Obvisously, the data needed some massaging, I decided to break the dataset up into one pandas data frame for countries, and one for the cities. We are looking for “situational sensors” of what is going on around the world, so this should help us understand the level of granularity we may want and the insights we could get.
Mobility by Country
The x axes show the countries in the dataset, the y axis is the time. The colour code illustrates the percentage mobility serach requests have dropped. I am using blue-red to be colorblind-safe.
There are a few countries that stand out, in particular, Hong Kong and Macao, where mobility dropped very early compared to the other nations. South Korea shows a steady decline in mobility search queries, so does Singapore. I cannot, at this point, iunderstand the Saudi Arabia trend, but that seems to be a weekly pattern.
It is fairly obvious that all countries had severe declines in mobility around April 10…April 17. This actually surprised me as I am in week 5 (I think) of home office and have not been using Apple or Google maps to plan a trip for weeks, it seems.
Mobility by City
We use the University of Oxford CORONAVIRUS GOVERNMENT RESPONSE TRACKER a lot, this is a wonderfully curated dataset. Mobility data, at high aggregation rates, can positively be used as sensors to understand when the public actually adopted lockdown measures, and to what extent.
Disclaimer: This information can be used for educational and research use. The author is not a health care professional and it is not recommended to use the views in this document for any healthcare related decision making.
Written by: Dr Klaus Paul, Berlin AI Hub and Emerging Technologies Capabilities Lead, R² Data Labs