Estimating Activity Using Mobility Data#
Understanding population movement can provide valuable insights for public policy and disaster response efforts, particularly during crises when less movement often correlates with reduced economic activity.
Similar to initiatives such as the COVID-19 Community Mobility Reports, Facebook Population During Crisis, and Mapbox Movement Data, we have developed a range of crisis-relevant indicators. These include baseline and subsequent device densities, as well as metrics like percent change and z-score. These indicators are derived by aggregating device counts within specific geographical tiles and across various time periods, utilizing longitudinal mobility data.
It’s important to note the inherent limitations associated with this approach, as detailed in Limitations. Notably, mobility data is typically collected through convenience sampling methods and lacks the controlled methodology of randomized trials.
Data#
In this section, we import from the data sources, available either publicly or via Datasets.
Area of Interest#
In this step, we import the clipping boundary and the H3 tessellation defined by area(s) of interest below.