Building Consistent Multi-temporal Population Data at Fine Resolution through Spatially Refined Areal Interpolation

Wednesday Sep 28   05:00 PM to 07:00 PM (2 hours)
Areal InterpolationSpatial RefinementDemographic AnalysisError ReductionCensus
Demographic data are aggregated over areal units to protect privacy and are often inconsistent over time, impeding demographic analysis across different censuses at fine spatial resolution. For example, U.S. census tracts show boundary changes over time due to underlying population dynamics. Areal interpolation methods are used to estimate population in one census year within the units of another year to construct temporally consistent small census units. This research enhances these methods by introducing three advanced spatial refinement approaches, tested in Mecklenburg County, North Carolina to estimate population in 2000 within census tracts from the 2010 census. The results demonstrate the effectiveness of spatial refinement, reducing estimation errors, systematically. The proposed methods can be used to analyze micro-scale spatio-temporal demographic processes with minimum estimation error.
University of Colorado

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