Dasymetric Modeling and Uncertainty

Abstract

Survey weights are often adjusted so that the estimated totals match with known benchmark totals. This practice is limited by the requirement that benchmarks be perfectly known and the tendency for survey weight variability to increase as more benchmarks are included. We modify the Iterative Proportional Fitting adjustment method to incorporate benchmarks that are imprecisely known. This allows the use of benchmarks controls from sources of data that are not currently eligible for benchmarking to, such as auxiliary surveys or other incomplete records. This method also allows us to efficiently increase the number and types of benchmark data that are used for survey weighting. We present results from efforts to adjust public use microdata samples to generate estimates and microsimulations for small areas (i.e. tracts and block groups).