Human Centric Accessibility Graph For Environment Analysis

Spatial Human Accessibility graph for Planning and Environment Analysis (SHAPE) provides a foundation for computational design and spatial analysis for human factors.  It connects the generation of accessibility graphs (analgous to a navmesh) and evaluation. In addition to the research paper that outlines the process and algorithms, the codebase is available here.  


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.

Fig. 1

The three environmental scales of City, Building, and Object, are all integrated. Path analysis and accessibility is from one point to another, and includes numerous environmental conditions.

Fig. 2

A multi-level building example of using the AG. Steps shown are (1) A starting model is given in the top left (2) the graph is generated finding accessible locations (3) a viewshed-type analysis is performed to find locations with maximal space (4) path planning start and end locations determined (5) generate paths with various criteria (6) reflection on paths generated by finding use cases for a given fire stairwell vs. the open staircase.

Fig. 4

Energy cost of walking at various gradients as defined by the polynomial function in Minetti et al. [13] and shown in Definition 9.

Fig. 7

The breadth-first process of defining node locations. The numbers correspond to the order the node was checked. Two box objects are defined as obstacles while the surface itself is a free form shape in 3D.

Fig. 16

Two paths generated with (16(a)) distance only and (16(b)) distance with the added cost of a cross-slope.


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.