David Crompton

Every May 17, a wave of people moves through Oslo. Families gather along the parade route in the morning, the children's parade walks through the city centre through lunchtime, and the crowd drifts down to the waterfront for the rest of the afternoon. Everyone in Oslo knows this rhythm. We wanted to see if we could watch it happen, live, from the data.

Parade peak. The focus areas along the route light up; the surrounding city stays close to its normal Sunday. The busiest area in this moment runs around seven times its usual presence at the same hour.

Using an activity-based transport model to measure the impact of one supermarket in a Norwegian residential area with no food shops.

Mobility analytics often start with a simple idea: stronger signal means closer user. The real world is not that simple.

Radio waves bend, scatter, reflect, and get absorbed by the materials they encounter. Those physical effects can shift a device's apparent location by tens or hundreds of meters, even when the data pipeline is mathematically correct.

This post is the first in a short series on radio propagation and positioning accuracy. The goal is not to explain every equation, but to show why physics matters and how we validate positioning logic when ground truth is scarce.

When millions of people move through a city each day, mathematical patterns emerge that reveal how societies organize themselves.

Human mobility science studies these aggregate patterns - not where individuals go, but how populations flow and cluster across space and time. It's the foundation for understanding disease spread, urban planning, and emergency response.

Human mobility patterns hold the key to building better cities, managing crises, and understanding how societies function. Yet the field of mobility science - decades of brilliant academic research - remains locked away from those who could use it to improve lives.

We founded Telcofy to bridge that gap.