# GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation

arXiv: https://arxiv.org/abs/2308.13588 · HTML: https://mlsanity.com/paper/2308.13588/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 75 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [RuleMatrix: Visualizing and Understanding Classifiers with Rules](https://mlsanity.com/paper/1807.06228/) (2018)
- [Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective](https://mlsanity.com/paper/1702.01226/) (2017)
- [A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations](https://mlsanity.com/paper/1705.01968/) (2017)
- [DataSite: Proactive Visual Data Exploration with Computation of Insight-based Recommendations](https://mlsanity.com/paper/1802.08621/) (2018)
- [Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality](https://mlsanity.com/paper/2009.02649/) (2020)

## Built on by (most cited first, 1 of 1)

- [MapStory: Prototyping Editable Map Animations with LLM Agents](https://mlsanity.com/paper/2505.21966/) (2025)

## Abstract (arXiv)

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.