Abstract
Location modeling, both inductive and deductive, is widely used in archaeology to predict or investigate the spatial distribution of sites. The commonality among these approaches is their consideration of only spatial effects of the first order (i.e., the interaction of the locations with the site characteristics). Second-order effects (i.e., the interaction of locations with each other) are rarely considered. We introduce a deductive approach to investigating such second-order effects using linguistic hypotheses about settling behavior in the Final Palaeolithic. A Poisson process was used to simulate a point distribution using expert knowledge of two distinct hunter–gatherer groups, namely, reindeer hunters and elk hunters. The modeled points and point densities were compared with the actual finds. The G-, F-, and K-function, which allow for the identification of second-order effects of varying intensity for different periods, were applied. The results reveal differences between the two investigated groups, with the reindeer hunters showing location-related interaction patterns, indicating a spatial memory of the preferred locations over an extended period of time. Overall, this paper shows that second-order effects occur in the geographical modeling of archaeological finds and should be taken into account by using approaches such as the one presented in this paper.
Highlights
The prediction of archaeological sites using predictive modeling is an important tool that facilitates planning for cultural resource management and the creation and testing of models of human locational behavior [1] (p. 1)
The empty-space function F(r) displays the probability of detecting a point within the radius r of an arbitrary reference location [45] (p. 262). This function does not distinguish between the actual locations and a completely random point pattern
As with the non-clustered distributed points of the elk hunters, the spreads of the points according to the fuzzified hypotheses for reindeer hunters indicate a regular point distribution
Summary
The prediction of archaeological sites using predictive modeling is an important tool that facilitates planning for cultural resource management and the creation and testing of models of human locational behavior [1] (p. 1). “Inductive” in this context refers to the use of a model that is based on the relationship between known archaeological sites and other attributes, as determined by machine learning methods, to predict locations of equal suitability. This widespread approach (as used in [3,4,5,6]) only describes first-order effects and is based on the premise that the sites are representative of the actual distribution. The authors in [8]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
