{"type":"ScholarlyArticle","title":"A Decomposition Approach for Urban Anomaly Detection Across Spatiotemporal Data","authors":[{"name":"Mingyang Zhang","slug":"mingyang-zhang","given":"Mingyang","family":"Zhang","affiliation":"The Hong Kong University of Science and Technology"},{"name":"Tong Li","slug":"tong-li","given":"Tong","family":"Li","affiliation":"The Hong Kong University of Science and Technology"},{"name":"Hongzhi Shi","slug":"hongzhi-shi","given":"Hongzhi","family":"Shi","affiliation":"Tsinghua University"},{"name":"Yong Li","slug":"yong-li","given":"Yong","family":"Li","affiliation":"Tsinghua University"},{"name":"Pan Hui","slug":"pan-hui","given":"Pan","family":"Hui","affiliation":"The Hong Kong University of Science and Technology; University of Helsinki"}],"abstract":"Urban anomalies such as abnormal flow of crowds and traffic accidents could result in loss of life or property if not handled properly. Detecting urban anomalies at the early stage is important to minimize the adverse effects. However, urban anomaly detection is difficult due to two challenges: a) the criteria of urban anomalies varies with different locations and time; b) urban anomalies of different types may show different signs. In this paper, we propose a decomposing approach to address these two challenges. Specifically, we decompose urban dynamics into the normal component and the abnormal component. The normal component is merely decided by spatiotemporal features, while the abnormal component is caused by anomalous events. Then, we extract spatiotemporal features and estimate the normal component accordingly. At last, we derive the abnormal component to identify anomalies. We evaluate our method using both real-world and synthetic datasets. The results show our method can detect meaningful events and outperforms state-of-the-art anomaly detecting methods by a large margin.","doi":"10.24963/ijcai.2019/837","url":"https://ijcai.org/proceedings/2019/837","pdf":"https://ijcai.org/proceedings/2019/0837.pdf","markdown":"https://ijcai.org/proceedings/2019/837.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/837","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"AI for Improving Human Well-being","track":"AI for Improving Human Well-being","number":837,"pages":{"first":6043,"last":6049},"keywords":["Special Track on AI for Improving Human-Well Being: AI safety (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Breunig et al., 2000] Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. 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