SpICE: an interpretable method for spatial data

Statistical learning methods are widely utilised in tackling complex problems due to their flexibility, good predictive performance and ability to capture complex relationships among variables. Additionally, recently developed automatic workflows have provided a standardised approach for implementing statistical learning methods across various applications. However, these tools highlight one of the main drawbacks of statistical learning: the lack of interpretability of the results. In the past few years, a large amount of research has been focused on methods for interpreting black box models. Having interpretable statistical learning methods is necessary for obtaining a deeper understanding of these models. Specifically in problems in which spatial information is relevant, combining interpretable methods with spatial data can help to provide a better understanding of the problem and an improved interpretation of the results. This paper is focused on the individual conditional expectation plot (ICE-plot), a model-agnostic method for interpreting statistical learning models and combining them with spatial information. An ICE-plot extension is proposed in which spatial information is used as a restriction to define spatial ICE (SpICE) curves. Spatial ICE curves are estimated using real data in the context of an economic problem concerning property valuation in Montevideo, Uruguay. Understanding the key factors that influence property valuation is essential for decision-making, and spatial data play a relevant role in this regard.