  Spatial and visual connectivity are important metrics when developing
workplace layouts. Calculating those metrics in real-time can be difficult,
depending on the size of the floor plan being analysed and the resolution of
the analyses. This paper investigates the possibility of considerably speeding
up the outcomes of such computationally intensive simulations by using machine
learning to create models capable of identifying the spatial and visual
connectivity potential of a space. To that end we present the entire process of
investigating different machine learning models and a pipeline for training
them on such task, from the incorporation of a bespoke spatial and visual
connectivity analysis engine through a distributed computation pipeline, to the
process of synthesizing training data and evaluating the performance of
different neural networks.
