Population mobility, urban centrality and subnetworks in China revealed by social sensing big data

Guo, H; Han, J; Wang, J

Population mobility, urban centrality and subnetworks in China revealed by social sensing big data

Guo, H Han, J Wang, J

Permalink

Publisher:: SAGE PUBLICATIONS INC
Publication Type:: Journal Article
Citation:: Environment and Planning A, 2021, 53, (8), pp. 1855-1858
Issue Date:: 2021-11-01

Closed Access

	Filename	Description	Size
	0308518x211035400.pdf	Published version	876.2 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Guo, H
dc.contributor.author	Han, J
dc.contributor.author	Wang, J https://orcid.org/0000-0001-6128-1254
dc.date.accessioned	2021-12-22T04:16:55Z
dc.date.available	2021-12-22T04:16:55Z
dc.date.issued	2021-11-01
dc.identifier.citation	Environment and Planning A, 2021, 53, (8), pp. 1855-1858
dc.identifier.issn	0308-518X
dc.identifier.issn	1472-3409
dc.identifier.uri	http://hdl.handle.net/10453/152464
dc.description.abstract	Studying the spatial pattern of population flows is important to gain insights into economic connections, city networks and traffic demand. The dearth of integrative data suggests that the spatial pattern of population migration has still received relatively scant research with modified algorithms despite its vital guidance in epidemic control. To address this gap, we employ unique social sensing data from the Tencent migration platform to investigate the spatial configuration of population migration in China. Based on the identified 2,555,596 aggregated origin-destination records in 2018, we map the spatial pattern of population flow with the algorithm of eigenvector centrality. The geovisualisation illustrates that population flows present an uneven spatial pattern with a clear east/west divide. The geovisualisation depicts that the cities with higher administrative levels show corresponding high centrality to a certain extent. The results also show that subnetworks of migration flow demonstrate that population flows are near movements.
dc.language	English
dc.publisher	SAGE PUBLICATIONS INC
dc.relation	National Natural Science Foundation of China71973093
dc.relation.ispartof	Environment and Planning A
dc.relation.isbasedon	10.1177/0308518X211035400
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1205 Urban and Regional Planning, 1402 Applied Economics, 1604 Human Geography
dc.subject.classification	Geography
dc.title	Population mobility, urban centrality and subnetworks in China revealed by social sensing big data
dc.type	Journal Article
utslib.citation.volume	53
utslib.for	1205 Urban and Regional Planning
utslib.for	1402 Applied Economics
utslib.for	1604 Human Geography
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access	*
dc.date.updated	2021-12-22T04:16:54Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	53
utslib.citation.issue	8

Abstract:

Studying the spatial pattern of population flows is important to gain insights into economic connections, city networks and traffic demand. The dearth of integrative data suggests that the spatial pattern of population migration has still received relatively scant research with modified algorithms despite its vital guidance in epidemic control. To address this gap, we employ unique social sensing data from the Tencent migration platform to investigate the spatial configuration of population migration in China. Based on the identified 2,555,596 aggregated origin-destination records in 2018, we map the spatial pattern of population flow with the algorithm of eigenvector centrality. The geovisualisation illustrates that population flows present an uneven spatial pattern with a clear east/west divide. The geovisualisation depicts that the cities with higher administrative levels show corresponding high centrality to a certain extent. The results also show that subnetworks of migration flow demonstrate that population flows are near movements.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/152464