Their study aims to know how an intervention (at time t) affects the COVID-19 spread one week later (t + 1). To do this, it requires county-level data and detailed mobility pattern data. Even with this information, it is extremely hard, as transmissions sometimes happen weeks before they are measured, different datasets handle dates differently, and data availability varies wildly over time.
To address these challenges, they assess COVID-19 data for accuracy, and offer their own contribution to the solution: CleanCovid19Counts (CC19C). This is a new archive of public COVID-19 time series that can be used to improve data accuracy.
How Placekey enabled research on COVID-19 data accuracy
To understand the spread of infection and data accuracy, Douglass et al. explore a number of working papers, reported statistics, point of interest (POI) location information from Placekey, and foot traffic data from SafeGraph. This paints a clear picture of the points of interest being analyzed and the traffic to these locations, allowing for deeper analysis of interaction and infection spread.
Even more than that, it lets us understand and identify errors and flaws in the data, enabling better, more accurate insights to be drawn from the data.
To learn about how you can benefit from SafeGraph and Placekey, join the Placekey community and talk with other users about what they’ve done (and what you can do)!
Placekey is the universal standard identifier for a physical place. Learn more about us at Placekey.io.
Get ready to unlock new insights on physical places