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Article: pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data

Titlepg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data
Authors
Keywordscausality
pattern mining
Bayesian learning
spatiotemporal (ST) big data
urban computing
Issue Date2018
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6687317
Citation
IEEE Transactions on Big Data, 2018, v. 4 n. 4, p. 571-585 How to Cite?
AbstractMany countries are suffering from severe air pollution. Understanding how different air pollutants accumulate and propagate is critical to making relevant public policies. In this paper, we use urban big data (air quality data and meteorological data) to identify the spatiotemporal (ST) causal pathways for air pollutants. This problem is challenging because: (1) there are numerous noisy and low-pollution periods in the raw air quality data, which may lead to unreliable causality analysis; (2) for large-scale data in the ST space, the computational complexity of constructing a causal structure is very high; and (3) the ST causal pathways are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present pg-Causality, a novel pattern-aided graphical causality analysis approach that combines the strengths of pattern mining and Bayesian learning to efficiently identify the ST causal pathways. First, pattern mining helps suppress the noise by capturing frequent evolving patterns (FEPs) of each monitoring sensor, and greatly reduce the complexity by selecting the pattern-matched sensors as 'causers'. Then, Bayesian learning carefully encodes the local and ST causal relations with a Gaussian Bayesian Network (GBN)-based graphical model, which also integrates environmental influences to minimize biases in the final results. We evaluate our approach with three real-world data sets containing 982 air quality sensors in 128 cities, in three regions of China from 01-Jun-2013 to 31-Dec-2016. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.
Persistent Identifierhttp://hdl.handle.net/10722/275017
ISSN
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhu, Y-
dc.contributor.authorZhang, C-
dc.contributor.authorZhang, H-
dc.contributor.authorZhi, S-
dc.contributor.authorLi, VOK-
dc.contributor.authorHan, J-
dc.contributor.authorZheng, Y-
dc.date.accessioned2019-09-10T02:33:45Z-
dc.date.available2019-09-10T02:33:45Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Big Data, 2018, v. 4 n. 4, p. 571-585-
dc.identifier.issn2332-7790-
dc.identifier.urihttp://hdl.handle.net/10722/275017-
dc.description.abstractMany countries are suffering from severe air pollution. Understanding how different air pollutants accumulate and propagate is critical to making relevant public policies. In this paper, we use urban big data (air quality data and meteorological data) to identify the spatiotemporal (ST) causal pathways for air pollutants. This problem is challenging because: (1) there are numerous noisy and low-pollution periods in the raw air quality data, which may lead to unreliable causality analysis; (2) for large-scale data in the ST space, the computational complexity of constructing a causal structure is very high; and (3) the ST causal pathways are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present pg-Causality, a novel pattern-aided graphical causality analysis approach that combines the strengths of pattern mining and Bayesian learning to efficiently identify the ST causal pathways. First, pattern mining helps suppress the noise by capturing frequent evolving patterns (FEPs) of each monitoring sensor, and greatly reduce the complexity by selecting the pattern-matched sensors as 'causers'. Then, Bayesian learning carefully encodes the local and ST causal relations with a Gaussian Bayesian Network (GBN)-based graphical model, which also integrates environmental influences to minimize biases in the final results. We evaluate our approach with three real-world data sets containing 982 air quality sensors in 128 cities, in three regions of China from 01-Jun-2013 to 31-Dec-2016. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6687317-
dc.relation.ispartofIEEE Transactions on Big Data-
dc.rightsIEEE Transactions on Big Data. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectcausality-
dc.subjectpattern mining-
dc.subjectBayesian learning-
dc.subjectspatiotemporal (ST) big data-
dc.subjecturban computing-
dc.titlepg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data-
dc.typeArticle-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TBDATA.2017.2723899-
dc.identifier.hkuros302924-
dc.identifier.volume4-
dc.identifier.issue4-
dc.identifier.spage571-
dc.identifier.epage585-
dc.identifier.isiWOS:000451911800011-
dc.publisher.placeUnited States-

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