Population Location and Movement Estimation through Cross-domain Data Analysis

Yang, X; Liu, W

Population Location and Movement Estimation through Cross-domain Data Analysis

Yang, X

Liu, W

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Publisher:: International Joint Conferences on Artificial Intelligence Organization https://www.ijcai.org/Proceedings/2020/0736.pdf
Publication Type:: Conference Proceeding
Citation:: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020, pp. 5192-5193
Issue Date:: 2020-07

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Field	Value	Language
dc.contributor.author	Yang, X https://orcid.org/0000-0001-6487-3183
dc.contributor.author	Liu, W https://orcid.org/0000-0002-3003-1313
dc.date	2020-07-11
dc.date.accessioned	2021-02-03T02:12:22Z
dc.date.available	2021-02-03T02:12:22Z
dc.date.issued	2020-07
dc.identifier.citation	Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020, pp. 5192-5193
dc.identifier.isbn	9780999241165
dc.identifier.uri	http://hdl.handle.net/10453/145750
dc.description.abstract	<jats:p>Estimations on people movement behaviour within a country can provide valuable information to government strategic resource plannings. In this paper, we propose to utilize multi-domain statistical data to estimate people movements under the assumption that most population tend to move to areas with similar or better living conditions. We design a Multi-domain Matrix Factorization (MdMF) model to discover the underlying consistency patterns from these cross-domain data and estimate the movement trends using the proposed model. This research can provide important theoretical support to government and agencies in strategic resource planning and investments.</jats:p>
dc.language	en
dc.publisher	International Joint Conferences on Artificial Intelligence Organization https://www.ijcai.org/Proceedings/2020/0736.pdf
dc.relation.ispartof	Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
dc.relation.ispartof	Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}
dc.relation.isbasedon	10.24963/ijcai.2020/736
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Population Location and Movement Estimation through Cross-domain Data Analysis
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-02-03T02:12:08Z
pubs.finish-date	2020-07-17
pubs.publication-status	Published
pubs.start-date	2020-07-11

Abstract:

Estimations on people movement behaviour within a country can provide valuable information to government strategic resource plannings. In this paper, we propose to utilize multi-domain statistical data to estimate people movements under the assumption that most population tend to move to areas with similar or better living conditions. We design a Multi-domain Matrix Factorization (MdMF) model to discover the underlying consistency patterns from these cross-domain data and estimate the movement trends using the proposed model. This research can provide important theoretical support to government and agencies in strategic resource planning and investments.

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http://hdl.handle.net/10453/145750