GIS-based Methods for Estimating Missing Poverty Rates & Projecting Future Rates in Census Tracts
Srini Vasan () and
Adelamar Alcantara ()
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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.
Review of Economics & Finance, 2016, vol. 6, pages 1-13
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.
Keywords: Poverty rate; Kriging; Interpolation; Small area; Geographically weighted regression; Census tracts; Prediction (search for similar items in EconPapers)
JEL-codes: C11 C15 Y10 (search for similar items in EconPapers)
Note: Short Title for Running Head: Poverty rate interpolation and prediction with GIS
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