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.

中文翻译：

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用于环境分析的以人为本的可访问性图

了解有关建筑物未来居住者的设计决策是良好设计的关键部分。但是，工具和专业知识的局限性阻碍了在设计过程中有意义的以人为本的决策。本文介绍了一种新颖的规划和环境分析空间人类可及性图（SHAPE），该图将数字模型的离散表示的技术挑战与评估环境的基于人的度量标准结合在一起。形状：不需要标记的几何体作为输入，可以与多层建筑物配合使用，捕获表面变化（例如地形中的坡度），并且可以与现有的图论（例如重力，中心度）技术一起使用。SHAPE使用射线投射进行搜索，生成环境中所有可访问位置的密集图，并在图中存储所需的行进类型（例如，上坡，下台阶）。同时显示能够同时评估和规划来自多个人为因素的路径的能力可以在房间，建筑物和地形比例尺的数字模型上起作用。结果使设计人员和规划人员能够以新的方式和更高的保真度评估构建环境的选项，从而可以建立更人性化和可访问的环境。