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Conference Paper: UAPD: Predicting Urban Anomalies from Spatial-Temporal Data

TitleUAPD: Predicting Urban Anomalies from Spatial-Temporal Data
Authors
Issue Date2017
PublisherSpringer.
Citation
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2017), Skopje, Macedonia, 18-22 September 2017. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II, p. 622-638. Cham: Springer, 2017 How to Cite?
AbstractUrban city environments face the challenge of disturbances, which can create inconveniences for its citizens. These require timely detection and resolution, and more importantly timely preparedness on the part of city officials. We term these disturbances as anomalies, and pose the problem statement: if it is possible to also predict these anomalous events (proactive), and not just detect (reactive). While significant effort has been made in detecting anomalies in existing urban data, the prediction of future urban anomalies is much less well studied and understood. In this work, we formalize the future anomaly prediction problem in urban environments, such that those can be addressed in a more efficient and effective manner. We develop the Urban Anomaly PreDiction (UAPD) framework, which addresses a number of challenges, including the dynamic, spatial varieties of different categories of anomalies. Given the urban anomaly data to date, UAPD first detects the change point of each type of anomalies in the temporal dimension and then uses a tensor decomposition model to decouple the interrelations between the spatial and categorical dimensions. Finally, UAPD applies an autoregression method to predict which categories of anomalies will happen at each region in the future. We conduct extensive experiments in two urban environments, namely New York City and Pittsburgh. Experimental results demonstrate that UAPD outperforms alternative baselines across various settings, including different region and time-frame scales, as well as diverse categories of anomalies. Code related to this chapter is available at: https://bitbucket.org/xianwu9/uapd.
Persistent Identifierhttp://hdl.handle.net/10722/308742
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID
Series/Report no.Lecture Notes in Computer Science ; 10535
Lecture Notes in Artificial Intelligence ; 10535

 

DC FieldValueLanguage
dc.contributor.authorWu, Xian-
dc.contributor.authorDong, Yuxiao-
dc.contributor.authorHuang, Chao-
dc.contributor.authorXu, Jian-
dc.contributor.authorWang, Dong-
dc.contributor.authorChawla, Nitesh V.-
dc.date.accessioned2021-12-08T07:50:02Z-
dc.date.available2021-12-08T07:50:02Z-
dc.date.issued2017-
dc.identifier.citationJoint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2017), Skopje, Macedonia, 18-22 September 2017. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II, p. 622-638. Cham: Springer, 2017-
dc.identifier.isbn9783319712451-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/308742-
dc.description.abstractUrban city environments face the challenge of disturbances, which can create inconveniences for its citizens. These require timely detection and resolution, and more importantly timely preparedness on the part of city officials. We term these disturbances as anomalies, and pose the problem statement: if it is possible to also predict these anomalous events (proactive), and not just detect (reactive). While significant effort has been made in detecting anomalies in existing urban data, the prediction of future urban anomalies is much less well studied and understood. In this work, we formalize the future anomaly prediction problem in urban environments, such that those can be addressed in a more efficient and effective manner. We develop the Urban Anomaly PreDiction (UAPD) framework, which addresses a number of challenges, including the dynamic, spatial varieties of different categories of anomalies. Given the urban anomaly data to date, UAPD first detects the change point of each type of anomalies in the temporal dimension and then uses a tensor decomposition model to decouple the interrelations between the spatial and categorical dimensions. Finally, UAPD applies an autoregression method to predict which categories of anomalies will happen at each region in the future. We conduct extensive experiments in two urban environments, namely New York City and Pittsburgh. Experimental results demonstrate that UAPD outperforms alternative baselines across various settings, including different region and time-frame scales, as well as diverse categories of anomalies. Code related to this chapter is available at: https://bitbucket.org/xianwu9/uapd.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofMachine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 10535-
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence ; 10535-
dc.titleUAPD: Predicting Urban Anomalies from Spatial-Temporal Data-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-71246-8_38-
dc.identifier.scopuseid_2-s2.0-85040218016-
dc.identifier.spage622-
dc.identifier.epage638-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000443110500038-
dc.publisher.placeCham-

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