Validation of Abstract Population Representations
Creators: name: Gaboardi, James David
Publisher: name: Zenodo
Year: 2020
Subjects: Geography
Geographic Population Representation
Spatial Abstraction
Population Microdata
GIS

description: Conference Information: 2019 Atlanta Research Data Center Annual Research Conference Vanderbilt University October 4, 2019 Abstract Spatial networks (e.g., roads, etc.) are fundamental to the human experience and, as such, attention must be given to their study in the spatial analysis of anthropocentric phenomena. In order to perform analyses on network-based events the objects in question must be allocated to the network. When the objects are known locations – points (e.g., fire stations) – this is done with certainty by snapping each one to the nearest network segment (road). However, when considering populations there is much more uncertainty due to the format of available population data generally being spatially-aggregated areas – polygons (e.g., census geographies). Two current methods for allocating population to networks are (1) the highly-efficient, industry-standard approach of snapping the geometric centroids of polygons to network segments; and (2) an advanced state of-the-art approach whereby areal interpolation of population is performed prior to being allocated to the network. This paper compares these two methods of network allocation to a novel method, populated polygons to networks (<em>pp2n</em>). The <em>pp2n</em> method generates population abstractions along network segments known to be populated (in this paper through publicly-available 2010 Decennial Census data), and does so with a similar worst-case computational complexity as the industry standard while displaying significantly less actual runtime than the state-of-the-art approach. Therefore, the <em>pp2n</em> method retains the strengths of both the industry standard and state-of-the-art approaches while mitigating their weaknesses. In order to determine the effects of varying the method of network allocation, segment-based population statistics are utilized to compare these three abstract approaches within Leon County, FL in 2010. The three methods are benchmarked against a publicly-available “pseudo-truth” dataset created with population-weighted property parcel data and the “gold-standard truth” of the highly-restricted, housing-unit level 2017 Master Address File (MAF) subset to 2010. Particular attention is given to the process of spatial data validation for both the publicly-available and restricted-access data. Results show that both the <em>pp2n</em> and state-of-the-art approaches for network allocation produce population distributions along network segments similar to the benchmark MAF, with the <em>pp2n</em> performing in considerably less runtime. Also, all methods perform increasingly more accurately with progressive spatial disaggregation (tracts to block groups to blocks). Further, the population-weighted property parcel dataset performs reasonably well against the MAF-generated dataset, thereby demonstrating its validity and value as a publicly-available, “true” benchmark. This paper is based on a chapter in the dissertation, “Populated Polygons to Networks: A Population-Centric Approach to Spatial Network Allocation,” and was partially funded by the Atlanta Research Data Center through Florida State University.