Koutsolampros, P; Sailer, K; Varoudis, T; Haslem, R; (2019) Dissecting Visibility Graph Analysis: The metrics and their role in understanding workplace human behaviour. In: Proceedings of the 12th International Space Syntax Symposium (12 SSS). International Space Syntax Symposium: Beijing, China.

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Abstract

Visibility Graph Analysis (VGA) is one of the main methods of analysis of interior space within the field of Space Syntax, formulated by Turner et al. (2001) by extending Benedikt’s work on isovists and isovist fields (1979). It is a means to quantify the configuration of space as regular units which can then be used to identify the relationship of that space to the behaviour of the humans that occupy it. This paper is interested in the application of this method and its related metrics in workplaces, and how it can be used to understand the behaviour of office workers. Such information can then be used as evidence when designing new office spaces. We focus on the main tool used by the Space Syntax community: depthmapX (previously known as Depthmap). There are 25 VGA metrics that depthmapX can currently calculate, a mixture of classic graph-theory metrics, metrics borrowed from the urban-scale Space Syntax theories and some VGA-specific metrics describing local spatial properties. Some of the metrics are also derivatives, permutations and normalisations of other metrics which provide new information in relation to the configuration of space. While some of the metrics were described in previous research, there has never been a comprehensive understanding of all the VGA metrics produced by depthmapX, especially the concepts, formulae and algorithms behind their calculation. This has led researchers to focus on small subsets of these metrics avoiding thus the scattered and opaque nature of the theory and application. In previous research we have used VGA extensively aiming to understand human behaviour in office spaces but specifically only explored those that deal with local and global visibility. This paper first describes and elaborates on the various metrics produced by the current version of depthmapX and also outlines the theoretical considerations for each metric and how these potentially relate to human behaviour. Using a large dataset with VGA and observation data in office spaces we examine how these metrics relate to two kinds of behaviours: movement and interaction. We test how well each metric predicts each behaviour using two aggregations, per-floor and per-metric-quantile-bin. We show that for most of the metrics tested, permetric-quartile-bin works better than per-floor. The findings suggest that of the two behaviours examined, movement is best predicted, with many of the local and global metrics significant and with high effects. This paper contributes to the general Space Syntax field in relation to indoor spatial analysis, by providing a thorough description of the metrics of VGA. It also aims to highlight how and which of these metrics can be used to specifically understand human behaviour in workplaces. Ultimately, such information can be used to predict this behaviour in newly designed office-spaces and thus allow designers to inform their designs.

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