MPC Member Publications

This database contains a listing of population studies publications written by MPC Members. Anyone can add a publication by an MPC student, faculty, or staff member to this database; new citations will be reviewed and approved by MPC administrators.

Full Citation

Title: Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic

Citation Type: Journal Article

Publication Year: 2023

ISBN: 1111111111

ISSN: 1932-6203

DOI: 10.1371/JOURNAL.PONE.0277913

PMID: 36662785

Abstract: Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.

Url: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0277913

User Submitted?: No

Authors: Chen, Tongxin; Zhu, Di; Cheng, Tao; Gao, Xiaowei; Chen, Huanfa

Periodical (Full): PLOS ONE

Issue: 1

Volume: 18

Pages: e0277913

Countries:

IPUMS NHGIS NAPP IHIS ATUS Terrapop