Earlier this year, we launched the public beta test of our proprietary POI collection and verification app, Geolancer. Aimed at providing up-to-date and manually-verified POI datasets, Geolancer is already helping ride sharing and real estate companies enrich their platforms with a hyper-accurate POI data feed. In this article, we provide a peek behind the curtain and explain how we maintain the high quality of POI data in Geolancer.
The Covid-19 pandemic and following global lockdowns were devastating for the retail industry. Activities like shopping and leisure quickly fell as people’s focus shifted to dealing with a pandemic. Essential retail like grocery, drugstores, and homeware had to adapt quickly to accommodate the needs of the hour, while non-essential retail segments of fashion, sports, beauty, luxury, etc. went into complete disarray.
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.
WEF must strike a balance between privacy, reality and the greater good
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