Spatial experience has been extensively researched in various fields, with Space Syntax being one of the most widely used methodologies. Multiple Space Syntax techniques have been developed and used to quantitively examine the relationship between spatial configuration and human experience. However, due to the heterogeneity of syntactic measures and experiential issues in the built environment, a systematic review of socio-spatial topics has yet to be developed for Space Syntax research. In response to this knowledge gap, this article employs an ‘intelligent’ method to classify and systematically review topics in Space Syntax studies on spatial experience. Specifically, after identifying 66 articles using the ‘Preferred Reporting Items for Systematic reviews and Meta-Analyses’ (PRISMA) framework, this research develops generative probabilistic topic models to classify the articles using the Latent Dirichlet Allocation (LDA) method. As a result, this research automatically generates three architectural topics from the collected literature data (A1. Wayfinding behaviour, A2. Interactive accessibility, and A3. Healthcare design) and three urban topics (U1. Pedestrian movement, U2. Park accessibility, and U3. Cognitive city). Thereafter it qualitatively examines the implications of the data and its LDA classification. This article concludes with an examination of the limitations of both the methods and the results. Along with demonstrating a methodological innovation (combining PRISMA with LDA), this research identifies critical socio-spatial concepts and examines the complexity of Space Syntax applications. In this way, this research contributes to future Space Syntax research that empirically investigates the relationships between syntactic and experiential variables in architectural and urban spaces. The findings support a detailed discussion about research gaps in the literature and future research directions.

中文翻译：

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用于空间体验空间句法研究的潜在狄利克雷分配 (LDA) 主题模型

空间体验在各个领域得到了广泛的研究，空间句法是最广泛使用的方法之一。多种空间句法技术已被开发并用于定量检查空间配置与人类经验之间的关系。然而，由于句法测量的异质性和建筑环境中的经验问题，空间句法研究尚未开发出对社会空间主题的系统回顾。针对这一知识空白，本文采用“智能”的方法对空间体验的空间句法研究主题进行分类和系统回顾。具体来说，在使用“系统评论和元分析的首选报告项目”(PRISMA) 框架识别 66 篇文章后，本研究开发了生成概率主题模型，使用潜在狄利克雷分配 (LDA) 方法对文章进行分类。因此，本研究从收集的文献数据中自动生成三个建筑主题（A1.寻路行为、A2.交互式可达性和A3.医疗保健设计）和三个城市主题（U1.行人运动、U2.公园可达性和U3） .认知城市）。此后，它定性地检查数据及其 LDA 分类的含义。本文最后检查了方法和结果的局限性。除了展示方法创新（将 PRISMA 与 LDA 相结合）之外，本研究还确定了关键的社会空间概念并检查了空间语法应用的复杂性。通过这种方式，这项研究有助于未来的空间句法研究，实证研究建筑和城市空间中句法变量和经验变量之间的关系。研究结果支持对文献中的研究差距和未来研究方向的详细讨论。