Graph-Based Analysis of Best Practices in Autism Centre Design

This paper explores the application of a graph-based analysis to investigate the spatial configuration of autism centres, guided by best practices in designing for autism. Using a learning-from-precedents approach, this paper aims to quantify the spatial configuration of autism centres by discovering the relationships between functional spaces. Key graph metrics, including Centrality, PageRank, Node Degree, Community Detection, Bridges and Cut-Vertices, are utilised to uncover the spatial patterns of autism centres. This research employs Topologicpy, a spatial design and analysis Python library, to construct and analyse graphs that demonstrate the potential of graph-based methods in assessing the alignment of actual spatial configurations with autism-friendly design aspects. It revealed that the graph-based metrics provide insights into the accessibility and connectivity within the layouts. By proposing a graph-based analysis approach, this study recommends the integration of graph-based metrics in the design guidelines to quantify the design aspects introduced in the autism-friendly design considerations.