Seismic Events for Q1 2017

Plot it here, there and everywhere...

My friend showed me the Phivolcs' data set on seismic activities and asked if I can interpret the data. I reviewed it and figured it would be great to add to my portfolio. At the same time, my Coursera assignment's due was almost up. We were tasked to pull data from the web and provide interpretation and data visualization. Sort of putting everything we learned all together...being an independent data scientist. I decided to use the Phivolcs data for the assignment. Hitting 2 birds with one stone...heh!

I started to work on the 2014 data since it showed all the data in 1 page. I used BeautifulSoup to scrape the data and I almost went insane after a week of working on the HTML file. I did not get the format that I needed.

So... To make things easier, I just used the 2017 data since the HTML tables were well formatted. I extracted a 3-month data sample of seismic activities from Phivolcs and converted the data to dataframes using Pandas. I used Matplotlib to plot the earthquake magnitudes and leveraged the Basemap toolkit to draw the map and plot the magnitudes in their respective longitude and latitude coordinates. Ha!


The complete python code may be found here. If you interested with the source file, I have extracted the raw logs as and saved as .csv here.

Here is a breakdown of my source code

Import the essential libraries needed. I am running Python 3.5 over Windows 8.1. As far as I can remember, I updated matplotlib to get the basemap library. I ran the command pip install matplotlib update under the administrator account to initiate the task. You may refer here for the documentation on basemap.
from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import pandas as pd from urllib.request import urlopen

The Q1 data was extracted from Phivolcs' site. The monthly data were encoded on separate pages and were extracted separately. The read_HTML function would have an output of list of dataframes. After inspection, the needed dataframes were on the 3rd index and were extracted respectively. The 3 dataframes were then concatenated and named as df. The dataframe columns were renamed for proper labeling. Saving the file is only optional. I used to_csv to export the file.
#Q1 2017 data only jan_url = "http://www.phivolcs.dost.gov.ph/html/update_SOEPD/EQLatest-Monthly/2017_January.htm" jan_html = urlopen(jan_url).read() jan_table = pd.read_html(jan_html) jan_df=jan_table[3].dropna(how='any') feb_url ="http://www.phivolcs.dost.gov.ph/html/update_SOEPD/EQLatest-Monthly/2017_February.htm" feb_html = urlopen(feb_url).read() feb_table = pd.read_html(feb_html) feb_df=feb_table[3].dropna(how='any') mar_url = "http://www.phivolcs.dost.gov.ph/html/update_SOEPD/EQLatest-Monthly/2017_March.html" mar_html = urlopen(mar_url).read() mar_table = pd.read_html(mar_html) mar_df=mar_table[3].dropna(how='any') df = pd.concat([jan_df,feb_df,mar_df]) df.rename(columns={0:'Date - Time',1:'Latitude',2:'Longitude',3:'Depth (km)',4:'Magnitude',5:'Location' }, inplace=True) #save file df.to_csv('earthquake.csv', sep=',')

Finally! Time to draw! Basemap is pretty easy to follow. (Not!) I used the Transverse Mercator Projection to have a realistic map.I also used the colorbar to aid in the data representation.
#draw map using basemap fig = plt.figure(figsize=(10,10)) plt.title('Philippine Earthquakes \n Q1 of 2017 ') map = Basemap(llcrnrlon=115,llcrnrlat=0,urcrnrlon=130,urcrnrlat=25,projection='tmerc', lat_0 = 12.8797, lon_0 = 121.7740) map.shadedrelief() #plot quake points x, y = map(df.Longitude.tolist(),df.Latitude.tolist()) map.scatter(x, y, c=df.Magnitude,marker='o') map.colorbar().set_label('Magnitude') plt.show()


This tutorial helped me a lot in the visualization using Basemap. I hope it can help you too.

And....the output!