Interactive map reports summarizing bivariate geographic data

Bivariate map visualizations use different encodings to visualize two variables but comparison across multiple encodings is challenging. Compared to a univariate visualization, it is significantly harder to read regional differences and spot geographical outliers. Especially targeting inexperienced users of visualizations, we advocate the use of natural language text for augmenting map visualizations and understanding the relationship between two geo-statistical variables. We propose an approach that selects interesting findings from data analysis, generates a respective text and visualization, and integrates both into a single document. The generated reports interactively link the visualization with the textual narrative. Users can get additional explanations and have the ability to compare different regions. The text generation process is flexible and adapts to various geographical and contextual settings based on small sets of parameters. We showcase this flexibility through a number of application examples. (C) 2019 Zhejiang University and Zhejiang University Press. Published by Elsevier B.V.