The number of people residing in cities continues to grow at an unprecedented rate. This rapid urbanisation is causing pollution exposure levels to exceed recommended limits. How can we address this challenge? The first step is to truly understand its impact. Dynamic data sources, such as mobile network data, enable us to accurately measure how this pollution affects the population, empowering us to design effective public policies for healthier, more sustainable cities.

A look over the least used stations in Spanish commuter railways [Part 1]

Inspired by the brilliant videos from Geoff Marshall, we kick off a series of posts to look at the least used stations of the Spanish Renfe commuter railway systems, Cercanías Renfe. In this first article, we analyse the least used Madrid Renfe Cercanías stop: San Yago.

Mobile phone data has become a fundamental tool to extract the mobility patterns of the population. However, this data often lack reliable information about some of the main sociodemographic characteristics of the users. In this article we will explain how we deal with these limitations at Nommon by using machine learning techniques.