Algorithms · Animation

How to make time-lapse animation of earthquakes with Python?

I have always been fascinated by maps. Sometimes I find myself wandering in Google Earth for hours, I just like it. Recently, while trying to solve a road networks related graph problem, I wanted to visualize the graph nodes (which were cities or addresses in that case) in a real map and quickly found out about the Basemap library integrated over matplotlib in Python. Once I started to play with the Basemap, I loved its features and ease of integration/manipulation of various data using it. Then, I decided to utilize it to get a time-lapse video occurring in my native Turkey and surroundings for the last hundred years. In this post, I want to share that resulting time-lapse video as well as some snapshots along the way and also the code that generated the animation. I hope you enjoy it. Note that here only the earthquakes with magnitudes larger than 5 are shown. Here is the video:

I already knew that Turkey and Greece are in serious earthquake zone, but the data shows here that south Greece is much more prone to earthquakes in the region. The Aegean Sea, both the Turkish and Greek coast are highly risky areas.


  • The earthquake data is obtained from the USGS site:
  • For some more details on map visualizations using Python, check the site:
  • Also, I learned a lot from peak5390 site where you can find a similar tutorial

Earthquake data as heatmap overlaid to Google maps using gmplot library

The whole code can be found below (I used object oriented approach). It is written in Python using Basemap library (and some other supporting ones such as pandas, numpy etc.). Additionally, heatmap of earthquake data overlaid on Google Maps is achieved using the gmplot library:

Please, share your comments/questions below and stay tuned for the next post.

''' Plots selected earthquake data on the map of the world '''

import os
import argparse
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
import gmplot

class EarthquakeData(object):
''' Class storing & manipulating earthquake data '''

def __init__(self):
self.latitude = []
self.longitude = []
self.minLatitude = 0.0
self.maxLatitude = 0.0
self.minLongitude = 0.0
self.maxLongitude = 0.0
self.midLatitude = 0.0
self.midLongitude = 0.0 = ''
self.magnitude = 0.0 = Basemap()

def ReadAndGetData(self, filename):
Reads the data file downloaded from the USGS site and filters as necessary
(e.g. only accept earthquakes with magnitude greater than 5 etc)

# Use pandas to get the USGS data content
df = pd.read_csv(filename) # e.g. filename: USGS_americas_1950_2017_over6.csv

# Use hardcoded min/max Longitude/Latitude for the specific map output
# One can also use other min/max values (up to the user)
self.minLongitude, self.maxLongitude = (18.81, 51.327)
self.minLatitude, self.maxLatitude = (29.155, 47.883)

# Filter data & get only thos larget than 5.0 magnitude
minMagnitudeDesired = 5.0

filteredDf = df[(df['latitude'] >= self.minLatitude) &
(df['latitude'] <= self.maxLatitude) & (df['longitude'] >= self.minLongitude - 3.4) &
(df['longitude'] <= self.maxLongitude - 0.7) & (df['mag'] >= minMagnitudeDesired)]

filteredDf = df

# Filtered data is in panda dataframe format, use df.values.tolist() to convert to list
#self.latitude = df.latitude # Unfiltered
#self.longitude = df.longitude
self.latitude = (filteredDf.latitude).values.tolist()
self.longitude = (filteredDf.longitude).values.tolist() = (filteredDf.time).values.tolist()
self.magnitude = (filteredDf.mag).values.tolist()

# If you don't want filtering, then min/max can be obtained from the read data
#self.minLongitude = min(self.longitude)
#self.maxLongitude = max(self.longitude)
#self.minLatitude = min(self.latitude)
#self.maxLatitude = max(self.latitude)

# Calculate mid-point for longitudes and latitudes to center the map upon
self.midLatitude = 0.5*(self.maxLatitude + self.minLatitude)
self.midLongitude = 0.5*(self.maxLongitude + self.minLongitude)

print "Quake info: \n", self.__str__()

def DrawMap(self):
''' Draw the main background map where the earthquake data will be overlayed

