Byte Curious

Beyond the Siren: What is Geospatial Risk Scoring? Imagine walking outside and relying on an app to tell you whether it might rain. Now, imagine that same kind of predictive tool, but instead of predicting weather, it forecasts crime rates in various neighborhoods. This is the crux of predictive policing , where historical crime data feeds into algorithms to produce what are known as geospatial risk scores or ‘hotspot’ maps. The analogy holds because, much like weather forecasts rely on patterns in meteorological data, predictive policing relies on patterns in crime-related data. However, these patterns can often be fraught with historical biases , which is where the issues begin to brew. While initially aimed at aiding law enforcement, these data are now increasingly used by various other sectors such as insurance companies, risk assessment firms, and financial institutions to assess and manage risks. Not surprisingly, the adoption of geospatial data extends far beyond its origin...

From Paper Tags to Pixels: The Invisible Price Change Remember the days of the bustling grocery store on a Saturday morning, flyers in hand, and the predictable joy of finding your favorite items on sale? Those paper tags and weekly specials are becoming relics. Now, pricing isn’t just about cost and margin; it’s also a question of when and where. Enter algorithmic pricing , a quiet digital revolution happening right under our noses—on the very shelves of our local supermarkets. At its core, dynamic pricing in retail resembles how airlines price their seats. Based on a variety of factors, the cost of, say, a gallon of milk might change multiple times in a week—much like how a seat’s price fluctuates until the airplane takes off. But what makes this possible? The advent of technologies like Electronic Shelf Labels (ESLs) , artificial intelligence, and expansive data collection enables this new age of variable pricing. The complex algorithms behind ESLs can instantly update prices b...