Urban Dynamics Through the Lens of Human Mobility

TL;DR

This paper introduces a real-time approach to analyze urban spatial dynamics using human mobility data, revealing shifts in city structures during COVID-19 and providing metrics for understanding mobility patterns and disease transmission.

Contribution

It presents the mobility centrality index $ riangle KS$ and the $KS_{HBT}$ metric to quantify urban dynamics and home-based travel from individual mobility data.

Findings

Cities became more monocentric during lockdowns

Urban structures remained stable during second COVID-19 wave

Metrics can detect changes in urban spatial structure

Abstract

The urban spatial structure represents the distribution of public and private spaces in cities and how people move within them. While it usually evolves slowly, it can change fast during large-scale emergency events, as well as due to urban renewal in rapidly developing countries. This work presents an approach to delineate such urban dynamics in quasi-real-time through a human mobility metric, the mobility centrality index $\Delta KS$. As a case study, we tracked the urban dynamics of eleven Spanish cities during the COVID-19 pandemic. Results revealed that their structures became more monocentric during the lockdown in the first wave, but kept their regular spatial structures during the second wave. To provide a more comprehensive understanding of mobility from home, we also introduce a dimensionless metric, $KS_{HBT}$, which measures the extent of home-based travel and provides statistical insights into the transmission of COVID-19. By utilizing individual mobility data, our metrics enable the detection of changes in the urban spatial structure.