Some other options to check for: = Basemap(resolution='h', # c(crude), l(low), i)intermediate), h(high), f(full) or None
projection='merc', # 'ortho', 'gnom', 'mill'
lat_0=40.320373, lon_0=-74.43,
llcrnrlon=minLon, llcrnrlat= minLat, urcrnrlon=maxLon, urcrnrlat=maxLat )

# It is also possible to download arcgis images through the following command'World_Physical_Map', xpixels = 5000, verbose= False)

fig, ax = plt.subplots(figsize=(16, 9))
fig.patch.set_facecolor('white') # Set white background = Basemap(height=1.7e6, width=2.8e6,
resolution='f', area_thresh=10., projection='omerc',
lon_0=self.midLongitude, lat_0=self.midLatitude,
lon_1=self.minLongitude, lat_1=self.minLatitude,
lon_2=self.maxLongitude, lat_2=self.maxLatitude)'#46bcec')'#f2f2f2', lake_color='#46bcec'), self.maxLatitude, 10.)), self.maxLongitude, 10.))



def PlotEarthquakeLocationsOnMap(self, bPlotPoints):
''' Just plot the points where the earthquake occurred '''

if bPlotPoints:
# Default size for already displayed points (in a time-lapse fashion, some points
# have already been displayed as shrinking points - at this stage only show shrunk versions)
pstart = ARGS.npoints - ARGS.nsimpoints
pend = ARGS.npoints
x, y =[0:pstart], self.latitude[0:pstart]), y, 'ro', alpha=0.8, markersize=5, markeredgecolor='red',
fillstyle='full', markeredgewidth=0.1)

# Custom (most of the time bigger) font for the new point to be displayed
x, y =[pstart:pend], self.latitude[pstart:pend]), y, 'ro', alpha=0.8, markersize=65-ARGS.markersize, markeredgecolor='red',
fillstyle='full', markeredgewidth=0.1)

day = ([ARGS.npoints].split('T'))[0]
magnitude = self.magnitude[pend]
plt.title('Earthquake on {} - magnitude {}'.format(day, magnitude))


def WriteCityNamesOnTheMap(self):
Write names of selected cities on the map after finding
their corresponding latitude/longitude value

# Lat/lon coordinates of several cities that lie in the map of interest
lats = [41.00, 41.71, 35.12, 35.24, 37.04, 37.26, 39.90,
44.42, 44.78, 41.32, 36.89, 35.46, 31.20, 32.09,
43.60, 33.89, 39.93, 42.13, 31.94, 45.04, 36.20,
43.85, 41.11, 31.76, 29.87, 44.61, 38.50, 38.35,
36.43, 45.65, 42.26, 38.42, 42.83]

lons = [28.97, 44.82, 33.42, 24.80, 22.11, 35.39, 41.26,
26.10, 20.44, 19.81, 30.71, 44.38, 29.91, 20.18,
39.73, 35.50, 32.85, 24.74, 35.92, 41.96, 37.13,
18.41, 16.87, 35.21, 40.10, 33.52, 43.37, 38.33,
28.21, 25.60, 42.71, 27.14, 31.70]

cities = ['Istanbul', 'Tblisi', 'Cyprus', 'Crete', 'Kalamata', 'Adana',
'Erzurum', 'Bucharest', 'Belgrade', 'Tirana', 'Antalya',
'Kerkuk', 'Alexandria', 'Benghazi', 'Sochi', 'Beirut',
'Ankara', 'Plovdiv', 'Amman', 'Stavropol', 'Aleppo',
'Sarajevo', 'Bari', 'Jerusalem', 'Sakaka', 'Sevastopol',
'Van', 'Malatya', 'Rhodes', 'Brasov', 'Kutaisi', 'Izmir',
'B L A C K S E A']

