Welcome to the Location Data Blog!

Location data is collected from multiple sources of varying quality GPS signals from mobile devices, beacons, and WIFI connections, the notorious Bidstream, and more. In most cases, even genuine location data cannot represent the entire population of the region. This discrepancy can be attributed to smartphone penetration in the country, app-specific demographic variations, hardware inconsistencies, and sources of location data.

To perform meaningful analysis that accounts for mobility patterns and other trends in a larger region, data scientists use projection models to make an accurate estimation of a region’s population and normalise data counts to fit the use case. This is called data extrapolation.

In the spirit of transparency, we are going to share some background info on how we at Quadrant analyse the quality of location data we provide to our buyers – some of the steps we take to ensure it is of the highest quality possible for their particular use case.

For data professionals, choosing a location data feed that is fit for purpose for your project can be challenging. The problem is that it’s not always clear what the quality of a given data feed is, but with Quadrant’s Data Quality Dashboard we’re changing that.