Small multiples enable comparison by providing different views of a single data set in a dense and aligned manner. A common frame defines each view, which varies based upon values of a conditioning variable. An increasingly popular use of this technique is to project two-dimensional locations into a gridded space (e.g. grid maps), using the underlying distribution both as the conditioning variable and to determine the grid layout. Using whitespace in this layout has the potential to carry information, especially in a geographic context. Yet, the effects of doing so on the spatial properties of the original units are not understood.  We explore the design space offered by such small multiples with gaps. We do so by constructing a comprehensive suite of metrics that capture properties of the layout used to arrange the small multiples for comparison (e.g. compactness and alignment) and the preservation of the original data (e.g. distance, topology and shape). We study these metrics in geographic data sets with varying properties and numbers of gaps. We use simulated annealing to optimize for each metric and measure the effects on the others. To explore these effects systematically, we take a new approach, developing a system to visualize this design space using a set of interactive matrices. We find that adding small amounts of whitespace to small multiple arrays improves some of the characteristics of 2D layouts, such as shape, distance and direction. This comes at the cost of other metrics, such as the retention of topology. Effects vary according to the input maps, with degree of variation in size of input regions found to be a factor. Optima exist for particular metrics in many cases, but at different amounts of whitespace for different maps. We suggest multiple metrics be used in optimized layouts, finding topology to be a primary factor in existing manually-crafted solutions, followed by a trade-off between shape and displacement. But the rich range of possible optimized layouts leads us to challenge single-solution thinking; we suggest to consider alternative optimized layouts for small multiples with gaps. Key to our work is the systematic, quantified and visual approach to exploring design spaces when facing a trade-off between many competing criteria—an approach likely to be of value to the analysis of other design spaces.

Citation and full paper:

Meulemans, W., Dykes, J., Slingsby, A., Turkay, C. & Wood, J. (2017). Small Multiples with GapsIEEE Transactions on Visualization and Computer Graphics.


Two resources can be downloaded for this paper: 

  1. The data and corresponding visualisation tool used in the analysis. Simply extract the archive and open “user manual.pdf” for further instructions on how to use the software to explore the data.
  2. The code used to generate small multiples with gaps. Again, simply extract the archive and open “smwg.html”. Its interface contains some annotation and labels to help you get started with it. Note that running it locally requires you to allow access to local files from local pages. This is allowed by default in Firefox, but not in many other browsers. To allow this in Chrome, launch Chrome with the “--allow-file-access-from-files” switch. Alternatively, we are also hosting the SMWG computation online or host it on your own (local) server.