Dataset Information

Partial Correlation between Spatial and Temporal Regularities of Human Mobility.

ABSTRACT: The regularity of human mobility has been extensively studied because of its prominent applications in a considerable number of important areas. Entropy, in addition to many other measures, has long been used to quantify the regularity of human mobility. We adopt the commonly used spatial entropy and develop an analogical temporal entropy to separately investigate the spatial and temporal regularities of human mobility. The underlying data are from an automated transit fare collection system operated by a metropolitan public transit authority in China. The distributions of both spatial and temporal entropies and their dependences on several widely used statistics are examined. The spatial and temporal entropies present a statistically significant correlation, which has not previously been reported to the best of our knowledge.

SUBMITTER: Geng W

PROVIDER: S-EPMC5524686 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Partial Correlation between Spatial and Temporal Regularities of Human Mobility.

Geng Wei W Yang Guang G

Scientific reports 20170724 1

The regularity of human mobility has been extensively studied because of its prominent applications in a considerable number of important areas. Entropy, in addition to many other measures, has long been used to quantify the regularity of human mobility. We adopt the commonly used spatial entropy and develop an analogical temporal entropy to separately investigate the spatial and temporal regularities of human mobility. The underlying data are from an automated transit fare collection system ope ...[more]

PMID: 28740176

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Project description:BackgroundPopulation mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases.ObjectiveThe aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina.MethodsThis longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting.ResultsPopulation mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%.ConclusionsUsing Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

Dataset Information

Partial Correlation between Spatial and Temporal Regularities of Human Mobility.

Publications

Partial Correlation between Spatial and Temporal Regularities of Human Mobility.

Similar Datasets

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