Integration of GIS and Predictive Modeling for Archaeological Site Prospection

EasyChair Preprint 14265

Abstract

The integration of Geographic Information Systems (GIS) and predictive modeling represents a significant advancement in the field of archaeological site prospection. This approach leverages spatial analysis and statistical techniques to predict the likelihood of archaeological site locations based on environmental, cultural, and historical factors. By synthesizing data from diverse sources, such as satellite imagery, topographic maps, and historical records, GIS allows for the creation of detailed spatial databases. These databases can be used to identify patterns and correlations that inform predictive models.

Predictive modeling, using algorithms such as logistic regression, machine learning, and spatial statistics, enables archaeologists to forecast potential site locations with a higher degree of accuracy. These models consider various factors, including elevation, proximity to water sources, soil types, and historical land use. The integration of these methodologies not only enhances the efficiency of field surveys by prioritizing areas of high probability but also reduces the time and resources needed for archaeological exploration.

This integrated approach has been successfully applied in various geographical contexts, demonstrating its versatility and effectiveness. The results contribute to the preservation and understanding of cultural heritage, enabling more informed decision-making in heritage management and conservation. As technology and methodologies continue to evolve, the fusion of GIS and predictive modeling will undoubtedly play a crucial role in the future of archaeological research and site preservation.

This abstract outlines the significant benefits and applications of integrating GIS and predictive modeling for archaeological site prospection, emphasizing its potential to transform the field through improved efficiency and accuracy.

Keyphrases: Archaeological Site Prospection, GIS, Integration of GIS, predictive modeling

@booklet{EasyChair:14265,
  author    = {Ralph Shad and Axel Egon and Kaledio Potter},
  title     = {Integration of GIS and Predictive Modeling for Archaeological Site Prospection},
  howpublished = {EasyChair Preprint 14265},
  year      = {EasyChair, 2024}}