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

Area of Science:

• Geographic Information Science
• Spatial Analysis
• Visual Analytics

Background:

• Geographic regression models identify patterns in spatial data but require clear explanations.
• Understanding the 'why' behind spatial phenomena necessitates detailing model structures, parameters, and geographic context.
• Local regression models emphasize the role of location in human behavior, demanding robust contextualization.

Purpose of the Study:

• Introduce GeoExplainer, a visual analytics framework for creating explanative documentation of spatial analyses.
• Support analysts in summarizing and contextualizing findings from geographic regression models.
• Enhance the interpretability and reproducibility of spatial analysis research.

Main Methods:

• GeoExplainer flags potential issues in model parameter selection.
• Utilizes template-based text generation for summarizing spatial model outputs.
• Integrates with external knowledge repositories for result annotation and explanation.
• Features an interactive report generation widget for capturing visualizations and annotations.

Main Results:

• The framework assists analysts in documenting spatial models effectively.
• Provides automated summaries and contextual annotations for model outputs.
• Facilitates the creation of interactive reports detailing spatial analysis findings.
• Demonstrated utility in a case study of 2016 US Presidential Election voting determinants.

Conclusions:

• GeoExplainer enhances the process of explaining complex spatial analyses.
• Improves the clarity and accessibility of findings from geographic regression models.
• Empowers researchers to create comprehensive and contextualized documentation for spatial studies.