Abstract
Areal interpolation is routinely used when spatial data are unavailable at desired geographical units. While many methods are available, few of them were developed specifically for and tested in highly developed urban cores. Even fewer studied subpopulation or population characteristics. This paper explores both issues using parcel map and decennial census data as ancillary information. Using census blocks as intermediate zones, the method first disaggregates source-zone data to intermediate zones, then disaggregates data to parcel level in intermediate zones intersecting target zones, and finally aggregates intermediate-zone and parcel-level estimates to obtain target-zone estimates. Compared to areal weighting and residential proportion, the proposed method is significantly more accurate. All three methods perform the best on population count, and worst on spatially clustered subpopulations such as black/African American population. Quotient variables are more difficult to interpolate than count variables. The research demonstrates the utility of parcel and decennial census data for areal interpolation in highly developed urban cores, and calls for future research on subpopulation and population characteristics.
Highlights
Accurate information on population distribution is essential for myriad applications such as transportation analysis, healthcare planning, and environmental management
We examine the utility of parcel data to estimate population and subpopulations in highly developed urban environments
The 2013 American Community Survey (ACS) data are publicly available by block groups and census tracts, but since block groups are more spatially detailed than census tracts, they are used as the source zones
Summary
Accurate information on population distribution is essential for myriad applications such as transportation analysis, healthcare planning, and environmental management. The U.S National Land Cover Dataset (NLCD), used by many studies (e.g., [35]), has a 30-meter spatial resolution and differentiates developed area into only three classes of high, medium, and low density This dataset is adequate for national-level research, but too aggregated for urban cores where it is not uncommon to find multiple land uses in a single street block or even building unit. Sophisticated statistical methods such as cokriging [15], regression [36], and maximum entropy allocation [18] can remedy the limitation in land use data to some extent, but high quality land use data that accurately reflect the variation in population and housing density are essential for population estimation in highly developed urban environments.
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