A spatial missing value imputation method for multi-view urban statistical data

Gong, Y; Li, Z; Zhang, J; Liu, W; Chen, B; Dong, X

A spatial missing value imputation method for multi-view urban statistical data

Gong, Y Li, Z Zhang, J

Liu, W

Chen, B Dong, X

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Publisher:: International Joint Conferences on Artificial Intelligence Organization
Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2020, 2021-January, pp. 1310-1316
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Gong, Y
dc.contributor.author	Li, Z
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541
dc.contributor.author	Liu, W https://orcid.org/0000-0002-3003-1313
dc.contributor.author	Chen, B
dc.contributor.author	Dong, X
dc.date	2020-07-11
dc.date.accessioned	2021-01-13T06:20:04Z
dc.date.available	2021-01-13T06:20:04Z
dc.date.issued	2020-01-01
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2020, 2021-January, pp. 1310-1316
dc.identifier.isbn	9780999241165
dc.identifier.issn	1045-0823
dc.identifier.uri	http://hdl.handle.net/10453/145397
dc.description.abstract	© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved. Large volumes of urban statistical data with multiple views imply rich knowledge about the development degree of cities. These data present crucial statistics which play an irreplaceable role in the regional analysis and urban computing. In reality, however, the statistical data divided into fine-grained regions usually suffer from missing data problems. Those missing values hide the useful information that may result in a distorted data analysis. Thus, in this paper, we propose a spatial missing data imputation method for multi-view urban statistical data. To address this problem, we exploit an improved spatial multi-kernel clustering method to guide the imputation process cooperating with an adaptive-weight non-negative matrix factorization strategy. Intensive experiments are conducted with other state-of-the-art approaches on six real-world urban statistical datasets. The results not only show the superiority of our method against other comparative methods on different datasets, but also represent a strong generalizability of our model.
dc.language	en
dc.publisher	International Joint Conferences on Artificial Intelligence Organization
dc.relation.ispartof	IJCAI 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.2020/182
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A spatial missing value imputation method for multi-view urban statistical data
dc.type	Conference Proceeding
utslib.citation.volume	2021-January
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-13T06:19:59Z
pubs.finish-date	2020-07-17
pubs.publication-status	Published
pubs.start-date	2020-07-11
pubs.volume	2021-January

Abstract:

© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved. Large volumes of urban statistical data with multiple views imply rich knowledge about the development degree of cities. These data present crucial statistics which play an irreplaceable role in the regional analysis and urban computing. In reality, however, the statistical data divided into fine-grained regions usually suffer from missing data problems. Those missing values hide the useful information that may result in a distorted data analysis. Thus, in this paper, we propose a spatial missing data imputation method for multi-view urban statistical data. To address this problem, we exploit an improved spatial multi-kernel clustering method to guide the imputation process cooperating with an adaptive-weight non-negative matrix factorization strategy. Intensive experiments are conducted with other state-of-the-art approaches on six real-world urban statistical datasets. The results not only show the superiority of our method against other comparative methods on different datasets, but also represent a strong generalizability of our model.

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http://hdl.handle.net/10453/145397