I’m always on the lookout for new ways to create more meaningful and impactful data visualizations. At Atelier Ten, we oftentimes went beyond merely translating data into information; we used simulations and visualizations to represent the experiential qualities of the built environment. Simulating and representing experiences, in service of conveying an idea, is a fascinating challenge and my ongoing interest in the subject prompted me to put together some work samples to try and better articulate the problem.
But first, some background. For 99% of my work as a design consultant, a good old-fashioned bar chart or line graph does the trick. As long as there are limited variables, and a clear story that needs to be told, these simple visualizations are extremely effective. Nevertheless, there are times when we confront the limits of conventional charts and graphs, even when we extend their functionality through interactivity. One of the first instances where I felt constrained by standard visualization libraries was in trying to map thermal comfort in buildings. This was a two part problem: first we had to figure out the appropriate metric for evaluating thermal comfort, and then we had to represent that metric in a way that people would understand.
For the metric, we settled on Predicted Mean Vote (PMV), which is a fairly well-documented and widely accepted method for calculating thermal comfort. One of the components of PMV is mean radiant temperature, which is partly a function of surface view factors–the degree to which a body “sees” each surface. There are a few ways of calculating this, but seeing as we already had the architectural model in Rhino, we used a raytracing script that simply counted the number of times a ray intersected each surface. The heart of the script relies on Vogel’s method for evenly distributing the rays, or vectors, around a sphere.
We face a similar problem of spatially and temporally mapping experiences in daylighting analysis. We want to show something dynamic. While it’s a little easier with light–it’s a visual representation of a visual experience–the same principles generally apply. One of the most common methods for representing lighting analysis results is with spatial maps. If the geometry is particularly complicated, you can even unfold surfaces to display analysis grids.
Moving forward, how do we leverage technology to represent complex experiences in the physical world. Acoustic engineers have sound labs, and lighting designers can start to use more immersive technologies like AR/VR. What is the best way to represent more complex phenomena, like thermal comfort? A few things I’ve learned in trying to answer these questions.
First, be very specific about what you’re analyzing. If you can narrow the analysis down to one or two variables, then it’s easier to use the visualization as a tool for making informed design decisions. Second, use visualization to inform the design process. This might sound intuitive, but it means designing visualizations as tools rather than one-off representations of an idea. If you are in a VR environment for example, you should be able to see design options without switching views, or loading a different dataset. Preserve object constancy whenever possible. Third, don’t fall into the sexy image trap. It’s hard to avoid the cult of architectural representation. Architects use various representational techniques to sell an idea, but that’s not the purpose of analysis. The point of this exercise is to better represent the reality of an experience to inform the design process.