Toward Finding Latent Cities with Non-Negative Matrix Factorization

TL;DR

This paper introduces Mobilicities, a method using Non-Negative Matrix Factorization to analyze and cluster city mobility patterns from mobile phone data, revealing interpretable latent city structures.

Contribution

It presents a novel approach to understanding urban mobility by analyzing movement patterns, extending city analysis beyond static activity locations.

Findings

Mobilicities reveal latent mobility structures.

Clustering improves understanding of city dynamics.

Method aids in urban planning and population activity analysis.

Abstract

In the last decade, digital footprints have been used to cluster population activity into functional areas of cities. However, a key aspect has been overlooked: we experience our cities not only by performing activities at specific destinations, but also by moving from one place to another. In this paper, we propose to analyze and cluster the city based on how people move through it. Particularly, we introduce Mobilicities, automatically generated travel patterns inferred from mobile phone network data using NMF, a matrix factorization model. We evaluate our method in a large city and we find that mobilicities reveal latent but at the same time interpretable mobility structures of the city. Our results provide evidence on how clustering and visualization of aggregated phone logs could be used in planning systems to interactively analyze city structure and population activity.