# Compute the native map projection coordinates for cities.
xc, yc =, lats)

# Plot filled circles at the locations of the cities.[:-1], yc[:-1], 'bo')

# Some certain city names need to be shifted for better visualization
for name, xpt, ypt in zip(cities, xc, yc):
if name == 'Alexandria' or name == 'Crete' or name == 'Van' or name == 'Malatya':
plt.text(xpt+10000, ypt-20000, name, fontsize=9)
elif name == 'Jerusalem':
plt.text(xpt-40000, ypt-30000, name, fontsize=9)
elif name == 'Kalamata':
plt.text(xpt-30000, ypt+15000, name, fontsize=9)
elif name == 'Istanbul' or name == 'Benghazi':
plt.text(xpt+20000, ypt-10000, name, fontsize=9)
elif name == 'B L A C K S E A':
plt.text(xpt+15000, ypt+10000, name, fontsize=11)
plt.text(xpt+10000, ypt+10000, name, fontsize=9) # Default visualization


def UseGMPLOTtoDumptoGoogleMap(self, htmlfilename):
''' Convert the same earthquake data info to Google Map heat map format
NOTE: gmplot package needs to be pre-installed

# Some other options using gmplot
gmap.plot(latitudes, longitudes, 'cornflowerblue', edge_width=10)
gmap.plot(latitudes, longitudes, 'red', edge_width=8)
gmap.scatter(more_lats, more_lngs, '#3B0B39', size=40, marker=False)
gmap.scatter(marker_lats, marker_lngs, 'k', marker=True)
gmap.scatter(lat, lon, 'r', size=10, marker=False)

gmap = gmplot.GoogleMapPlotter(self.midLatitude, self.midLongitude, 3) # lat/lon/google map zoom level

gmap.heatmap(self.latitude, self.longitude)



def SaveSnapshotsToFile(self, bSaveFigs=True, snapshotfilename='earthquakes_dpi240'):
Save the earthquake snapshots in time to file
Note that for (16,9) sized figure, dpi=120 gives (16,9)*120 =[1920,1080] pixels png file
Similarly, dpi=240 gives (16,9)*240 =[3840,2160] pixels png file

if bSaveFigs:
OutFolder = 'Snapshots_{}'.format(ARGS.npoints - ARGS.npoints%100)
if not os.path.exists(OutFolder):

outpngfilename = '{}/{}_{}_{}.png'.format(OutFolder, snapshotfilename,
ARGS.npoints, ARGS.markersize)
plt.savefig(outpngfilename, facecolor='w', dpi=240)


def __str__(self):
''' Print some data on the earthquake class'''

str1 = "Number of points = {}".format(len(self.longitude))
str2 = "Latitude (min,max) = {}, {}".format(self.minLatitude, self.maxLatitude)
str3 = "Longitude (min,max) = {}, {}".format(self.minLongitude, self.maxLongitude)
str4 = "Mid points (Longitude, Langitude)= {}, {}".format(self.midLongitude, self.midLatitude)

return '{}\n{}\n{}\n{}\n'.format(str1, str2, str3, str4)

def ParseInput():
''' Parse input arguments '''

parser = argparse.ArgumentParser()

parser.add_argument("-markersize", type=float, default=5, help="Marker size to represent the earthquake data on the map")
parser.add_argument("-npoints", type=int, default=1, help="Total number of points in the graph [sweep the whole range for final video output]")
parser.add_argument("-nsimpoints", type=int, default=1, help="Number of simultaneous points having different marker size")
parser.add_argument("-usgsdata", type=str, help="Filename (e.g. usgs.csv) that contains the earthquake data as downloaded from USGS")

args = parser.parse_args()

return args

def main():

quake = EarthquakeData();


# This is a bonus feature - dumping the earthquake heatmap to google maps format [uses gmplot]

if __name__ == '__main__': # standard boilerplate calling main()
ARGS = ParseInput()

Additional references:


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