Dynamic Census: Developing Dynamic Population Statistics using Mobile Phone Data

This project aims to develop dynamic demographics data of nationwide population, named Dynamic Census, using anonymized mobile phone data. An interoperable cloud system for Dynamic Census is also under development.

Population statistics, typically census data, have been widely used not only for making policies but also for devising development projects and research in various fields. However, it requires a lot of financial resources and much time for collecting and updating the data. The uniqueness of this project is that methodologies, which we are developing through Dynamic Census, are easily replicable globally and will help lower data collection costs greatly compared with conventional statistics. This is due to its high population coverage and similarities in mobile phone data formats and data items across countries and regions.

Our project will contribute to understanding regional human mobility patterns in Mozambique, which are considered to be closely related to the spread of Malaria. The project was awarded for Grand Challenge Explorations by the Bill & Melinda Gates Foundation in 2017. (Acceptance rate is 1.4% for Malaria Analytics projects and 2.1% of all applications). Our project in Sri Lanka is selected as one of 10 Innovative Projects by the Global Partnership for Sustainable Development Data (GPSDD) initiated by the United Nations and the World Bank (acceptance rate is 2.5%).

- LIRNEasia(Sri Lanka)
- Fundo de Estradas(Mozambique)

- Arai, A. 2015, September. PhD dissertation. Dynamic Census: Estimation of demographic structure and spatiotemporal distribution of dynamic living population by analyzing mobile phone call detail records.
- Arai, A., A. Witayangkurn, H. Kanasugi, T. Horanont, X. Shao, & R. Shibasaki. Understanding User Attributes from Calling Behavior: Exploring Call Detail Records through Field Observations. In Proceedings of the 12th International Conference on Advances in Mobile Computing & Multimedia (MoMM2014), pp. 94-104. 8-10 December 2014, Kaohsiung, Taiwan. ACM. DOI:10.1145/2684103.2684107.
- Arai, A., A. Witayangkurn, T. Horanont, X. Shao, & R. Shibasaki. Understanding the Unobservable Population in Call Detail Records through Analysis of Mobile Phone User Calling Behavior: A Case Study of Greater Dhaka in Bangladesh. In Proceedings of the 13th IEEE International Conference on Pervasive Computing and Communications (PerCom2015), pp. 207-214. 23-27 March 2015, St. Louis, USA. http://doi.ieeecomputersociety.org/10.1109/PERCOM.2015.7146530
- Arai, A., A. Witayangkurn, H. Kanasugi, T. Horanont, X. Shao, & R. Shibasaki. Understanding User Attributes from Calling Behavior: Exploring Call Detail Records through Field Observations and Potential of Estimating User Attributes of Anonymized Call Records. In Proceedings of NetMob 2015: Book of Abstracts::Oral. pp. 54-57. 7-10 April 2015, Boston, USA.
- Arai, A., Fan, Z., Matekenya, D., & Shibasaki, R. (2016). Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone Users. ISPRS International Journal of Geo-Information, 5 (6), 85. doi:10.3390/ijgi5060085.
- Fan, Z., Arai, A., Song, X., Witayangkurn, A., Kanasugi, H., & Shibasaki, R. ”A Collaborative Filtering Approach to Citywide Human Mobility Completion from Sparse Call Records”, Proc. of 25th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2500-2506, 2016. http://dl.acm.org/citation.cfm?id=3060971.