Ranking places in attributed temporal urban mobility networks

Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can furt...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2020-10, Vol.15 (10), p.e0239319-e0239319
Main Authors: Nanni, Mirco, Tortosa, Leandro, Vicent, José F, Yeghikyan, Gevorg
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
Biology and Life Sciences
Cities
Computer and Information Sciences
Computer science
Earth Sciences
Economic conditions
Economics
Evolution
Geospatial data
Graph theory
Graphical representations
Heterogeneity
Medicine and Health Sciences
Mobility
Nodes
Physical Sciences
Research and Analysis Methods
Social mobility
Social networks
Social Sciences
Socioeconomics
Urban areas
Visualization
Workflow
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c735t-8cbcedca27b774541265a66a1784c424046acdf73b17c960e701f2c2386f50643
cites cdi_FETCH-LOGICAL-c735t-8cbcedca27b774541265a66a1784c424046acdf73b17c960e701f2c2386f50643
container_end_page e0239319
container_issue 10
container_start_page e0239319
container_title PloS one
container_volume 15
creator Nanni, Mirco
Tortosa, Leandro
Vicent, José F
Yeghikyan, Gevorg
description Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.
doi_str_mv 10.1371/journal.pone.0239319
format article
fullrecord <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2451126853</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A638372410</galeid><doaj_id>oai_doaj_org_article_f8b378e5f17e436fb8d23df21bee8fcb</doaj_id><sourcerecordid>A638372410</sourcerecordid><originalsourceid>FETCH-LOGICAL-c735t-8cbcedca27b774541265a66a1784c424046acdf73b17c960e701f2c2386f50643</originalsourceid><addsrcrecordid>eNqNkltrFDEYhgdRbK3-A8EBQfRi15wze6FQioeFQqEebkOSSXbTZpI1yaj992bdUTrSC8lFwpfne78Db9M8hWAJMYevr-KYgvTLXQxmCRBeYbi61xzDFUYLhgC-f-t91DzK-QoAijvGHjZHGAOKVpAdN28uZbh2YdPuvNQmty60spTk1FhM3xYz7GKSvh2TkqEdonLelZs2mPIjpuv8uHlgpc_myXSfNF_ev_t89nFxfvFhfXZ6vtAc07LotNKm1xJxxTmhBCJGJWMS8o5oggggTOrecqwg1ysGDAfQIo1qt5YCRvBJ8-ygu_Mxi2nyLBChsGp1FFdifSD6KK_ELrlBphsRpRO_AzFthEzFaW-E7RTmnaEWckMws6rrEe4tgsqYzmpVtd5O1UY11L5NKHUHM9H5T3BbsYnfBaeUUcyrwMtJIMVvo8lFDC5r470MJo6HvnEHMEUVff4Pevd0E7WRdQAXbKx19V5UnDLcYY4IBJVa3kHV05vB6WoT62p8lvBqllCZYn6WjRxzFutPl__PXnydsy9usVsjfdnm6MfiYshzkBxAnWLOydi_S4ZA7F3-Zxti73IxuRz_AjEr7K0</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2451126853</pqid></control><display><type>article</type><title>Ranking places in attributed temporal urban mobility networks</title><source>Publicly Available Content (ProQuest)</source><source>PubMed Central</source><creator>Nanni, Mirco ; Tortosa, Leandro ; Vicent, José F ; Yeghikyan, Gevorg</creator><contributor>Abbasi, Alireza</contributor><creatorcontrib>Nanni, Mirco ; Tortosa, Leandro ; Vicent, José F ; Yeghikyan, Gevorg ; Abbasi, Alireza</creatorcontrib><description>Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0239319</identifier><identifier>PMID: 33052916</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Artificial intelligence ; Biology and Life Sciences ; Cities ; Computer and Information Sciences ; Computer science ; Earth Sciences ; Economic conditions ; Economics ; Evolution ; Geospatial data ; Graph theory ; Graphical representations ; Heterogeneity ; Medicine and Health Sciences ; Mobility ; Nodes ; Physical Sciences ; Research and Analysis Methods ; Social mobility ; Social networks ; Social Sciences ; Socioeconomics ; Urban areas ; Visualization ; Workflow</subject><ispartof>PloS one, 2020-10, Vol.15 (10), p.e0239319-e0239319</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Nanni et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Nanni et al 2020 Nanni et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c735t-8cbcedca27b774541265a66a1784c424046acdf73b17c960e701f2c2386f50643</citedby><cites>FETCH-LOGICAL-c735t-8cbcedca27b774541265a66a1784c424046acdf73b17c960e701f2c2386f50643</cites><orcidid>0000-0002-2990-1879</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2451126853/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2451126853?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids></links><search><contributor>Abbasi, Alireza</contributor><creatorcontrib>Nanni, Mirco</creatorcontrib><creatorcontrib>Tortosa, Leandro</creatorcontrib><creatorcontrib>Vicent, José F</creatorcontrib><creatorcontrib>Yeghikyan, Gevorg</creatorcontrib><title>Ranking places in attributed temporal urban mobility networks</title><title>PloS one</title><description>Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial intelligence</subject><subject>Biology and Life Sciences</subject><subject>Cities</subject><subject>Computer and Information Sciences</subject><subject>Computer science</subject><subject>Earth Sciences</subject><subject>Economic conditions</subject><subject>Economics</subject><subject>Evolution</subject><subject>Geospatial data</subject><subject>Graph theory</subject><subject>Graphical representations</subject><subject>Heterogeneity</subject><subject>Medicine and Health Sciences</subject><subject>Mobility</subject><subject>Nodes</subject><subject>Physical Sciences</subject><subject>Research and Analysis Methods</subject><subject>Social mobility</subject><subject>Social