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Choropleth maps have been studied and extended in many ways to counteract themany biases that can occur when using them. Two recent techniques, Surprisemetrics and Value Suppressing Uncertainty Palettes (VSUPs), offer promisingsolutions but have yet to be tested empirically with users of visualizations.In this paper, we explore how well people can make use of these techniques inmap exploration tasks. We report a crowdsourced experiment where n = 300participants are assigned to one of Choropleth, Surprise (only), and VSUPconditions (depicting rates and Surprise in a suppressed palette). Results showclear differences in map analysis outcomes, e.g. with Surprise maps leadingpeople to significantly higher areas of population, or VSUPs performing similaror better than Choropleths for rate selection. Qualitative analysis suggeststhat many participants may only consider a subset of the metrics presented tothem during exploration and decision-making. We discuss how these resultsgenerally support the use of Surprise and VSUP techniques in practice, andopportunities for further technique development. The material for the study(data, study results and code) is publicly available on https://osf.io/exb95/.

Taken By Surprise? Evaluating how Bayesian Weighting Influences Peoples' Takeaways in Map Visualizations