Reproducible visualization strategies for spatially varying coefficient (SVC) models: Incorporating uncertainty and assessing replicability

Spatially varying coefficient (SVC) models are often regarded as “big models” due to the extensive volume of outputs that they produce. This can make it challenging to identify important trends, and maps are typically used when interpreting results from these models. However, visualization best practices are often overlooked, and uncertainty is not incorporated, leading to misinterpretation and complicating pattern extraction and comparison. This has important implications for reproducibility and replicability (R&R) in SVC models, which has received limited attention in the literature. Addressing these gaps requires a structured approach that enhances interpretability, facilitates model comparison, and effectively incorporates model uncertainty when analyzing SVC model output. This study introduces svc-viz, an open-source Python tool that codifies best practices into a standardized framework for interpreting and communicating SVC model results. By integrating established visualization principles, svc-viz improves clarity and reduces the risk of misinterpretation. Additionally, svc-viz introduces strategies for visualizing model uncertainty and assessing replicability across datasets, methods, and model inputs. The utility of the tool is demonstrated using a 2020 U.S. presidential election dataset. By formalizing visualization strategies, this study advances reproducibility, replicability, and uncertainty consideration in multiscale local modeling, providing researchers with a more robust framework for analyzing and communicating spatial relationships.