networks</subject><subject>Social Sciences</subject><subject>Socioeconomics</subject><subject>Urban areas</subject><subject>Visualization</subject><subject>Workflow</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqNkltrFDEYhgdRbK3-A8EBQfRi15wze6FQioeFQqEebkOSSXbTZpI1yaj992bdUTrSC8lFwpfne78Db9M8hWAJMYevr-KYgvTLXQxmCRBeYbi61xzDFUYLhgC-f-t91DzK-QoAijvGHjZHGAOKVpAdN28uZbh2YdPuvNQmty60spTk1FhM3xYz7GKSvh2TkqEdonLelZs2mPIjpuv8uHlgpc_myXSfNF_ev_t89nFxfvFhfXZ6vtAc07LotNKm1xJxxTmhBCJGJWMS8o5oggggTOrecqwg1ysGDAfQIo1qt5YCRvBJ8-ygu_Mxi2nyLBChsGp1FFdifSD6KK_ELrlBphsRpRO_AzFthEzFaW-E7RTmnaEWckMws6rrEe4tgsqYzmpVtd5O1UY11L5NKHUHM9H5T3BbsYnfBaeUUcyrwMtJIMVvo8lFDC5r470MJo6HvnEHMEUVff4Pevd0E7WRdQAXbKx19V5UnDLcYY4IBJVa3kHV05vB6WoT62p8lvBqllCZYn6WjRxzFutPl__PXnydsy9usVsjfdnm6MfiYshzkBxAnWLOydi_S4ZA7F3-Zxti73IxuRz_AjEr7K0</recordid><startdate>20201014</startdate><enddate>20201014</enddate><creator>Nanni, Mirco</creator><creator>Tortosa, Leandro</creator><creator>Vicent, José F</creator><creator>Yeghikyan, Gevorg</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2990-1879</orcidid></search><sort><creationdate>20201014</creationdate><title>Ranking places in attributed temporal urban mobility networks</title><author>Nanni, Mirco ; Tortosa, Leandro ; Vicent, José F ; Yeghikyan, Gevorg</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c735t-8cbcedca27b774541265a66a1784c424046acdf73b17c960e701f2c2386f50643</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Analysis</topic><topic>Artificial intelligence</topic><topic>Biology and Life Sciences</topic><topic>Cities</topic><topic>Computer and Information Sciences</topic><topic>Computer science</topic><topic>Earth Sciences</topic><topic>Economic conditions</topic><topic>Economics</topic><topic>Evolution</topic><topic>Geospatial data</topic><topic>Graph theory</topic><topic>Graphical representations</topic><topic>Heterogeneity</topic><topic>Medicine and Health Sciences</topic><topic>Mobility</topic><topic>Nodes</topic><topic>Physical Sciences</topic><topic>Research and Analysis Methods</topic><topic>Social mobility</topic><topic>Social networks</topic><topic>Social Sciences</topic><topic>Socioeconomics</topic><topic>Urban areas</topic><topic>Visualization</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nanni, Mirco</creatorcontrib><creatorcontrib>Tortosa, Leandro</creatorcontrib><creatorcontrib>Vicent, José F</creatorcontrib><creatorcontrib>Yeghikyan, Gevorg</creatorcontrib><collection>CrossRef</collection><collection>Gale in Context : Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Proquest Nursing &amp; Allied Health Source</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>ProQuest - Health &amp; Medical Complete保健、医学与药学数据库</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>Biological Sciences</collection><collection>Agriculture Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>ProQuest Biological Science Journals</collection><collection>Engineering Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>ProQuest advanced technologies &amp; aerospace journals</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nanni, Mirco</au><au>Tortosa, Leandro</au><au>Vicent, José F</au><au>Yeghikyan, Gevorg</au><au>Abbasi, Alireza</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ranking places in attributed temporal urban mobility networks</atitle><jtitle>PloS one</jtitle><date>2020-10-14</date><risdate>2020</risdate><volume>15</volume><issue>10</issue><spage>e0239319</spage><epage>e0239319</epage><pages>e0239319-e0239319</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>33052916</pmid><doi>10.1371/journal.pone.0239319</doi><tpages>e0239319</tpages><orcidid>https://orcid.org/0000-0002-2990-1879</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1932-6203
ispartof PloS one, 2020-10, Vol.15 (10), p.e0239319-e0239319
issn 1932-6203
1932-6203
language eng
recordid cdi_plos_journals_2451126853
source Publicly Available Content (ProQuest); PubMed Central
subjects Algorithms
Analysis
Artificial intelligence
Biology and Life Sciences
Cities
Computer and Information Sciences
Computer science
Earth Sciences
Economic conditions
Economics
Evolution
Geospatial data
Graph theory
Graphical representations
Heterogeneity
Medicine and Health Sciences
Mobility
Nodes
Physical Sciences
Research and Analysis Methods
Social mobility
Social networks
Social Sciences
Socioeconomics
Urban areas
Visualization
Workflow
title Ranking places in attributed temporal urban mobility networks
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T06%3A47%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Ranking%20places%20in%20attributed%20temporal%20urban%20mobility%20networks&rft.jtitle=PloS%20one&rft.au=Nanni,%20Mirco&rft.date=2020-10-14&rft.volume=15&rft.issue=10&rft.spage=e0239319&rft.epage=e0239319&rft.pages=e0239319-e0239319&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0239319&rft_dat=%3Cgale_plos_%3EA638372410%3C/gale_plos_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c735t-8cbcedca27b774541265a66a1784c424046acdf73b17c960e701f2c2386f50643%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2451126853&rft_id=info:pmid/33052916&rft_galeid=A638372410&rfr_iscdi=true