Abstract
AbstractIn order to anticipate the impact of local public policies, a synthetic population reflecting the characteristics of the local population provides a valuable test bed. While synthetic population datasets are now available for several countries, there is no open-source synthetic population for Canada. We propose an open-source synthetic population of individuals and households at a fine geographical level for Canada for the years 2021, 2023 and 2030. Based on 2016 census data and population projections, the synthetic individuals have detailed socio-demographic attributes, including age, sex, income, education level, employment status and geographic locations, and are related into households. A comparison of the 2021 synthetic population with 2021 census data over various geographical areas validates the reliability of the synthetic dataset. Users can extract populations from the dataset for specific zones, to explore ‘what if’ scenarios on present and future populations. They can extend the dataset using local survey data to add new characteristics to individuals. Users can also run the code to generate populations for years up to 2042.
Funder
RCUK | Economic and Social Research Council
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference57 articles.
1. Kagho, G. O., Balać, M. & Axhausen, K. W. Agent-based models in transport planning: current state, issues, and expectations. In The 9th International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS), 726–732, (2020).
2. Pagani, A., Ballestrazzi, F., Massaro, E. & Binder, C. R. ReMoTe-S. Residential mobility of tenants in Switzerland: an agent-based model. Journal of Artificial Societies and Social Simulation 25, 4 (2022).
3. Li, F., Li, Z., Chen, H., Chen, Z. & Li, M. An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen, China. Land Use Policy 95, 104620 (2020).
4. Oh, S. et al. Assessing the impacts of automated mobility-on-demand through agent-based simulation: a study of Singapore. Transportation Research Part A: Policy and Practice 138, 367–388 (2020).
5. Balać, M., Rothfeld, R. L. & Hörl, S. The Prospects of on-demand urban air mobility in Zurich, Switzerland. 2019 IEEE Intelligent Transportation Systems Conference (ITSC) 906–913 (2019).
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Hybrid Simulations;Fuzzy Cognitive Maps;2024
2. Synthetic population data for small area estimation in the United States;Environment and Planning B: Urban Analytics and City Science;2023-11-16