Population Location and Movement Estimation through Cross-domain Data Analysis

Xinghao Yang; Wei Liu

IJCAI 2020poster0 citations

Population Location and Movement Estimation through Cross-domain Data Analysis

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.

Data Mining: Clustering, Unsupervised LearningMachine Learning: Multi-instanceMulti-labelMulti-view learningMachine Learning: Tensor and Matrix MethodsMachine Learning: Clustering

BibTeX

@inproceedings{ijcai2020p736,
  title     = {Population Location and Movement Estimation through Cross-domain Data Analysis},
  author    = {Yang, Xinghao and Liu, Wei},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Christian Bessiere},
  pages     = {5192--5193},
  year      = {2020},
  month     = {7},
  note      = {Doctoral Consortium},
  doi       = {10.24963/ijcai.2020/736},
  url       = {https://doi.org/10.24963/ijcai.2020/736},
}