2011
DOI: 10.1080/17538947.2011.576778
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Geospatial predictive modelling of the Neolithic archaeological sites of Magnesia in Greece

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Cited by 5 publications
(4 citation statements)
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“…Currently, APM represents a powerful tool for preventive archaeology [4], cultural heritage management [5,6] and improving national-scale archaeological inventories [7,8]. APMs have been used successfully in different geographical areas of the globe including Africa [9,10], Europe [1,[11][12][13], Asia [14,15] and America [16][17][18]. Different intuitive (qualitative) and quantitative statistical methods have been used to identify the locations of archaeological settlements.…”
Section: Introductionmentioning
confidence: 99%
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“…Currently, APM represents a powerful tool for preventive archaeology [4], cultural heritage management [5,6] and improving national-scale archaeological inventories [7,8]. APMs have been used successfully in different geographical areas of the globe including Africa [9,10], Europe [1,[11][12][13], Asia [14,15] and America [16][17][18]. Different intuitive (qualitative) and quantitative statistical methods have been used to identify the locations of archaeological settlements.…”
Section: Introductionmentioning
confidence: 99%
“…The most commonly tested era is the Neolithic, likely due to the pivotal nature of this period, and the fact that landforms were like modern features. Different areas (e.g., Italy [4], Greece [12], Scotland [27]) have distinct landscape features which may have been decisive for prehistoric people when choosing where to place a settlement. For example, in Italy [4], the favoured features were alluvial plains, terraces, caves (lithic industry), and low slope areas, while in Greece [12], the area around Thessaly represented an important connection between the islands of the Aegean Sea with the northern and southern parts of the country, being at the same time characterised by permanent inhabitation.…”
Section: Introductionmentioning
confidence: 99%
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“…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%