Predictive Site Location
Predictive site location modeling uses machine learning algorithms (particularly maximum entropy models) to predict the probability of archaeological site occurrence across a landscape based on environmental and spatial variables. Developed for ecology but adapted for archaeology, predictive modeling identifies areas with high archaeological potential, guiding survey strategies and resource management.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259. · DOI 10.1016/j.ecolmodel.2005.03.026
- Verhagen, P., & Whitley, T. W. (2012). Predictive modelling for archaeological resource management. Journal of Archaeological Science, 39(5), 1066-1077. · URL
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.