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GIS-Based and Statistical Approaches in Archaeological Predictive Modelling (NE Romania)

Author

Listed:
  • Ionut Cristi Nicu

    (High North Department, Norwegian Institute for Cultural Heritage Research (NIKU), Fram Centre, N-9296 Tromsø, Norway)

  • Alin Mihu-Pintilie

    (Institute for Interdisciplinary Research, Science Research Department, “Alexandru Ioan Cuza” University of Iaşi (UAIC), St. Lascăr Catargi 54, 700107 Iaşi, Romania)

  • James Williamson

    (Department of Archaeology, Faculty of Humanities and Social Sciences, Memorial University of Newfoundland, P.O. Box 4200, St. John’s, NL A1C 5S7, Canada)

Abstract

Archaeological predictive modelling (APM) is an important method for archaeological research and cultural heritage management. This study tests the viability of a new statistical method for APM. Frequency ratio (FR) is widely used in the field of geosciences but has not been applied in APM. This study tests FR in a catchment from the north-eastern part of Romania to predict the possible location(s) of Eneolithic sites. In order to do that, three factors were used: soils, heat load index and slope position classification. Eighty percent of the sites were used to build the model, while the remaining 20% were used to externally test the model’s performance. The final APM was made with the help of GIS software and classified into four susceptibility classes: very high, high, medium and low. The success rate curve and the prediction rate curve reported values of the area under curve (AUC) of 0.72, and 0.75 respectively. The Kvamme’s Gain value for the model has a value of 0.56. Therefore, the final APM is reliable, so FR is a viable technique for APM. The final map can be successfully used in archaeological research, cultural heritage management and protection, preventive archaeology and sustainable development.

Suggested Citation

  • Ionut Cristi Nicu & Alin Mihu-Pintilie & James Williamson, 2019. "GIS-Based and Statistical Approaches in Archaeological Predictive Modelling (NE Romania)," Sustainability, MDPI, vol. 11(21), pages 1-13, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:5969-:d:280732
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    References listed on IDEAS

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    1. Omid Rahmati & Ali Haghizadeh & Hamid Reza Pourghasemi & Farhad Noormohamadi, 2016. "Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 1231-1258, June.
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    Cited by:

    1. Dimitris Goussios & Ioannis Faraslis, 2022. "Integrated Remote Sensing and 3D GIS Methodology to Strengthen Public Participation and Identify Cultural Resources," Land, MDPI, vol. 11(10), pages 1-16, September.

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