Map line-ups: effects of spatial structure on graphical inference

graphical inference
spatial autocorrelation
uncertainty analysis
just noticeable difference
crime analysis

Roger Beecham, Jason Dykes, Wouter Meulemans, Aidan Slingsby, Cagatay Turkay and Jo Wood (2017) “Map line-ups: effects of spatial structure on graphical inference”, IEEE Transactions on Visualization & Computer Graphics, doi: 10.1109/TVCG.2016.2598862


School of Geography, University of Leeds

Department of Computer Science, City University of London

Department of Mathematics and Computer Science, TU Eindhoven

Department of Computer Science, City University of London

Centre for Interdisciplinary Methodologies, University of Warwick

Department of Computer Science, City University of London


January 2017



Fundamental to the effective use of visualization as an analytic and descriptive tool is the assurance that presenting data visually provides the capability of making inferences from what we see. This paper explores two related approaches to quantifying the confidence we may have in making visual inferences from mapped geospatial data. We adapt Wickham et al.’s `Visual Line-up’ method as a direct analogy with Null Hypothesis Significance Testing (NHST) and propose a new approach for generating more credible spatial null hypotheses. Rather than using as a spatial null hypothesis the unrealistic assumption of complete spatial randomness, we propose spatially autocorrelated simulations as alternative nulls. We conduct a set of crowdsourced experiments (n = 361) to determine the just noticeable difference (JND) between pairs of choropleth maps of geographic units controlling for spatial autocorrelation (Moran’s I statistic) and geometric configuration (variance in spatial unit area). Results indicate that people’s abilities to perceive differences in spatial autocorrelation vary with baseline autocorrelation structure and the geometric configuration of geographic units. These results allow us, for the first time, to construct a visual equivalent of statistical power for geospatial data. Our JND results add to those provided in recent years by Klippel et al. (2011), Harrison et al. (2014) and Kay & Heer (2015) for correlation visualization. Importantly, they provide an empirical basis for an improved construction of visual line-ups for maps and the development of theory to inform geospatial tests of graphical inference.

Important figure

Figure 2: Example stimuli used in the experiments. Three categories of geography were used: regular grid, regular real and irregular real.

BibTeX citation

    title = {Map line-ups: effects of spatial structure on graphical inference},
          journal = {{IEEE} Transactions on Visualization and Computer Graphics},
    author = {Beecham, R. and Dykes, J. and Meulemans, W. and Slingsby, A. and Turkay, C. and Wood, J.},
    volume =  {23},
    number = {1},
    pages = {391--400},
    year = {2017}