SurpriseExplora: Tuning and Contextualizing Model-derived Maps with Interactive Visualizations

Abstract

People craft choropleth maps to monitor, analyze, and understand spatially distributed data. Recent visualization work has addressed several known biases in choropleth maps by developing new model- and metrics- based approaches (e.g. Bayesian surprise). However, effective use of these techniques requires extensive parameter setting and tuning, making them difficult or impossible for users without substantial technical skills. In this paper we describe SurpriseExplora, which addresses this gap through direct manipulation techniques for re-targeting a Bayesian surprise model's scope and parameters. We present three use cases to illustrate the capabilities of SurpriseExplora, showing for example how models calculated at a national level can obscure key findings that can be revealed through interaction sequences common to map visualizations (e.g. zooming), and how augmenting funnel-plot visualizations with interactions that adjust underlying models can account for outliers or skews in spatial datasets. We evaluate SurpriseExplora through an expert review with visualization researchers and practitioners. We conclude by discussing how SurpriseExplora uncovers new opportunities for sense-making within the broader ecosystem of map visualizations, as well as potential empirical studies with non-expert populations.

Code and demo video available at https://osf.io/7m89w/