Revealing spatio-temporal interaction patterns behind complex cities

TL;DR

This study uncovers universal patterns in urban spatio-temporal interactions using cellphone data, revealing city states, community dynamics, and proposing a model to predict human mobility.

Contribution

It introduces a comprehensive analysis of city interaction patterns, identifies universal switching behaviors, and proposes a new dynamic mobility model based on temporal and population factors.

Findings

Cities exhibit stable rank-size distributions despite constant movement.

Cities switch between 'active' and 'sleeping' states with distinct community structures.

Larger cities have greater heterogeneity and sleep less as population increases.

Abstract

Cities are typical dynamic complex systems that connect people and facilitate interactions. Revealing universal collective patterns behind spatio-temporal interactions between residents is crucial for various urban studies, of which we are still lacking a comprehensive understanding. Massive cellphone data enable us to construct interaction networks based on spatio-temporal co-occurrence of individuals. The rank-size distributions of hourly dynamic population of locations are stable, although people are almost constantly moving in cities and hotspots that attract people are changing over time in a day. A larger city is of a stronger heterogeneity as indicated by a larger scaling exponent. After aggregating spatio-temporal interaction networks over consecutive time windows, we reveal a switching behavior of cities between two states. During the "active" state, the whole city is concentrated in fewer larger communities; while in the "sleeping" state, people are scattered in more smaller communities. Above discoveries are universal over diversified cities across continents. In addition, a city sleeps less, when its population grows larger. And spatio-temporal interaction segregation can be well approximated by residential segregation in smaller cities, but not in larger ones. We propose a temporal-population-weighted-opportunity model by integrating time-dependent departure probability to make dynamic predictions on human mobility, which can reasonably well explain observed patterns of spatio-temporal interactions in cities.