  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.
