude and longitude
ax.set_xlabel("Longitude", fontsize=12)
ax.set_ylabel("Latitude", fontsize=12)
#ax.grid(True, linestyle="--", alpha=0.5)  # If I want to Add grid

# Adding colorbar for Wind Speed
sm = plt.cm.ScalarMappable(cmap='cool', norm=mcolors.Normalize(vmin=florence["Wind"].min(), vmax=florence["Wind"].max()))
sm._A = []  # Dummy array for colorbar
cbar = fig.colorbar(sm, ax=ax, shrink=0.6)
cbar.set_label("Wind Speed (mph)")

# Manually creating legend for hurricane markers and map boundary
legend_elements = [
    Line2D([0], [0], marker="<", color='w', markerfacecolor='purple', markersize=10, label="Hurricane Florence"),
    Line2D([0], [0], color='#3B3C6E', lw=4, label="U.S. Map")
]

ax.legend(handles=legend_elements, loc="lower left")

# title
ax.set_title("Geospatial Analysis of Hurricane Florence Across the U.S. (2018)", fontsize=20)

# plot
plt.show()
In [ ]: