Center for Spatial Studies

While a model prediction is a probabilistic claim about a system state to transpire in the future, a model projection is an if-then statement about the potential future of a system, by definition subject to (changes in) boundary conditions with an unknown likelihood. Despite a robust body of literature on the various potential purposes of models - and to predict is only one of these purposes - some modellers tend to refer to all their model outputs as predictions, while they are more often projections or neither of these two. Both geosimulation and spatial machine learning scholars are careless in how they refer to their model outputs. This is confusing for all involved and especially for the general public, for whom the model output is usually the only model component they get to see. In this paper we provide definitions, justifications, and a decision tree for classifying model outputs. This can help the GIScience community to gain clarity about what their model output entails.