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ScalableAccess: Computing Travel-Time Zones of Fine Spatial Granularity for Accessibility Analysis at Scale
Topics: Spatial Analysis & Modeling
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Keywords: Spatial Accessibility, Spatial Analysis, CyberGIS Session Type: Virtual Paper Abstract Day: Monday Session Start / End Time: 2/28/2022 09:40 AM (Eastern Time (US & Canada)) - 2/28/2022 11:00 AM (Eastern Time (US & Canada)) Room: Virtual 61
Authors:
Alexander Michels, University of Illinois at Urbana-Champaign
Jeon-Young Kang, Kongju National University, South Korea
Shaowen Wang, University of Illinois at Urbana-Champaign
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Abstract
ScalableAccess is developed as a novel algorithm for spatial partitioning which is used for efficiently computing travel-time zones over large spatial extents with a user-provided memory limit on computation. This allows researchers to derive fine-grain zones for spatial accessibility analysis at scales and granularities that were previously computationally infeasible. ScalableAccess realizes this through spatial partitioning that preserves spatial relationships required to compute travel-time zones and respects a user-provided memory limit. The steps of the algorithm merge parts of the network by spatial relationships and estimates of the combined network's memory requirement. An enhancement of a spatial accessibility analysis method allows users to aggregate results to flexible geographic units. We demonstrate ScalableAccess by computing spatial accessibility to hospital beds across the continental United States.
ScalableAccess: Computing Travel-Time Zones of Fine Spatial Granularity for Accessibility Analysis at Scale
