Global urban activity changes from COVID-19 physical distancing restrictions

TL;DR

This study uses satellite nighttime lights and machine learning to map and analyze daily human activity changes in urban areas worldwide during COVID-19 restrictions, providing a comprehensive global dataset.

Contribution

It introduces TRACE-NTL, the first dataset to resolve daily COVID-19 related activity changes across all metropolitan regions globally using satellite data.

Findings

Global urban activity decreased during COVID-19 restrictions

The dataset enables detailed analysis of human activity disruptions

Supports environmental and social impact assessments

Abstract

During the COVID-19 pandemic changes in human activity became widespread through official policies and organically in response to the virus's transmission, which in turn, impacted the environment and the economy. The pandemic has been described as a natural experiment that tested how social and economic disruptions impacted different components of the global Earth System. To move this beyond hypotheses, locally-resolved, globally-available measures of how, where, and when human activity changed are critically needed. Here we use satellite-derived nighttime lights to quantify and map daily changes in human activity that are atypical for each urban area globally for two years after the onset of the pandemic using machine learning anomaly detectors. Metrics characterizing changes in lights from pre-COVID baseline in human settlements and quality assurance measures are reported. This dataset, TRacking Anomalous COVID-19 induced changEs in NTL (TRACE-NTL), is the first to resolve COVID-19 disruptions for all metropolitan regions globally, daily. It is suitable to support a variety of post-pandemic studies that assess how changes in human activity impact environmental systems.