Deletion/Substitution/Addition (DSA) model selection algorithm applied to the study of archaeological settlement patterning

Fernandes, Ricardo; Geeven, Geert; Soetens, Steven; Klontza-Jaklová, Věra

doi:10.1016/j.jas.2011.03.035

Cited by 21 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results presented in this paper represent an extension of previous work (Fernandes et al 2011) that was strictly focused on the understanding of past location choices for Minoan populations during the Protopalatial period (1900-1650 BC) in central-eastern Crete. That research was able to establish a model with an excellent statistical fit and a sound archaeological interpretation.…”

Section: Conclusion and Further Workmentioning

confidence: 64%

Landscape Archaeology between Art and Science : From a Multi- to an Interdisciplinary Approach

Kluiving¹,

Guttmann-Bond²

2012

View full text Add to dashboard Cite

The publication of this book is made possible by grants from: All rights reserved. Without limiting the rights under copyright reserved above, no part of this book may be reproduced, stored in or introduced into a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording or otherwise) without the written permission of both the copyright owner and the author of the book.Every effort had been made to obtain permission to use all copyrighted illustrations reproduced in this book.Nonetheless, whosoever believes to have rights to this material is advised to contact the publisher. PrefaceSince the 1960s, 'landscape' has been a key topic of archaeological research all over the world. Initially drawing on environmental archaeology, and using models from the earth sciences as well as cultural ecology, landscapes have been conceptualised predominantly as the natural environments determining human behaviour or as a backdrop to human action. In the New Archaeology of the 1960s, ecology and settlement patterns were studied together with anthropology, with the aim of piecing together information on past economic and social systems (Trigger 1989, 295). Lewis Binford argued that the goal of archaeology should be to understand the range of human behaviours and the differences in culture, based on a belief that cultures were adaptive responses to our environment (Binford 1962), and archaeologists at the time were optimistic that culture and culture change were rational and could be predicted based on archaeological assemblages and settlement patterns.In This volume will begin with a discussion of Landscape Archaeology and its history, followed by a discussion of themes and summaries of the papers in this volume. We end with some concluding remarks 10 · landscape archaeology between art and science and suggestions for future research. Although the editors have put a lot of energy into editing the text of this volume, please note that many contributors are writing in English as a second language. We have endeavoured to smooth out the English, but we apologise for the awkwardness of some phrasing in the text.All 35 papers of the LAC2010 proceedings have been peer-reviewed by at least two reviewers. The editors have approached a large number of peer reviewers within the participating institution within the VU University, the Cultural Heritage Agency and the international Advisory Board of LAC2010. We wish to thank the following colleagues who have made the realisation of the proceedings of LAC2010 possible.

show abstract

Section: Conclusion and Further Workmentioning

confidence: 64%

Landscape Archaeology between Art and Science : From a Multi- to an Interdisciplinary Approach

Kluiving¹,

Guttmann-Bond²

2012

View full text Add to dashboard Cite

show abstract

“…The logistic regression analysis was mostly used to delineate the probability of predicting the location of an archaeological site by a binary dependent variable (0: nonoccurrence; 1: occurrence) and independent variables that affect event occurrence [42][43][44]. In this research, the incident occurrence of an archaeological site being located in a landscape (marked as {y = 1}) and independent variables Xj related to defensive factors are expressed as below: (2) (3) where variable β0 and regression coefficients βj reflect the impact of independent variables Xj on an incident of P(y = 1).…”

Section: Logistic Regression Analysismentioning

confidence: 99%

GIS-Based Visibility Network and Defensibility Model to Reconstruct Defensive System of the Han Dynasty in Central Xinjiang, China

