A Framework for Explainable Artificial Intelligence in Geospatial Contexts

Artificial intelligence has become a key driver of innovation in geospatial data analysis. It enables new prediction, classification and decision-making methods in areas such as urban planning, environmental monitoring and mobility services. However, many artificial intelligence models, particularly complex machine learning algorithms, lack transparency. This makes it difficult for users to understand, validate or trust the outcomes of these models. This is particularly problematic in geospatial contexts, where decisions often have real-world implications and depend on local knowledge and geospatial reasoning. While existing explainable artificial intelligence methods offer insights into model behavior, they often disregard the geospatial context, such as the location of phenomena and the relationships between nearby areas. This limits their effectiveness in understanding and interpreting the geospatial behavior of the model. This dissertation addresses this challenge by advancing the field of geospatial explainable artificial intelligence, an emerging area that redefines how explanations relating to geospatial data should be constructed and communicated. It proposes a novel framework that works with a new type of explanation which can be tailored to the geospatial context. Glocal explanations are computed through the aggregation of local explanations and defined conditions. This enables explanations to be created that range between local and global explainable artificial intelligence. As for explanations, the model quality is adapted to these glocal units, enabling analysis of where and when the model performs well or poorly. The framework also provides interactive visualizations that enable users to explore, interpret, and question model outputs. One visualization is an interactive matrix, while a second uses multi-level glyphs to enable geospatial analysis of the explanations. Both visualizations integrate model quality to reliably interpret the reasoning behind the model's decisions. The novel contributions are supported by real-world experiments and prototype implementations. Three distinct use cases are considered: predicting rental bike station bookings, predicting car park occupancy, and classifying accident severity. An additional evaluation of the usability of the visualizations shows that representations such as the developed matrix or geovisualization significantly improve efficiency, effectiveness, and satisfaction. The final framework can be used by different types of users. Machine learning experts can use it to identify and understand where and why the model performs poorly. Domain experts can use the framework to gain a better understanding of the actions and causes within a use case, potentially enabling them to take actions that could have economic and social benefits.