A predictive model of archaeological potential: An example from northwestern Belize

Abstract

Binary Logistic Regression is used to identify areas of high archaeological potential in a portion of northwestern Belize. The predictive modeling process involves remotely sensed imagery, Geographic Information System (GIS) data and techniques, and multivariate statistical approaches. Predictive variables represent both the pre-historic current landscape of the ancient Maya and the present day physical landscape. An optimal predictive model obtained using logistic regression includes one variable derived from a Landsat image representing contemporary vegetation patterns associated with Maya settlement and two variables derived from a digital elevation model (DEM) and an analog hydrography map representing resource endowments relevant to the ancient Maya. The predictive model identifies several areas of high archaeological probability as well as areas that are unlikely to contain any archaeological remains. Results can be used to inform future field surveys in a more cost efficient manner. Prior research has utilized remote sensing and GIS approaches for Maya site identification in the southern lowlands region of the Mexican Yucatan peninsula and the northern lowlands of Peten, Guatemala. This research represents the first landscape archaeological approach using satellite imagery for the Maya region in northwestern Belize.