What happened?
A magnitude 8.1 earthquake was measured in the Pacific region offshore Chiapas, Mexico at 04:49:21 UTC on September 8th 2017.
Working in the Water Mission Area for the USGS, our Data Science Team was already hard at work polishing up water visualizations for the devastating Hurricane Harvey, as well as creating a new visualization for Hurricane Irma. That is to say, the resources available for a timely visualization were already spent.
By 10 am on Friday however, a team of USGS groundwater scientist had realized that the affects of the earthquake were being observed in groundwater water level measurements across the United States, and were collecting a list of sites that clearly showed the phenomena. It was interesting to view the data directly from the National Water Information System NWIS pages.
While the NWIS site allowed a very easy way to observe the data, it was not quite as dramatic and interesting since it was only plotted in local time zones (so the earthquake peaks were not lining up), and the y-axis scales were so different, they could not be plotted on a single graph:
Affected Groundwater Levels
The challenge was on: could we very quickly get a graph out that aggregates this data, unifies the time zones, and visualize the information in an intuitive way for the general population? This blog documents the process of rapidly creating the plot in order to get the information out to social media in a timely manner. The final plot could certainly be improved, but with the limited resources and time constraints, it still managed to be a fun and interesting graphic.
Aggregate USGS Data
Without question, the hardest part is to find the right data. Luckily for me, this was already done by 10am by the groundwater scientists Rodney A. Sheets, Charles Schalk, and Leonard Orzol (contact information is provided at the end of this post). They provided me with a list of unique site identification numbers from groundwater wells across the nation, and a “parameter code” that represents water levels measurements.
Using the dataRetrieval R package, it then becomes trivial to access USGS data and import it into the R environment.
The following code retrieves the data:
library(dataRetrieval)
sites