Human centric accessibility graph for environment analysis

TY - JOUR

T1 - Human centric accessibility graph for environment analysis

AU - Schwartz, Mathew

N1 - Publisher Copyright: © 2021

PY - 2021/7

Y1 - 2021/7

N2 - Understanding design decisions in relation to the future occupants of a building is a crucial part of good design. However, limitations in tools and expertise hinder meaningful human-centric decisions during the design process. In this paper, a novel Spatial Human Accessibility graph for Planning and Environment Analysis (SHAPE) is introduced that brings together the technical challenges of discrete representations of digital models, with human-based metrics for evaluating the environment. SHAPE: does not need labeled geometry as input, works with multi-level buildings, captures surface variations (e.g., slopes in a terrain), and can be used with existing graph theory (e.g., gravity, centrality) techniques. SHAPE uses ray-casting to perform a search, generating a dense graph of all accessible locations within the environment and storing the type of travel required in a graph (e.g., up a slope, down a step). The ability to simultaneously evaluate and plan paths from multiple human factors is shown to work on digital models across room, building, and topography scales. The results enable designers and planners to evaluate options of the built environment in new ways, and at higher fidelity, that will lead to more human-friendly and accessible environments.

AB - Understanding design decisions in relation to the future occupants of a building is a crucial part of good design. However, limitations in tools and expertise hinder meaningful human-centric decisions during the design process. In this paper, a novel Spatial Human Accessibility graph for Planning and Environment Analysis (SHAPE) is introduced that brings together the technical challenges of discrete representations of digital models, with human-based metrics for evaluating the environment. SHAPE: does not need labeled geometry as input, works with multi-level buildings, captures surface variations (e.g., slopes in a terrain), and can be used with existing graph theory (e.g., gravity, centrality) techniques. SHAPE uses ray-casting to perform a search, generating a dense graph of all accessible locations within the environment and storing the type of travel required in a graph (e.g., up a slope, down a step). The ability to simultaneously evaluate and plan paths from multiple human factors is shown to work on digital models across room, building, and topography scales. The results enable designers and planners to evaluate options of the built environment in new ways, and at higher fidelity, that will lead to more human-friendly and accessible environments.

KW - Accessibility

KW - Computation

KW - Graph

KW - Human factors

KW - Spatial analysis

KW - Walkability

UR - https://www.scopus.com/pages/publications/85105281342

UR - https://www.scopus.com/pages/publications/85105281342#tab=citedBy

U2 - 10.1016/j.autcon.2021.103557

DO - 10.1016/j.autcon.2021.103557

M3 - Article

SN - 0926-5805

VL - 127

JO - Automation in Construction

JF - Automation in Construction

M1 - 103557

ER -