To measure population changes in areas where census unit boundaries do not align across time, a common approach is to interpolate data from one census's units to another's. This article presents a broad assessment of areal interpolation models for estimating counts of 2000 characteristics in 2010 census units throughout the United States. We interpolate from 2000 census block data using 4 types of ancillary data to guide interpolation: 2010 block densities, imperviousness data, road buffers, and water body polygons. We test 8 binary dasymetric (BD) models and 8 target-density weighting (TDW) models, each using a unique combination of the 4 ancillary data types, and derive 2 hybrid models that blend the best-performing BD and TDW models. The most accurate model is a hybrid that generally gives high weight to TDW (allocating 2000 data in proportion to 2010 densities) but gives increasing weight to a BD model (allocating data uniformly within developed land near roads) in proportion to the estimated 2000–2010 rate of change within each block. Although for most 2010 census units, this hybrid model's estimates differ little from the simplest model's estimates, there are still many areas where the estimates differ considerably. Estimates from the final model, along with lower and upper bounds for each estimate, are publicly available for over 1000 population and housing characteristics at 10 geographic levels via the National Historical Geographic Information System (NHGIS – http://nhgis.org).
|Original language||English (US)|
|Number of pages||11|
|Journal||Computers, Environment and Urban Systems|
|State||Published - Mar 1 2017|
Bibliographical noteFunding Information:
This work was completed for the NHGIS project with funding from the National Science Foundation [ SES-1324875 ] and Eunice Kennedy Shriver National Institute of Child Health and Human Development [ NICHD 2R01HD057929 ]. David Van Riper advised on the research and data product design, and Kevin Horne and Jacob Wellington implemented the software that generates the final standardized data. The author also gratefully acknowledges support from the Minnesota Population Center [ NICHD R24HD041023 ].
- Areal interpolation
- Census geography
- Population estimation
- Spatio-temporal analysis