Within our Analytics team we are creating demo applications on a regular basis and a difficulty that we generally encounter is showing data that is actually relevant and relatable to the audience. So I thought rather than do a blog around utilities, manufacturing et al, our first showcase blog is going to centre around global earthquakes. The reasons for this are threefold:
- Highlights the power of integrating multiple datasets
- The datasets work well with the visualisations below
- Actually quite interesting to see how many earthquakes (over a magnitude of 5.5) there were between 1965 and 2016 (A LOT - 23,412)
So without further ado the rest of the blog will focus on earthquakes. The embedded objects are all interactive so please click and find a story that you never thought of before ....
Generally the trend of earthquakes are increasing year on year, this is largely because of technology improvements and tools that may not have been around in 1960's. To drill down to a month simply click a year below.
So over time the amount of earthquakes are increasing, but where are they happening at? The below map highlights this, you can select a year / month / date from the above chart to drill down to view the map at a more granular level. A recent example of a major set of earthquakes was in Japan in March 2011 (in the above bar chart, have a click on 2011, then March 2011 and see the significance of this period on the map below).
Bohal Earthquake 2013
So it is clear that there are a lot of earthquakes that we generally don't hear about, likely because they don't result in any significant damage, but what about the destructive earthquakes? I'll now focus on a particular earthquake that took place in Bohal (Philippines) on 15th October 2013. The below info graphic highlights the loss of life that occurred because of the earthquake and it cost in excess of $50,000,000 of damage.
Another area to focus on is that impact of the Bohal earthquake on social media usage, was there a spike after the earthquake, where people using it to stay in contact or provide updates? Based on the Twitter information below it seems that there is an increase in activity on the date of the earthquake and the following dates. If you looked at this dataset in isolation the first question would be, "Why was there a spike on 15th October 2013?", but with the addition of the earthquake dataset it is obvious that this an effect on the natural disaster.
Make your own story
The aim of this post was to help you understand that it's not always about looking at one dataset and reaching a conclusion, by integrating and analysing additional areas (which may not be always relevant) it helps shape your knowledge base and provide alternative viewpoints that you may have never considered.
We're aiming to do a regular showcase (including revisiting my earlier Premier League article) so keep an eye out for them, any suggestions please tweet me @ralph_g85 .