Zhu

Nie

Gao

et al. 2017

IJGI

View full text Add to dashboard Cite

Abstract:The Silk Road opened during the Han Dynasty, and is significant in global cultural communication. Along this route in the central part of Xinjiang, the archaeological sites with defensive characteristics once provided a safeguard for this area. Reconstructing the defensive system is an important way to explore the ancient culture's propagation and the organizational structure of these sites. In this research, the compound visibility network with complex network analysis (CNA) and the least-cost paths based on the defensibility models from linear and logistic regression methods constitute the principle defensive structure. As possible transportation corridors, these paths are considered to be mostly fitted to each other in general, and are different from normal slope-based paths. The sites Kuhne Shahr and Agra play important roles for information control according to the CNA measures, while the sites Kuhne Shahr and Kuyux Shahr are considered to be crucial cities due to their positions and structural shapes. Some other sites, including Uzgen Bulak, Shah Kalandar, Chuck Castle, Caladar, and Qiuci, as well as some beacons, have important effects on defending the transportation corridors. This method is proven efficient for the study of the historical role of archaeological sites with defensive characteristics.

show abstract

“…Deductive approach, which are relatively rare, constructs the predictive model based on prior anthropological and archaeological knowledge, and uses previously identified sites to evaluate the model (Kamermans, 2006). Numerous predictive models have been developed using different methods including Bayesian statistics and Dempster-Shafer modelling to detect archaeological sites (Verhagen et al, 2010;Kvamme, 1990;Lang, 2000;Gibbon, 2000;Konnie et al 2000;Fernandes et al 2011) and in developing these approaches Kamermans (2010) has identified a range of problems concerned with quality and quantity of archaeological input data including relevance of the environmental input data, lack of temporal and/or spatial resolution, use of spatial statistics, testing of predictive models, and need to incorporate social and cultural input data. A number of recommendations to address these problems have been developed as archaeological experience with quantitative GIS has emerged (Verhagen et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Application of pattern recognition in detection of buried archaeological sites based on analysing environmental variables, Khorramabad Plain, West Iran

Sharafi

Simpson

Barceló

2016

Journal of Archaeological Science: Reports

View full text Add to dashboard Cite

Archaeologists continue to search for techniques that enable them to analyze archaeological data efficiently with Artificial Intelligence approaches increasingly employed to create new knowledge from archaeological data. The purpose of this paper is to investigate the application of Pattern Recognition methods in detection of buried archaeological sites of the semi-arid Khorramabad Plain located in west Iran. This environment has provided suitable conditions for human habitation for over 40,000 years. However, environmental changes in the late Pleistocene and Holocene have caused erosion and sedimentation resulting in burial of some archaeological sites making archaeological landscape reconstructions more challenging. In this paper, the environmental variables that have influenced formation of archaeological sites of the Khorramabad Plain are identified through the appliation of Arc GIS. These variables are utilized to create an accurate predictive model based on the application of One-Class classification Pattern Recognition techniques. These techniques can be built using data from one class only, when the data from other classes are difficult to obtain, and are highly suitable in this context. The experimental results of this paper confirm one-class classifiers, including Auto-encoder Neural Network, Kmeans, Principal Component Analysis Data Descriptor, Minimum Spanning Tree Data Descriptor, K-Nearest neighbor and Gaussian distribution as promising applications in creating an effective model for detecting buried archaeological sites. Among the investigated classifiers, Minimum Spanning Tree Data Descriptor achieved the best performance on the Khorramabad Plain data set.

show abstract

Deletion/Substitution/Addition (DSA) model selection algorithm applied to the study of archaeological settlement patterning

Cited by 21 publications

References 28 publications

Landscape Archaeology between Art and Science : From a Multi- to an Interdisciplinary Approach

Landscape Archaeology between Art and Science : From a Multi- to an Interdisciplinary Approach

GIS-Based Visibility Network and Defensibility Model to Reconstruct Defensive System of the Han Dynasty in Central Xinjiang, China

Application of pattern recognition in detection of buried archaeological sites based on analysing environmental variables, Khorramabad Plain, West Iran

Contact Info

Product

Resources

About