Latent Dirichlet Allocation (LDA) topic models for Space Syntax studies on spatial experience

Abstract

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.