Assessing Surface Correspondence and Trade of Maya Figurines and Moulds Using Multi‐Stripe Laser Technology and Metrology*

One of the most intriguing questions for archaeologists studying clay artefacts concerns technologies employed in their manufacture and whether standardization can be demonstrated through traditional ceramic analysis. Visual comparisons and mechanical measurements have been used to determine correlations and infer standardization. Using a laser multi‐line scanner and software developed to quantify Hausdorff distances between vertices in 3D surfaces, we analysed sample collections of figurines and moulds from Belize, Mexico and Honduras. The results indicated that this procedure was a more precise indicator of common source and standardization in moulded clay artefacts. This method provided data relevant to assessing the scope of ancient trade networks and the nature of social and economic relationships that existed among the ancient inhabitants of Mesoamerica.     

A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial‐Filter‐Based Techniques

Geographically weighted regression (GWR) is a technique that explores spatial nonstationarity in data‐generating processes by allowing regression coefficients to vary spatially. It is a widely applied technique across domains because it is intuitive and conforms to the well‐understood framework of regression. An alternative method to GWR that has been suggested is spatial filtering, which it has been argued provides a superior alternative to GWR by producing spatially varying regression coefficients that are not correlated with each other and which display less spatial autocorrelation. It is, therefore, worthwhile to examine these claims by comparing the output from both methods. We do this by using simulated data that represent two sets of spatially varying processes and examining how well both techniques replicate the known local parameter values. The article finds no support that spatial filtering produces local parameter estimates with superior properties. The results indicate that the original spatial filtering specification is prone to overfitting and is generally inferior to GWR, while an alternative specification that minimizes the mean square error (MSE) of coefficient estimates produces results that are similar to GWR. However, since we generally do not know the true coefficients, the MSE minimizing specification is impractical for applied research.