Population downscaling using high-resolution, temporally-rich U.S. property data. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Wan, Heng; Yoon, Jim; Srikrishnan, Vivek; Daniel, Brent; Judi, David
• Is Part Of: Cartography and geographic information science. Volume 49:Issue 1(2022)
• Page Start: 18
• Page End: 31
• Publication Date: 2022-01-02
• Subjects: Population downscaling -- dasymetric mapping -- BUPR -- HISDAC-US -- CONUS; Cartography -- Periodicals; Geographic information systems -- Periodicals; 526
• DOI: 10.1080/15230406.2021.1991479
• Languages: English
• ISSNs: 1523-0406

Abstract

ABSTRACT: Multi-temporal and spatially explicit population data are vital in many fields, such as demography, urban planning, disaster prevention, economics, and environmental modeling. Population data used in these studies are typically aggregated at census enumeration units, which are too coarse for many applications. Accurate population downscaling methods are needed to obtain population data at finer spatial resolutions. We use a novel settlement-related database, Built-Up Property Records (BUPR) from the Historical Settlement Data Compilation for the United States (HISDAC-US) to downscale population from census tracts to block groups. The BUPR dataset provides the number of built-up property records for each 250-m grid at 5-year temporal resolution from 1810 to 2015 for most contiguous United States (CONUS). The ability of BUPR to downscale population from census tracts to block groups for four states, representing a range of population densities, is evaluated here by comparing against other commonly-used ancillary datasets. The BUPR-based method outperforms all other methods in all but one state with highly-incomplete BUPR. A more detailed accuracy assessment is performed by dividing each state into low, medium, and high population density categories. The BUPR method produces more accurate downscaled population estimates for low and medium categories, though its performance deteriorates in the high density category due to its relatively coarse spatial resolution. BUPR-based dasymetric mapping is subsequently applied to the CONUS and found to generalize well beyond the four comparison states with high downscaling accuracy. The long-term record of the HISDAC-US dataset enables the potential construction of fine-grained population data back to 1810.