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)
- SPEW: Synthetic Populations and Ecosystems of the World article R package (archived), [https://github.com/leerichardson/spew 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 [2].
- 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). [3]. 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). [4]. 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 [5]. 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 [6]. 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 [7] 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 [8] 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.