Visualization of explainable artificial intelligence for GeoAI
Cédric Roussel
Abstract
Shapley additive explanations are a widely used technique for explaining machine learning models. They can be applied to basically any type of model and provide both global and local explanations. While there are different plots available to visualize Shapley values, there is a lack of suitable visualization for geospatial use cases, resulting in the loss of the geospatial context in traditional plots. This study presents a concept for visualizing Shapley values in geospatial use cases and demonstrate its feasibility through an exemplary use case—predicting bike activity in a rental bike system. The visualizations show that visualizing Shapley values on geographic maps can provide valuable insights that are not visible in traditional plots for Shapley additive explanations. Geovisualizations are recommended for explaining machine learning models in geospatial applications or for extracting knowledge about real-world applications. Suitable visualizations for the considered use case are a proportional symbol map and a mapping of computed Voronoi values to the street network.