University of Córdoba Develops MAPLID Software Using Mobile Data to Revolutionize Real Time Urban Planning

Discover how MAPLID software analyzes mobile signals to map city routines and improve public transit. A new era of data driven urban design is here.

By: AXL Media

Published: Apr 29, 2026, 6:23 AM EDT

Source: Information for this report was sourced from EurekAlert!

Mapping Urban Rhythms Through Mobile Connectivity

A research team at the University of Córdoba has introduced a sophisticated software tool designed to transform raw telecommunications data into actionable urban insights. The system, known as MAPLID, utilizes a multi label approach to classify city spaces, acknowledging that a single location often serves multiple purposes depending on the time of day. According to the study, a university campus might be categorized as a workplace during morning hours, a leisure hub in the afternoon, and a residential zone at night. This dynamic classification allows for a more realistic understanding of city life compared to traditional, static mapping methods.

The Mechanics of Signal Tracking and Privacy

The tool operates by analyzing geolocated data generated whenever a mobile device connects to a network antenna to initiate a call or transmit a message. By recording these interactions and tracking their recurrence over a weekly period, MAPLID can decipher the routines of a population and identify when specific areas experience spikes in density. Lead researcher Manuel Mendoza Hurtado noted that while the algorithm is highly effective, the team relied on an open database from Telecom Italia due to the strict privacy regulations that currently limit access to large scale mobile data in Spain.

Validation Through Italian Pilot Programs

To verify the reliability of the algorithm, the researchers conducted pilot tests in the Italian cities of Milan and Trento. These locations were selected for their contrasting urban layouts, providing a rigorous environment to test the software's adaptability. By overlaying millions of mobile data points with contextual geographic information from OpenStreetMap, the team successfully mapped the shifting pace of these cities. The results demonstrated that the tool could accurately capture complex urban functionalities that simpler models often overlook by oversimplifying human movement patterns.