Forecasting statistical models of archaeological site location

Giuseppe Arbia, Giuseppe Espa

«Archeologia e Calcolatori» 1996, 7, 365-372

Abstract

Forecasting statistical models are becoming increasingly important in archaeological research. One of the reasons of this popularity is that archaeological sites tend to present themselves in particular environments so that forecasting models can help in identifying areas where the probability is higher based on previously collected statistical information. In the present paper we consider a class of statistical models designed to produce maps of the probability for archaeological site location (ASL) which incorporate both deductive and inductive considerations. In the discussion we criticise the use of the logistic regression for the production of ASL probability maps, a popular approach known in archaeological literature as the “integrated strategy”. The application of the method is statistically incorrect since in archaeological studies the hypothesis of independence between sites, which is at the basis of the logistic regression model, is violated. To overcome such limitations we propose two alternative models. The first one is an autopredictive model in which the probability of ASL is modelled as a function of the observations coming from field surveys in neighbouring zones. This approach accounts for the problem of non-independency of observations, but neglects a priori auxiliary information on the archaeological area. The second approach is a more comprehensive one which overcomes the problems of logistic regressions while preserving the role of a priori information.

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