Prediction of the Possibility of Prehistoric Archaeological Sites from Calcolithic to Iron Age using Logistic Regression Model in GIS, Case study: Harsin_Bisotun Plain

Document Type : علمی - پژوهشی

Authors

1 Graduated Master of Archaeology, Shahidbeheshti University of Tehran

2 Assistant Professor, Department of Archaeology, Shahid Beheshti University of Tehran

3 Assistant Professor, Department of Remote Sensing and GIS, Tarbiat Modarres University of Tehran

Abstract

Data collection and recording of Archaeological sites in Archaeological research is costly and requires a lot of manpower and time. Accordingly, the use of methods that can predict the presence of ancient monuments without direct observation will play a significant role in saving time and cost of Archaeological surveys. The main issue of this research is to assess the ability of the logistic regression model to predict the dispersal of ancient sites in the Harsin-Bisotun plain. Predictor variables for this study include the environmental variables of slope, height, distance to river, vegetation, distance to modern cities, density of modern villages and distance to main roads, and dependent variable  is the most turbulent  of area in terms of existence prehistoric Archaeological sites. For modeling, using logistic regression, GIS and IDRISI softwares was used. By analyzing the results of the logistic regression model, results showed that, the logistic regression model was successful in prediction the dispersion of ancient sites in the Harsin-Bisotun plain. As well, the introduction of densly populated areas due to the presence of ancient sites  as a modle-dependent variable in the plain-bound areas is more important than the mere introduction of GPS points of the ancient sites as a dependent variable. and, Accordingly, the cultural variability of village density in the Calcolithic Age, village density, distance to cites and the distance to main roads in the Bronze Age and distance to cites, distance to main roads in Iron Age have had the greatest impact in prediction the dispersion of ancient sites.

Keywords

References

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