Google Mobility Report

This datasets show how visits and length of stay at different locations change compared to a baseline. These changes are calculated using the same kind of aggregated and anonymised data used to show popular times for places in Google Maps.

Changes for each day are compared to a baseline value for that day of the week. The baseline is the median value, for the corresponding day of the week, during the 5-week period Jan 3–Feb 6, 2020. Definitions for the place categories can be found below.

Whilst this data cannot be thought of as being representative of the population as whole, as the data only covers those with woriking smartphones and data connection and the workplace data likely only covers those who are regularly employed in the same location, it does offer valuable insight into the behaviour of this segment of the population and their movement and economic activities. 

National level analysis shows that all activities, other than staying at home decreased sharply from the week starting March 9th. Towards the end of April activities such as going to work, shopping and outdoor leisure started increasing again. This return to pre-covid behaviour has been gradually increasing for all categories apart from attendance at workplaces which started to decline at the end of June. 

Whilst the national level picture looks much as you'd expect, the County level analysis shows a very different story. The second of the charts below shows that people in a significant number of counties are attending workplaces, using transit stations and visiting outdoor leisure spaces above at rates above the pre-covid baseline.

In the week starting 6th of July the data indicates that people were attending workplaces at higher rate than the pre-covid baseline in the following counties: 

Narok Taita Taveta Trans Nzoia Bomet Siaya Migori Turkana Nyandurua Homa Bay Busia Garissa

Given that smartphone penetration is very low in some of these counties this cannot be through of as representative of the whole population but does offer insights into patterns of travel and activities undertaken by the smartphone owning population. 

Place categories

Grocery & pharmacy

Mobility trends for places like grocery markets, food warehouses, farmers markets, specialty food shops, drug stores, and pharmacies.

Parks

Mobility trends for places like local parks, national parks, public beaches, marinas, dog parks, plazas, and public gardens.

Transit stations

Mobility trends for places like public transport hubs such as subway, bus, and train stations.

Retail & recreation

Mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.

Residential

Mobility trends for places of residence.

Workplaces

Mobility trends for places of work.