Kenya Mobility Sitrep
Mutono Nyamai
University of Nairobi, Institute of Tropical and Infectious Diseases, Washington State University Global Health Programmutononyamai@students.uonbi.ac.ke; mutono.nyamai@wsu.edu
Thumbi Mwangi
University of Nairobi Institute of Tropical and Infectious Diseases, Washington State Universitythumbi.mwangi@uonbi.ac.ke; thumbi.mwangi@wsu.edu
## [1] "Updated on 2021-04-12 12:21:58"Data
The figures in this report are generated using movement data generated by Facebook. These data represent people who use Facebook in Kenya and have location services enabled.
Data are aggregated at a 600m x 600m sized tiles and vectors (lines) are drawn connecting all areas to each other. These lines provide data in both directions (going from area A to B and from area B to A). The Facebook data provides:
- The starting point of each line
- The ending point of each line
- The number of people who traveled along this line in both directions for the 45 days preceeding the collection of the data (noted on the bottom of every set of figures)
- The number of people who traveled along this line in both directions for a given time period.
- The length of the line in euclidean distance (as the flamingos would fly between Lake Natron and Lake Nakuru, not through the existing travel network).
- Data are aggregated in 8 hour blocks, one of these blocks for Kenya is from 2100 to 0500. We treat this as belonging to the date that 2100 is in.
Figures - Country Level
Percent Change in Travel Map - The percent change in travel within and between counties in Kenya. Some counties may show up in one map and not the other - as people stop moving, and especially if they don’t leave their homes, they no longer contribute to FB movement data and hence their information is not captured.
Percent change in total number of trips compared to baseline - The total number of trips originating and ending in the region for which we have data minus the average number of trips originating from the same area for the 45 days preceding the start of data collection.
Total distance traveled - The total distance of trips originating and ending in the region for which we have data.
Figures - County Level
Local Travel Network - A map of the region for which we have data showing the top locations of travel to and from that county.
Percent change in total number of trips compared to baseline - The total number of trips originating and ending in the region for which we have data minus the average number of trips originating from the same area for the 45 days preceding the start of data collection.
Total distance traveled - The total distance of trips originating and ending in the region for which we have data.
Pointers on evaluating the data
- It’s best to look at percent change in trips and total distance traveled as two views of a “mobility” metric.
- For example, if the number of trips goes up but the total distance traveled goes down, it likely means that people are moving a bit more but mainly going shorter distances, perhaps even just exercising or walking around the neighborhood.
- When looking at the travel network remember that people will live at boundaries of the area of interest, therefore, it may just be short distance movements that are resulting in people traveling from one location to another. -Long distance travel connections are more difficult to rationalize and warrant further investigation.
- You’ll often see an uptick in movement and total distance traveled on the weekends. This is generally normal behavior, though deviation from this during lock down measures should be evaluated.
- Keep an eye on the Y axis, it may be log scaled to better show the data. The labels are correct but rates of change are more extreme than they appear.
Key takeaways
- There is a general upward trend in movement evident from data from Nairobi - in spite of the travel restrictions to and from the county.
- The movement in and out of Mombasa county has remained low.
- For most counties, movement within counties has generally not changed pre and during the COVID-19 crisis.
Kenya Summary
County Specific Analysis
Baringo County
Bomet County
Bungoma County
Busia County
Elgeyo Marakwet County
Embu County
Garissa County
Homa Bay County
Kajiado County
Kakamega County
Kericho County
Kiambu County
Kilifi County
Kirinyaga County
Kisii County
Kisumu County
Kitui County
Kwale County
