GeoExplainer: A Visual Analytics Framework for Spatial Modeling
  Contextualization and Report Generation
Fan Lei Yuxin Ma Stewart Fotheringham Elizabeth Mack Ziqi Li Mehak Sachdeva Sarah Bardin Ross Maciejewski
Geographic regression models of various descriptions are often applied to
identify patterns and anomalies in the determinants of spatially distributed
observations. These types of analyses focus on answering why questions about
underlying spatial phenomena, e.g., why is crime higher in this locale, why do
children in one school district outperform those in another, etc.? Answers to
these questions require explanations of the model structure, the choice of
parameters, and contextualization of the findings with respect to their
geographic context. This is particularly true for local forms of regression
models which are focused on the role of locational context in determining human
behavior. In this paper, we present GeoExplainer, a visual analytics framework
designed to support analysts in creating explanative documentation that
summarizes and contextualizes their spatial analyses. As analysts create their
spatial models, our framework flags potential issues with model parameter
selections, utilizes template-based text generation to summarize model outputs,
and links with external knowledge repositories to provide annotations that help
to explain the model results. As analysts explore the model results, all
visualizations and annotations can be captured in an interactive report
generation widget. We demonstrate our framework using a case study modeling the
determinants of voting in the 2016 US Presidential Election.