  This paper introduces GeoShapley, a game theory approach to measuring spatial
effects in machine learning models. GeoShapley extends the Nobel Prize-winning
Shapley value framework in game theory by conceptualizing location as a player
in a model prediction game, which enables the quantification of the importance
of location and the synergies between location and other features in a model.
GeoShapley is a model-agnostic approach and can be applied to statistical or
black-box machine learning models in various structures. The interpretation of
GeoShapley is directly linked with spatially varying coefficient models for
explaining spatial effects and additive models for explaining non-spatial
effects. Using simulated data, GeoShapley values are validated against known
data-generating processes and are used for cross-comparison of seven
statistical and machine learning models. An empirical example of house price
modeling is used to illustrate GeoShapley's utility and interpretation with
real world data. The method is available as an open-source Python package named
geoshapley.
