Evaluation of spatial interpolation methods for socioeconomic variables

Abstract

In Cartography, interpolation methods are used to identify the spatial arrangement of a variable by estimating unknown values based on sample points. Although they are increasingly used in social and human sciences, in general, in these fields of knowledge, there is no concern regarding the use of the ideal interpolator. Considering this research question, this paper presents an evaluation of the three most used methods for the spatial interpolation of socioeconomic variables: Inverse Distance Weighting (IDW), Thin Plate Spline (TPS) and Kriging. The performances of the three methods for interpolating a real socioeconomic variable (average land prices per square meter in a Brazilian city) are compared to the respective results for a physical variable (hypsometry by quoted points). The evaluation of the interpolators was performed based on visual analysis of the spatialization, and with cross-validation statistics using two error metrics (Mean Absolute Error and Root Mean Squared Error). In general, the results indicate that the socioeconomic variable was interpolated more efficiently by the Kriging method, while the IDW and Spline interpolators performed better for the physical variable.