Welcome to the Location Data Blog!

The location data industry is characterised by opaque supply chains, making it challenging for buyers to procure high-quality datasets. Quadrant’s mission to instill transparency into the industry starts with how and where we source the data. We have been successfully working on acquiring data right where it is created, incentivizing developers to ethically monetize their mobile apps.

One way of doing this is SDK integration, which we have covered in detail. The other, increasingly popular method is Server-to-Server (S2S) integration; in this piece, we give readers a peek behind the curtain so that they can understand how S2S works.  

Having access to large repositories of data enables businesses to optimise operations in several ways. This includes personalised advertising, greater supply chain efficiency, and more satisfying customer experiences.

However, people have grown increasingly wary of trusting businesses and governments with their data. Several high-profile data privacy breaches at LinkedIn, Alibaba, and Yahoo (to name a few) collectively impacted billions of users.

Ethically managing data and making sure no Personally Identifiable Information (PII) makes it to big data sets is a challenge we take very seriously at Quadrant.

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.

The intricate web of connected devices and applications produces a lot of location data. Used in the right manner, accurate and timely geospatial intelligence can be leveraged to boost operational efficiency,  improve government and public services, increase marketing and advertising ROI, and expand services to underserved areas.

Geospatial data have the potential to uncover valuable insights about the physical world. It can be used by governments to save lives and influence public welfare. Businesses can use it to drive consumer acquisition by capturing the attention of the right people at the right time. Anonymized location data has a lot of value to offer without breaching people’s privacy.