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Showing posts with the label social analysis

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

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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 thre...
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Publications in a social network

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The Computing staff's network of co-authors, at the University of Northampton, based on the University's  research repository NECTAR -  http://nectar.northampton.ac.uk/view/divisions/SSTCT.html  on 12th November 2016.  The data goes back to 2010. The data was analysed using the software VOSviewer -  http://www.vosviewer.com/  free software for visualising networks . Differences in colours represents, the clusters of publications with those authors picked out by the software. The relative size of the circles is the relative number of publications listed; so for the two biggest circles/hubs it relates to 55 and 34 publications in this time period. Some relatively new authors, to the University but not to research, explains some of the 'islands' and the number of publications within it - it only reflects publications whilst at the University of Northampton. To dig a little deeper, going to  look...
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