Population Density Maps

For understanding Where people are we use “Population Density Maps”, which are provided by the Facebook’s Data for Good initiative. Specifically, we use Population density maps at the tile level (see Granularity).

Description

A Population density map seperates the country into different areas, and for each it counts the number of users that spend most of their time there, for three eight hour time windows each day.

Example

For example, for the working hours time window on the 20th of April, 86.725 active Facebook users that spent most of their time the Nairobi county. Because data is aggregated like this, there is no information left to identify individuals and privacy is thus preserved (see Privacy and data loss below).

Time

The earliest data made available starts april. For each day, three data files are provided; one for each of the time windows 0–8, 8–16, and 16–24 GMT. In Kenya time windows in local time are 03–11, 11–19 and 19–03. In Nigeria time windows in local time are 01-09, 9-17 and 17-01.

Granularity

Population density maps splits the country into different areas. We gain access to two different levels of granularity. Level (1) describes population counts at the lowest administrative level, which for Kenya and Nigeria are counties. Level (2) describes population counts inside geographical tiles up to 1.5 km by 1.5 km in size. The administrative level maps are aggregates of the tile level maps, but they differ in a systematic way due to privacy preservation (see Privacy and data loss below).

Administrative regions: Kenya, size is based on observed population

Baseline

For each movement count (whether it is for a tile or an administrative region) a corresponding baseline count is provided. The baseline is the average count for the same day of the week and time window over the 45 days prior to data generation. It is important to note, that since data generation starts in april but lockdown started already in March for both Kenya and Nigeria, around two weeks of the 45 baseline days span into the lockdown period. Therefore, by comparing to this baseline reported effects may be as much as 33% smaller than those we might have obtained if comparing to a baseline that did not include lockdown.

Privacy and data loss

Counts are made unavailale in tiles/municipalties at times when there are less than 10 active users. This causes a systematic underreporting of population counts from very low-density rural regions. This is especially a problem in the tile-level maps where it is more likely that an area has few users. Thus, there is a trade-off between granularity and data availablity.