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ZENODO
Conference object . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Other literature type . 2023
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Conference object . 2023
License: CC BY
Data sources: Datacite
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Dynamic changes in the spatial-temporal patterns of urban green space topics on Twitter during the COVID-19 pandemic

descriptionPublicationkeyboard_double_arrow_right Conference object , Other literature type 19 Apr 2023Publisher:Zenodo
Authors: Cui, Nan; Houlden, Vikki; Malleson, Nick; Comber, Alexis;

doi: 10.5281/zenodo.7839566 , 10.5281/zenodo.7839565

Dynamic changes in the spatial-temporal patterns of urban green space topics on Twitter during the COVID-19 pandemic

Abstract

This study aims to detect spatial-temporal changes in urban green space (UGS) topics pre-, during, and after the peak of the COVID-19 pandemic. Twitter data was selected as data source. Structural topic modelling (STM) was used to identify UGS topics and detect the trends of all topics over time. The inverse distance weighted (IDW) interpolation method was used to show the spatial distributions of all topics over all periods. The research found that the topic Nature observation was the most popular among all topics and showed an increasing trend in topic proportions and dynamic changes in spatial-temporal patterns.

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Keywords

Urban green space, Topic detection, Spatial-temporal analysis, COVID-19

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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Corona Virus Disease