I am currently working for a non profit organisation, working on enhancing the interaction between the energy industry and the weather, climate and broader environmental sciences community.
Not coming from a climate science background, I had to become quickly accustomed to the terminology and technologies associated with this field of research. A commonly used file types produced to represent weather and climate data, are Network Common Data Files (aka NetCDF files).
As I’ve been using Google Analytics daily for the past year, I thought it was high time I got properly certified. I completed the beginners course on Analytics Academy today and picked up some useful tips I hadn’t been utilising. Will be doing the intermediate and advanced courses in the coming weeks.
Sixteen47 Ltd (Dawn French and Helen Teague) hired me to develop an online marketing strategy and social media campaign for their exclusive plus size clothing brand. This included creating a blog website to host archive material and discussion topics relevant to the companies interests. I worked closely with Sixteen47 to ensure the design was suitably customised to match their distinctive brand aesthetics.
Recently I have been looking into new and interesting ways of visualising data. I stumbled across an excellent suite of open source tools called nodebox (http://nodebox.net) so decided to have a play.
I have experimenting with nodebox v3 for a couple of days and found it really quick and intuitive to make some nice generative animations. You can watch them at: https://www.instagram.com/mrlukesanger/
Wha initially interested me in nodebox is the fact it can accept CSV files. So the next step is to load up some datasets and seeing what kind of data visualisations are possible.
A short case study analysing twitter sentiment regarding the Chilcot Inquiry, a lengthy paper investigating the legalities of the UK’s involvement in the Iraq war. I collected a snapshot of 1000 tweets each day for a week after the paper was released (7/7/16 – 13/7/16) and plotted the results using R with ggplot2 package.
It is clear from the visualisation that tweets containing words associated with ‘negative’ and ‘fear’ are scoring consistently highest, with a large spike 48 hours after the report was released. This would correlate with the time taken for full media coverage on the topic.
Also worth noting is the constant low appearances of tweets containing words associated with ‘surprise’ and ‘joy’, which suggests the results of the inquiry were as predicted and brought little comfort.
The sentiment analysis was performed using my own sentiment analysis tool. You can try it out here for free: http://bit.ly/tweetanalyser