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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Bit of fun - SNA of RI Xmas Lectures part 2

The previous post http://datavizexperiments.blogspot.co.uk/2016/12/bit-of-fun-sna-of-ri-xmas-lectures-part.html look at a set of tools for playing with the twitter data and the results for #xmaslectures a hashtag associated with the Royal Institution's Christmas Lectures, but for the pre-lecture time and after for the first one. 

 This post looks at the last lecture.

Before the last lecture
The last two and half days 26/12/2016 to 2pm 28/12/2016

 Using Sociov.net and GEPHI to visualise the tweets. Figure 1 shows peaks when the lectures are broadcast and the sustained rise (as compared to the levels pre-lectures being broadcast levels) in values between the lectures
figure 1

Figure 2
Figure two shows the dominance of the two tweeters @ri_science and @siafulchemstry (the host and the presenter respectively). There is another level of nodes/tweeters who are often (but not all) guest presenters within the lectures.
After all three lectures (26/12/2016 -28/12/2016)
figure 3
figure 4
Figures 3 and 4 were produced with the socioviz and GEPHI approach showing a very active and well connected (in my terms) network.
figure 5
Figure 5 shows central hubs but quite a bit of discussion between groups as well.
Now for the 'Death Star' - I think it looks like a Death Star - diagram using the TAGS tool.


This tool is great in that it is interactive; by following the link http://hawksey.info/tagsexplorer/?key=16Bwd4nQDGVHETdgc97RUM6yScHRmGRSic0jI8Fru5WQ&gid=400689247 you can 'dig in' further; such as the contribution of each node to the discussion.







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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