Data Visualisation : Mapping of Earthquake data and its analysis

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.basemap import Basemap
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')

Data¶

In [2]:

earth_quake = pd.read_csv("earthquake.csv")

In [3]:

earth_quake.head()

Out[3]:

	Date	Time	Latitude	Longitude	Type	Depth	Depth Error	Depth Seismic Stations	Magnitude	Magnitude Type	...	Magnitude Seismic Stations	Azimuthal Gap	Horizontal Distance	Horizontal Error	Root Mean Square	ID	Source	Location Source	Magnitude Source	Status
0	01/02/1965	13:44:18	19.246	145.616	Earthquake	131.6	NaN	NaN	6.0	MW	...	NaN	NaN	NaN	NaN	NaN	ISCGEM860706	ISCGEM	ISCGEM	ISCGEM	Automatic
1	01/04/1965	11:29:49	1.863	127.352	Earthquake	80.0	NaN	NaN	5.8	MW	...	NaN	NaN	NaN	NaN	NaN	ISCGEM860737	ISCGEM	ISCGEM	ISCGEM	Automatic
2	01/05/1965	18:05:58	-20.579	-173.972	Earthquake	20.0	NaN	NaN	6.2	MW	...	NaN	NaN	NaN	NaN	NaN	ISCGEM860762	ISCGEM	ISCGEM	ISCGEM	Automatic
3	01/08/1965	18:49:43	-59.076	-23.557	Earthquake	15.0	NaN	NaN	5.8	MW	...	NaN	NaN	NaN	NaN	NaN	ISCGEM860856	ISCGEM	ISCGEM	ISCGEM	Automatic
4	01/09/1965	13:32:50	11.938	126.427	Earthquake	15.0	NaN	NaN	5.8	MW	...	NaN	NaN	NaN	NaN	NaN	ISCGEM860890	ISCGEM	ISCGEM	ISCGEM	Automatic

5 rows × 21 columns

In [4]:

earth_quake.columns

Out[4]:

Index(['Date', 'Time', 'Latitude', 'Longitude', 'Type', 'Depth', 'Depth Error',
       'Depth Seismic Stations', 'Magnitude', 'Magnitude Type',
       'Magnitude Error', 'Magnitude Seismic Stations', 'Azimuthal Gap',
       'Horizontal Distance', 'Horizontal Error', 'Root Mean Square', 'ID',
       'Source', 'Location Source', 'Magnitude Source', 'Status'],
      dtype='object')

In [5]:

earth = earth_quake[["Date","Latitude","Longitude","Magnitude"]]

In [6]:

earth.head()

Out[6]:

	Date	Latitude	Longitude	Magnitude
0	01/02/1965	19.246	145.616	6.0
1	01/04/1965	1.863	127.352	5.8
2	01/05/1965	-20.579	-173.972	6.2
3	01/08/1965	-59.076	-23.557	5.8
4	01/09/1965	11.938	126.427	5.8

In [7]:

earth.tail()

Out[7]:

	Date	Latitude	Longitude	Magnitude
23407	12/28/2016	38.3917	-118.8941	5.6
23408	12/28/2016	38.3777	-118.8957	5.5
23409	12/28/2016	36.9179	140.4262	5.9
23410	12/29/2016	-9.0283	118.6639	6.3
23411	12/30/2016	37.3973	141.4103	5.5

In [8]:

earth["Date"] = pd.to_datetime(earth["Date"])

In [9]:

earth.shape

Out[9]:

(23412, 4)

Creating a Basemap instance¶

In [10]:

m = Basemap(projection="mill")

Converting from spherical to cartesian coordinates¶

In [11]:

longitudes = earth["Longitude"].tolist()
latitudes = earth["Latitude"].tolist()
x,y = m(longitudes,latitudes)

Generating a Scatter Plot¶

In [12]:

fig = plt.figure(figsize=(12,10))
plt.title("All affected areas")
m.scatter(x,y, s = 4, c = "blue")
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
m.drawmapboundary()
m.drawcountries()
plt.show()

The Severity of an Earthquake¶

Minimum and Maximum Magnitude¶

In [13]:

minimum = earth["Magnitude"].min()
maximum = earth["Magnitude"].max()
average = earth["Magnitude"].mean()

print("Minimum:", minimum)
print("Maximum:",maximum)
print("Mean",average)

Minimum: 5.5
Maximum: 9.1
Mean 5.882530753460003

In [14]:

(n,bins, patches) = plt.hist(earth["Magnitude"], range=(0,10), bins=10)
plt.xlabel("Earthquake Magnitudes")
plt.ylabel("Number of Occurences")
plt.title("Overview of earthquake magnitudes")

print("Magnitude" +"   "+ "Number of Occurence")
for i in range(5, len(n)):
    print(str(i)+ "-"+str(i+1)+"         " +str(n[i]))

Magnitude   Number of Occurence
5-6         16058.0
6-7         6616.0
7-8         698.0
8-9         38.0
9-10         2.0

• Over 16,000 (68.5%) earthquakes magnitude were between 5 and 6
• Over 40 (0.17%) earthquakes magitude were greater than 8.

In [15]:

plt.boxplot(earth["Magnitude"])
plt.show()

In [16]:

highly_affected = earth[earth["Magnitude"]>=8]

In [17]:

print(highly_affected.shape)

(40, 4)

In [18]:

longitudes = highly_affected["Longitude"].tolist()
latitudes = highly_affected["Latitude"].tolist()
x,y = m(longitudes,latitudes)

fig = plt.figure(figsize=(12,10))
plt.title("Highly affected areas affected areas")
m.scatter(x,y,  c = "blue", s = highly_affected["Magnitude"] *20)
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
m.drawmapboundary()
m.drawcountries()
plt.show()