EarthQuake

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Earthquake Start of a project to bring in information from a website with an Application Programming Interface (API). We are going to use a modified version of "Project: Fetching Current Weather Data" from "Automate the boring stuff with Python" by Al Sweigart What is going on below? We import libraries to dela with json, reuest from the server and the pandas library. In [1]: import json , requests import pandas as pd from pandas import json_normalize In this section we creating a string made up of the URL. Requesting the information from the site with the URL we created and pass back the information. Data comes from the US Geological survey  https://www.usgs.gov/about/about-us/who-we-are and one of their earthquake feeds. Then print out what was returned. In [2]: url = 'https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_hour.geojson' response = requests . get ( url ) response . raise_for_status () Now we need load the data which i
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Social Analysis using socioviz

Originally posted in https://computingnorthampton.blogspot.co.uk/2016/01/mini-projectsocial-network-analysis-fun.html

figure 1. #StarWars 30/12/15
Playing with Socioviz (socioviz.net) - a free online tool for looking at influence on twitter. The image to left show connections between tweeters using the hashtag #StarWars on the 30th December 2015 up to 6pm (GMT).

Figure 2 shows the most active tweeters for this hashtag and the most influential based on Retweets and Mentions - the four greatest influencers are picked out in the video below, showing the map evolving (speed-up 20 times).

 

figure 2
To experiment with this a bit more +The Royal Institution  has a long traditional of holding a series of Christmas Lectures  which are now televised, Dr Kevin Fong presented this years. I was curious about who the biggest influencers on twitter were for the hashtag #xmaslectures over the three days of the show. The three biggest influencers came out as  , the presenter, the host organisation and one of the main guests (had to present virtually as he is on the ISS at the time of writing). The figure below shows the centre of the graph were the majority of the connections.

figure 3 #xmaslectures


To read more about this go to the tutorial by Alessandro Zonin https://alessandrozonin.wordpress.com/2015/02/20/socioviz-a-free-social-network-analysis-tool-for-twitter/.

Another example of used it for is plotting a particular tweetchat (based on hashtag) and seeing if there were groupings within the data. There was for this particular chat.
figure 4 plotting a tweetchat


Looking at certain an institution's considerable twitter links (@UniNorthants) below over a one month period (8/12/2015-8/1/2016). Different types of groups and links between groups example shown in figures 6 and 7.
figure 5 organisations twitter connections over a month






figure 6 same organisation just focussing on one group
figure 7 links between groups

Updated 

All views are the authors and do not reflect the views of any organisations the author is associated with. Twitter: @scottturneruon

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