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Compiling Granular Population Data Using Geospatial Information

Author

Listed:
  • Thomas Mitterling

    (World Data Lab)

  • Katharina Fenz

    (World Data Lab)

  • Arturo Martinez Jr

    (Asian Development Bank)

  • Joseph Bulan

    (Asian Development Bank)

  • Mildred Addawe

    (Asian Development Bank)

  • Ron Lester Durante

    (Asian Development Bank)

  • Marymell Martillan

    (Asian Development Bank)

Abstract

Granular spatial information on the distributions of human population is relevant to a variety of fields like health, economics, and other areas of public sector planning. This paper applies ensemble methods and aims at assessing their applicability to analyzing and forecasting population density on a grid level. In a first step, we use a Random Forest approach to estimate population density in the Philippines and Thailand on a 100 meter by 100-meter level. Second, we use different specifications of Random Forest and Bayesian model averaging techniques to create forecasts of the grid-level population density in three Thailand provinces and evaluate their predictive power.

Suggested Citation

  • Thomas Mitterling & Katharina Fenz & Arturo Martinez Jr & Joseph Bulan & Mildred Addawe & Ron Lester Durante & Marymell Martillan, 2021. "Compiling Granular Population Data Using Geospatial Information," ADB Economics Working Paper Series 643, Asian Development Bank.
    • Handle: RePEc:ris:adbewp:0643
    as

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    References listed on IDEAS

    as
    1. Zeugner, Stefan & Feldkircher, Martin, 2015. "Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i04).
    2. Tiago M. Fragoso & Wesley Bertoli & Francisco Louzada, 2018. "Bayesian Model Averaging: A Systematic Review and Conceptual Classification," International Statistical Review, International Statistical Institute, vol. 86(1), pages 1-28, April.
    3. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • D30 - Microeconomics - - Distribution - - - General
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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