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GIS-based Methods for Estimating Missing Poverty Rates & Projecting Future Rates in Census Tracts

Author

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  • Srini Vasan

    (Department of Mathematics & Statistics, University of New Mexico MSC01 1115, 1 University of New Mexico, Albuquerque NM 87131-001 U.S.A.)

  • Adelamar Alcantara

    (Geospatial & Population Studies, University of New Mexico MSC01 1115, 1 University of New Mexico, Albuquerque NM 87131-001 U.S.A.)

Abstract

Since 2000 census, the American Community Survey (ACS) publishes poverty rate data based on five-year estimates only. We look at poverty rate estimation in two stages. In part 1, a situation where 5% of the poverty rate data is purposely missing from census tracts is simulated. Several interpolation methods were tried in GIS including Empirical Bayesian Kriging (EBK) and local polynomial interpolation (LPI). It is seen that using the EBK method a mean absolute percent error (MAPE) of 4.1% in the estimation process can be achieved as validated by the 2007-11 five year interval estimates of ACS poverty data. In part 2, the census tract poverty rates from 2000 as well as the ACS five year interval estimates from 2005-09, 2006-10 and 2007-11 were processed by first devising a procedure for unifying the underlying variable census tract geography. Then, poverty data for the time periods were used to create three dimensional poverty rate surfaces using the EBK method. Geographically Weighted Regression method enabled validation of the prediction process with a very low MAPE of 1.5% in comparison to the predicted poverty surface, followed by prediction of poverty rates across census tracts for a "future" period in time.

Suggested Citation

  • Srini Vasan & Adelamar Alcantara, 2016. "GIS-based Methods for Estimating Missing Poverty Rates & Projecting Future Rates in Census Tracts," Review of Economics & Finance, Better Advances Press, Canada, vol. 6, pages 1-13, August.
    • Handle: RePEc:bap:journl:160301
    Note: Short Title for Running Head: Poverty rate interpolation and prediction with GIS
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    References listed on IDEAS

    as
    1. Nicoletti, Cheti & Peracchi, Franco & Foliano, Francesca, 2011. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 61-72.
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    3. Matías Martínez & José Lorenzo & Noela Rubio, 2000. "Kriging methodology for regional economic analysis: Estimating the housing price in Albacete," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 6(3), pages 438-450, August.
    4. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May.
    5. Robert Tanton & Yogi Vidyattama & Binod Nepal & Justine McNamara, 2011. "Small area estimation using a reweighting algorithm," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 931-951, October.
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    Cited by:

    1. Rodrigo García Arancibia & Pamela Llop & Mariel Lovatto, 2023. "Nonparametric prediction for univariate spatial data: Methods and applications," Papers in Regional Science, Wiley Blackwell, vol. 102(3), pages 635-672, June.

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    More about this item

    Keywords

    Poverty rate; Kriging; Interpolation; Small area; Geographically weighted regression; Census tracts; Prediction;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • Y10 - Miscellaneous Categories - - Data: Tables and Charts - - - Data: Tables and Charts

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