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A Geospatial Dynamic Microsimulation Model for Household Population Projections

Author

Listed:
  • Susan M. Rogers

    (Research Triangle Institute)

  • James Rineer

    (Research Triangle Institute)

  • Matthew D. Scruggs

    (Research Triangle Institute)

  • William D. Wheaton

    (Research Triangle Institute)

  • Phillip C. Cooley

    (Research Triangle Institute)

  • Douglas J. Roberts

    (Research Triangle Institute)

  • Diane K. Wagener

    (Research Triangle Institute)

Abstract

Forecasting Populations (FPOP) is a microsimulation model (MSM) that is the demographic core of an extensible modeling framework. The framework, with FPOP at its core, enables the geospatial projection of a population under purely demographic processes or under the additional influence of exogenous factors such as disease, policy changes and prevention programs, or environmental stressors. Empirically-derived transition probabilities of life events such as birth, death, marriage, divorce and migration, captured in lookup table format, drive the simulation. These transition probabilities can be modified dynamically by external user-defined functions or other external MSMs. The use of MSM structures and methodologies enables FPOP to portray the impact of heterogeneity in the geospatial dimension (e.g., distribution of environmental factors or distribution of intervention programs), as well as the social dimension (e.g., household or social network correlates), on the projections. POP is designed and structured to: enable linking with external MSMs of any kind; support inclusion or configuration of more detailed transition probabilities; be scalable to millions of agents; use either an existing baseline synthetic population or a custom synthetic population of the users design; and, run under computing environments that dont require a high degree of specialized software or hardware. In this paper we describe the design and structure of FPOP and then apply FPOP first under purely demographic processes and, secondly, in conjunction with an external disease model of obesity.The objective of FPOP is to provide a demographically realistic projection of the size, structure, and movement of populations and households decades into the future.

Suggested Citation

  • Susan M. Rogers & James Rineer & Matthew D. Scruggs & William D. Wheaton & Phillip C. Cooley & Douglas J. Roberts & Diane K. Wagener, 2014. "A Geospatial Dynamic Microsimulation Model for Household Population Projections," International Journal of Microsimulation, International Microsimulation Association, vol. 7(2), pages 119-146.
  • Handle: RePEc:ijm:journl:v:7:y:2014:i:2:p:119-146
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    References listed on IDEAS

    as
    1. Sabine Zinn, 2012. "A Mate-Matching Algorithm for Continuous-Time Microsimulation Models," International Journal of Microsimulation, International Microsimulation Association, vol. 5(1), pages 31-51.
    2. Zucchelli, E & Jones, A.M & Rice, N, 2010. "The evaluation of health policies through microsimulation methods," Health, Econometrics and Data Group (HEDG) Working Papers 10/03, HEDG, c/o Department of Economics, University of York.
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    4. Mark Levitan & Christine D'Onofrio & Gayatri Koolwal & John Krampner & Daniel Scheer & Todd Seidel & Vicky Virgin, 2010. "Using the American community survey to create a National Academy of Sciences-style poverty measure: Work by the New York City Center for Economic Opportunity," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 373-386.
    5. Beckman, Richard J. & Baggerly, Keith A. & McKay, Michael D., 1996. "Creating synthetic baseline populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(6), pages 415-429, November.
    6. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
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    Cited by:

    1. Mohana Mondal & Michael P. Cameron & Jacques Poot, 2021. "Towards a dynamic spatial microsimulation model for projecting Auckland's spatial distribution of ethnic groups," Working Papers in Economics 21/12, University of Waikato.
    2. Cathal O'Donoghue & Gijs Dekkers, 2018. "Increasing the Impact of Dynamic Microsimulation Modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 61-96.
    3. Abbygail Jaccard & Lise Retat & Martin Brown & Laura Webber & Zaid Chalabi, 2018. "Global Sensitivity Analysis of a Model Simulating an Individual’s Health State through Their Lifetime," International Journal of Microsimulation, International Microsimulation Association, vol. 11(3), pages 100-121.
    4. Barry J. Milne & Roy Lay-Yee & Jessica M. Mc Lay & Janet Pearson & Martin von Randow & Peter Davis, 2015. "Modelling the Early life-course (MELC): A Microsimulation Model of Child Development in New Zealand," International Journal of Microsimulation, International Microsimulation Association, vol. 8(2), pages 28-60.

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    More about this item

    Keywords

    Mathematical Models; Numerical Methods; Computational Techniques; Order Dynamics; Computer Programs; Software; Map-Reduce programming model;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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