Predictive modeling for archaeological site locations: Comparing logistic regression and maximal entropy in north Israel and north-east China

Ido Wachtel*, Royi Zidon, Shimon Garti, Gideon Shelach-Lavi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Archaeological predictive modeling is a tool that helps assess the likelihood of archaeological sites being present at different locations in the landscape. Such models are used for research purposes, as an analytical tool to better explain settlement patterns and past human behavior. They are also an important tool for the preservation of archaeological sites, as they can help planners avoid areas where sites are likely to exist. In this study we compare two methods of predictive modeling for archaeological site locations using two independent case studies. The more commonly used method of logistic regression is compared with a newer method of maximal entropy (MaxEnt). We examine the effectiveness of both models on two independent datasets collected from the Upper Galilee (northern Israel) and the Fuxin area (northeast China). While both methods have proven useful, in both cases the MaxEnt models produced much better results, which were much more efficient, than those of the logistic regression.

Original languageAmerican English
Pages (from-to)28-36
Number of pages9
JournalJournal of Archaeological Science
Volume92
DOIs
StatePublished - Apr 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Ltd

Keywords

  • Logistic regression
  • Maximal entropy (MaxEnt)
  • Northeast China
  • Northern Israel
  • Predictive modeling

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