Small-Area Analyses Using Public American Community Survey Data: A Tree-Based Spatial Microsimulation Technique

Graetz, Nick; Ummel, Kevin; Da, Cohen

doi:10.2139/ssrn.3574679

Cited by 1 publication

(1 citation statement)

References 38 publications

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“…More recently, researchers have begun to employ more complex machine learning techniques to predict fine-scale population distributions within source zones, often using a wide array of ancillary datasets, such as road networks, nighttime lights, infrastructure and building footprint data, in addition to land use layers as covariates in the models [ 27 – 32 ]. These highly-modeled approaches can represent a significant improvement from simpler techniques, especially in regions of the world for which source zone population estimates from official census surveys are infrequent and/or only exist at very coarse spatial resolutions [ 33 – 37 ]. Leyk et al (2019 ) [ 11 ] provide a thorough review of dasymetric mapping methods employed in past studies, including these highly-modeled approaches, to construct large-scale (i.e.…”

Section: Introductionmentioning

confidence: 99%

High-resolution gridded estimates of population sociodemographics from the 2020 census in California

2022

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This paper introduces a series of high resolution (100-meter) population grids for eight different sociodemographic variables across the state of California using data from the 2020 census. These layers constitute the ‘CA-POP’ dataset, and were produced using dasymetric mapping methods to downscale census block populations using fine-scale residential tax parcel boundaries and Microsoft’s remotely-sensed building footprint layer as ancillary datasets. In comparison to a number of existing gridded population products, CA-POP shows good concordance and offers a number of benefits, including more recent data vintage, higher resolution, more accurate building footprint data, and in some cases more sophisticated but parsimonious and transparent dasymetric mapping methodologies. A general accuracy assessment of the CA-POP dasymetric mapping methodology was conducted by producing a population grid that was constrained by population observations within block groups instead of blocks, enabling a comparison of this grid’s population apportionment to block-level census values, yielding a median absolute relative error of approximately 30% for block group-to-block apportionment. However, the final CA-POP grids are constrained by higher-resolution census block-level observations, likely making them even more accurate than these block group-constrained grids over a given region, but for which error assessments of population disaggregation is not possible due to the absence of observational data at the sub-block scale. The CA-POP grids are freely available as GeoTIFF rasters online at github.com/njdepsky/CA-POP, for total population, Hispanic/Latinx population of any race, and non-Hispanic populations for the following groups: American Indian/Alaska Native, Asian, Black/African-American, Native Hawaiian and other Pacific Islander, White, other race or multiracial (two or more races) and residents under 18 years old (i.e. minors).

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