{"type":"ScholarlyArticle","title":"A Spatial Missing Value Imputation Method for Multi-view Urban Statistical Data","authors":[{"name":"Yongshun Gong","slug":"yongshun-gong","given":"Yongshun","family":"Gong","affiliation":"Faculty of Engineering and IT , University of Technology Sydney , Sydney , Australia"},{"name":"Zhibin Li","slug":"zhibin-li","given":"Zhibin","family":"Li","affiliation":"Faculty of Engineering and IT , University of Technology Sydney , Sydney , Australia"},{"name":"Jian Zhang","slug":"jian-zhang","given":"Jian","family":"Zhang","affiliation":"Faculty of Engineering and IT , University of Technology Sydney , Sydney , Australia"},{"name":"Wei Liu","slug":"wei-liu","given":"Wei","family":"Liu","affiliation":"Faculty of Engineering and IT , University of Technology Sydney , Sydney , Australia"},{"name":"Bei Chen","slug":"bei-chen","given":"Bei","family":"Chen","affiliation":"Microsoft Research , Beijing , China"},{"name":"Xiangjun Dong","slug":"xiangjun-dong","given":"Xiangjun","family":"Dong","affiliation":"School of Computer , Qilu University of Technology (Shandong Academy of Sciences) , Jinan , China"}],"abstract":"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.","doi":"10.24963/ijcai.2020/182","url":"https://ijcai.org/proceedings/2020/182","pdf":"https://ijcai.org/proceedings/2020/0182.pdf","markdown":"https://ijcai.org/proceedings/2020/182.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/182","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":182,"pages":{"first":1310,"last":1316},"keywords":["Data Mining: Big Data, Large-Scale Systems","Data Mining: Applications","Data Mining: Mining Spatial, Temporal Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24970/file_24970.m3u8","length_sec":298,"provider":"hls"}],"references":[{"position":1,"raw":"[Atluri et al., 2018] Gowtham Atluri, Anuj Karpatne, and Vipin Kumar. 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