Population Location and Movement Estimation through Cross-domain Data Analysis

Population Location and Movement Estimation through Cross-domain Data Analysis

Xinghao Yang, Wei Liu

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 5192-5193. https://doi.org/10.24963/ijcai.2020/736

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.
Keywords:
Data Mining: Clustering, Unsupervised Learning
Machine Learning: Multi-instance;Multi-label;Multi-view learning
Machine Learning: Tensor and Matrix Methods
Machine Learning: Clustering