Spatial predictive modeling of prehistoric sites in the Bohemian-Moravian Highlands based on graph similarity analysis

Abstract: This paper presented a new method for identifying promising areas for archaeological research. The method is based on graph analysis that iteratively compares and manipulates Hamming distances between graphs of input geographical parameters and graphs of human activity data in various historical periods. The weights learned from the comparison was used to build a prediction model to estimate the potential presence of an archaeological site of a certain time period in a given cadaster. This was applied in the Bohemian Moravian Highlands region based on the most complete archaeological dataset of the area. Resulting maps were analyzed from the archaeological and historical point of view to test against the existing knowledge of prehistoric population movement in the region. Overall, the method proved to overcome problems such as fragmentary inputs and is a good candidate for application in smaller and geographically diverse research areas. The aim of this work was to contribute to the methodology of the prediction of historical human activity, to facilitate greater comprehension of past local settlement dynamics, and to possibly ease the protection of cultural heritage.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Spatial predictive modeling of prehistoric sites in the Bohemian-Moravian Highlands based on graph similarity analysis ; volume:10 ; number:1 ; year:2018 ; pages:261-274 ; extent:14 ; Open Geosciences ; 10, Heft 1 (2018), 261-274 (gesamt 14)

Beteiligte, Orts- und Zeitangaben

• Urheber: Mertel, Adam ; Ondrejka, Peter ; Šabatová, Klára

Weitere Informationen

• DOI: 10.1515/geo-2018-0020
• URN: urn:nbn:de:101:1-2501051607246.639778772308
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:26 MESZ