With COVID-19 dictating significant changes to everyday life, there is no time like the present for Skyhook to release the Mobility Index, which helps to visualize and model population behaviors. The Mobility Index is an aggregated look at anonymous mobile device location information, used to provide generalized, de-identified and normalized location intelligence to characterize the overall device movement.
This offering was originally developed to help characterize human population behaviors to support COVID-19 suppression planning and response, and can be observed at global, national, state, and even county levels. With hourly time increments, the Mobility Index can offer clear, up to date, information on how the population’s movement has changed — and is continuing to change — in the wake of the COVID-19 pandemic.
How the Mobility Index Works
The Skyhook Mobility Index can establish the scale and intensity of collective device movement, or itinerancy, across geographic scales ranging from global to local. Skyhook gathers this itinerancy data for all devices in our pool to determine how much aggregate motion is occurring across these target geographies and how this changes over time. The Mobility Index divides the world into millions of tiles which are individual areas approximately 100 meters by 100 meters. As the number of tiles visited by a device increases, the mobility index goes up as well. As these counts are collected for all devices operating within a geographic area (state, county, town), and only reported as numbers within that area, location privacy of all devices is carefully preserved.
What the Mobility Index Can Show
As devices visit more tiles, the degree of mobility increases. Because tiles are bound to specific counties, states, and countries, it’s possible to observe and analyze movement patterns at different municipal levels. By understanding device movement patterns and how they have changed and are changing over time, location data can provide a powerful way to make informed business decisions in response to changes in public health policies and stay-at-home orders. The data can also help to show how closely these orders are being followed, and how that lines up with other data, like infection rates.
Here is an example of how the movement data can be visualized. The darker red areas indicate higher levels of population mobility.
