Synthetic population

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Synthetic population is an imaginary population that is used in agent-based population modelling. It attempts to mimic the key properties of an actual population, e.g. age structure, geographical location etc. Synthetic populations have been used in modelling of epidemic diseases, microeconomics, urban planning, transport, ecology and more.

  • RTI U.S. Synthetic Household Population: Providing accurate representation of the complete household and person population throughout the United States [1] (RTI International was previously Research Triangle Institute)
  • IIASA World Population project (with shared socioeconomic projections SSP and European scenarios) [2]
  • SPEW: Synthetic Populations and Ecosystems of the World article R package (archived), [ Github (updated 2017-09-09). SPEW lets researchers choose from a variety of sampling methods for agent characteristics and locations and is implemented as an open-source R package.
  • Beckman 1996. Creating synthetic baseline populations [3].
  • Multi-Agent Transport Simulation MATSim book, 2016: an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested.
  • Synthetic Population Generation by Combining a Hierarchical, Simulation-Based Approach with Reweighting by Generalized Raking article (2015). A recent approach for generating populations of synthetic individuals through simulation is extended to produce households of grouped individuals. The method involves a two-step approach with Gibbs sampling or hierarchical Markov chain Monte Carlo (MCMC), which was able to generate a hierarchical structure. The second step, a postprocessing step, uses generalized raking (GR),
  • Synthetic Population Generation at Disaggregated Spatial Scales for Land Use and Transportation Microsimulation (2014). [4]. This paper presents a two-stage population synthesis approach not only to improve the accuracy of population generation with imperfect microdata and marginal data, but also to use additional data sets when the spatial details of the synthetic population are interpolated. A general iterative proportional fitting (IPF) method is used in the first stage to estimate the joint distribution of household and individual characteristics under multiple levels of constraints. Additional building information is collected from multiple sources and used to estimate spatial patterns of housing and household characteristics that are then preserved through a second IPF procedure.
  • Population synthesis for microsimulation (2010). [5]. We summarize recent efforts to population synthesis for microsimulation (Auld et al., 2010; Pritchard and Miller, 2009; Ye et al., 2009; Srinivasan and Ma, 2009; Arentze et al., 2007; Guo and Bhat, 2007). All of the aforementioned works share two tasks: (a) adjustment of an initial population, taken from a past census or other survey data, to current constraints, and (b) selecting households and optionally assigning them to geographic areas.
  • GAMA PLATFORM [6]. GAMA is a modeling and simulation development environment for building spatially explicit agent-based simulations. It supports multiple application domains; is based on GAML, a high-level and intuitive agent-based language; use GIS and Data-Driven models: Instantiate agents from any dataset, including GIS data, and execute large-scale simulations (up to millions of agents); and declare interfaces supporting deep inspections on agents, user-controlled action panels, multi-layer 2D/3D displays & agent aspects.
    • Designing social simulation to (seriously) support decision-making: COMOKIT, an agent-based modeling toolkit to analyze and compare the impacts of public health interventions against COVID-19 [7]. This approach is currently being implemented by an interdisciplinary group of modellers, all signatories of this response, who have started to design and implement on the GAMA platform a generic model called COMOKIT, around which they now wish to gather the maximum number of modellers and researchers in epidemiology and social sciences.
    • Gen*: a generic toolkit to generate spatially explicit synthetic populations [8] intro to sampling methods: A complete generic toolkit called Gen* dedicated to generating spatially explicit synthetic populations from global (census and GIS) data. This article focuses on the localization methods provided by Gen* that are based on regression, geometrical constraints and spatial distributions. Gen* works on GAMA (written in Java) using GAML notation. It does not understand layered populations (like households and individuals)
  • Geard et al 2012. Synthetic Population Dynamics: A Model of Household Demography [9] We present a parsimonious individual-based model for generating synthetic population dynamics that focuses on the effects that demographic change have on the structure and composition of households.

Agent-based modelling

Synthetic population is an excellent starting point for agent-based modelling. This is a list of some relevant references. References

  • The Goldilocks Challenge: Right-fit Evidence for the Social Sector by Mary Kay Gugerty and Dean Karlan, Oxford University Press, 2018. ISBN: 019936608X, 9780199366088.
  • Angel Hsu, Jonas Tan, Yi Ming Ng, Wayne Toh, Regina Vanda & Nihit Goyal. Performance determinants show European cities are delivering on climate mitigation. Nature Climate Change volume 10, pages 1015–1022 (2020).
  • Kagho GO, Balac M, Axhausen KW. Agent-based models in transport planning: current state, issues, and expectations. Procedia Computer Science 170 (2020) 726–732 [10]
  • Nägeli C, Jakob M, Catenazzi G, Ostermeyer Y. Towards agent-based building stock modeling: Bottom-up modeling of long-term stock dynamics affecting the energy and climate impact of building stocks. Energy and Buildings Volume 211, 15 March 2020, 109763.
  • Stefan Pfenninger, Joseph DeCarolis, Lion Hirth, Sylvain Quoilin, Iain Staffell. The importance of open data and software: Is energy research lagging behind? Energy Policy Volume 101, February 2017, Pages 211-215.
  • Tuomisto, JT; Tainio, M. 2005. An economic way of reducing health, environmental, and other pressures of urban traffic: a decision analysis on trip aggregation. BMC PUBLIC HEALTH 5:123.
  • Tuomisto JT, Yrjölä J, Kolehmainen M, Bonsdorff J, Pekkanen J, Tikkanen T. (2020b) An agent-based epidemic model REINA for COVID-19 to identify destructive policies. MedRxiv preprint, submitted 2020-04-09. doi:
  • Adnan, M., Outay, F., Ahmed, S. et al. Integrated agent-based microsimulation framework for examining impacts of mobility-oriented policies. Pers Ubiquit Comput 25, 205–217 (2021).
  • Hoertel, N., Blachier, M., Blanco, C. et al. A stochastic agent-based model of the SARS-CoV-2 epidemic in France. Nat Med 26, 1417–1421 (2020).
  • Agent-based building stock modelling [11]
  • Open Source Web-based Transit Demand Modeling (Using GTFS) [12]