2021-09-30, 15:30–16:00, Academic
High resolution mapping of human populations is often achieved through the disaggregation of aggregate counts (e.g. census tabulations) from tabulation areas (source zones) to smaller areas (target zones), with the aid of ancillary spatial data characterizing the built environment (e.g. land cover/use, building footprints) with some known or presumed functional relationship with population density. Source zones and built environment data, found in a variety of raster/vector formats and resolutions, are often converted to a common raster resolution for analysis. This process is computationally efficient at coarse resolutions and existing software and methods facilitate modeling for those with an understanding of raster-based spatial analysis.
This approach has several shortcomings due to limitations of raster data formats. When compared to vectors, the other common geospatial data format, rasters are less precise, hold less information, and are less conducive to smaller area constructs, such as building outlines and parcels, and are less accessible to the broader scientific community because of the special handling required. Given these shortcomings, we propose a vector analytical framework for population modeling. The framework is designed to combine all of the lines defining the input layers so that fields enclosed by those lines (i.e. polygons) are uniformly attributable to each of the input layers. This richer data stack allows for the development of models with more complex logic that are straightforward to implement and explain, as well as increasing the accessibility of modeled estimates and intermediate layers to a broader audience.
Moehl, Jessica (1)
Weber, Eric (1)
McKee, Jacob (1)
(1) Oak Ridge National LaboratoryTrack –
2 - Basic. General basic knowledge is required.Language of the Presentation –
Jessica is a research scientist at Oak Ridge National Laboratory