Koutsolampros, P; Sailer, K; Haslem, R; (2020) Travel Concentration: The effects of attractor-bound movement on workplace activity. In: Kaempf-Dern, A and Will-Zocholl, M, (eds.) Future Workspaces. Proceedings of the Transdisciplinary Workplace Research (TWR) Conference 2020. (pp. pp. 395-406). TWR Network: Frankfurt am Main, Germany.

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Abstract

PURPOSE: The purpose of this paper is to explore the effects of office attractors on workplace activity. First, it aims to describe how movement towards different attractors such as canteens and entrances can be approximated in a 2D spatial model, and second, to show how those simulated effects relate to actual observations of movement and interaction. THEORY: Human activity in physical workspace is typically examined from the perspective of the purely geometric properties of the space (i.e. in the field of space syntax), or by other properties of workspaces, such as barriers and distance between workers. Movement in offices however is an activity that is driven by both geometric and non-geometric properties. The non-geometric properties relate to the functional configuration of space (where seats/canteens/meeting rooms are) but the activity itself happens in the real space and it is thus bound by spatial configuration.Furthermore, while the driver for movement is the need to travel to specific attractors, it is the actual space that allows for secondary effects such as serendipitous interactions to emerge. Thus, it can be expected that a successful approximation of workplace movement will also contribute to understanding interaction, especially that which happens away from spaces programmed for it such as meeting rooms. This paper examines the two activities of movement and interaction under the hypothesis that a spatial model that properly simulates attractor-bound movement can successfully identify the locations where movement happens, but also provide relevant hints for serendipitous interaction. DESIGN/METHODOLOGY/APPROACH: To study this hypothesis, we constructed paths from each seat to a set of three types of attractors, specifically the building entrance, the closest canteen or kitchen and the closest WC. These paths were then transformed to zones of visibility to take into account the surrounding space as well as to allow for interaction to be examined as that activity is unlikely to happen directly on the path. The final result is a metric of travel concentration that measures how likely is it that a space will be seen from those generated paths. The metric is validated against actual observations of movement and interaction in a linear model, tested initially against a large sample of different workplaces (216 floors), but also against two sets of floors, one with high and one with low seat density. FINDINGS: The new metric fares well against both movement and interaction on the whole sample, but on the two sets of floors the effects are less robust. In high-density floors the main driver of attractor movement is the one generated from outside the floor and to a lesser extent the one that comes from within the floor. In low density floors only interaction is somewhat predictable albeit with a weak effect and only in relation to travel from within the floor. Travel concentration was found to be less effective than the existing Visual Mean Depth metric, however combinations of the two were found, in some cases to yield the best results. ORIGINALITY/VALUE: The new metric presented here is a useful simulation of movement in office spaces which can be applied to the analysis of existing spaces, but also provide a way for designers to test against floor plans of new buildings.

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