{"type":"ScholarlyArticle","title":"Population Location and Movement Estimation through Cross-domain Data Analysis","authors":[{"name":"Xinghao Yang","slug":"xinghao-yang","given":"Xinghao","family":"Yang","affiliation":"Advanced Analytics Institute , School of Computer Science , University of Technology Sydney , Australia"},{"name":"Wei Liu","slug":"wei-liu","given":"Wei","family":"Liu","affiliation":"Advanced Analytics Institute , School of Computer Science , University of Technology Sydney , Australia"}],"abstract":"Estimations on people movement behaviour within a country can provide valuable information to government strategic resource plannings. In this paper, we propose to utilize multi-domain statistical data to estimate people movements under the assumption that most population tend to move to areas with similar or better living conditions. We design a Multi-domain Matrix Factorization (MdMF) model to discover the underlying consistency patterns from these cross-domain data and estimate the movement trends using the proposed model. This research can provide important theoretical support to government and agencies in strategic resource planning and investments.","doi":"10.24963/ijcai.2020/736","url":"https://ijcai.org/proceedings/2020/736","pdf":"https://ijcai.org/proceedings/2020/0736.pdf","markdown":"https://ijcai.org/proceedings/2020/736.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/736","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":"Doctoral Consortium","track":"Doctoral Consortium","number":736,"pages":{"first":5192,"last":5193},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Clustering","Data Mining: Clustering, Unsupervised Learning","Machine Learning: Tensor and Matrix Methods"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24668/file_24668.m3u8","length_sec":272,"provider":"hls"}],"references":[{"position":1,"raw":"[Do et al., 2017] Quan Do, Wei Liu, and Fang Chen. Dis- covering both explicit and implicit similarities for cross- domain recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 618-630. Springer, 2017.","title":"Discovering both explicit and implicit similarities for crossdomain recommendation","authors":"Quan Do, Wei Liu, Fang Chen","year":"2017","venue":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}