A predictive model of archaeological potential: An example from northwestern Belize

Vaughn, Sallie; Crawford, T. Hugh

doi:10.1016/j.apgeog.2009.01.001

Cited by 53 publications

(33 citation statements)

References 9 publications

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“…Although it is frequently assumed such models predict the probability of encountering undiscovered sites, there is a significant difference between describing known-site locations, predicting the locations of unrecorded archaeological resources on the basis of the knowledge gained by describing known-site locations, and the concept of archaeological potential. Despite being cognisant of the important, but often subtle, differences between the aforementioned concepts, some interpretations appear to ignore the differences, which results in poorly conceived interpretations of modelling results (e.g., Vaughn and Crawford, 2009;Graves, 2011). In particular, prediction with reference to probability and the notion of archaeological potential are troublesome and when conflated they obstruct the clear development of conclusions derived from studies involving archaeological prospection.…”

Section: Introduction: Description Prediction and Potentialmentioning

confidence: 92%

A locally-adaptive model of archaeological potential (LAMAP)

Carleton

Conolly

Ianonne

2012

Journal of Archaeological Science

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Section: Introduction: Description Prediction and Potentialmentioning

confidence: 92%

A locally-adaptive model of archaeological potential (LAMAP)

Carleton

Conolly

Ianonne

2012

Journal of Archaeological Science

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“…We will illustrate this problem with preliminary study based LOGIT -binary logistic regression model [57,58]. Although popular in archaeological sciences [11,15,59] LOGIT requires representative data and is very sensitive to outliers [20]. Figure 4A presents the spatial distribution of probability that a given cell contains traces of human activity during the Stone Age.…”

Section: Methodsmentioning

confidence: 99%

“…To date, at the interface of the spatial sciences and archaeology many methods that have been proposed to model the spatial patterns of past human behaviour have used the location of archaeological sites and natural or socioeconomic variables [8]. We can distinguish two analytical approaches: 1) predictive (or inductive) modelling used to quantitatively estimate the probability of encountering archaeological remains outside zones where they have already been discovered [9][10][11][12][13][14][15]; and 2) the explanatory (or deductive) approach where archaeological data and GIS techniques are used to test theoretical models based on expert knowledge rather than those that are learned from data [5,[16][17][18][19]. In the last 10 years much effort has been expended to eliminate the dichotomy between the above two approaches, and increase the use of models trained from the data to explain relationships between natural and other variables (if they exist) and archaeological processes [6,[20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Geo-spatial modelling with unbalanced data:modelling the spatial pattern of human activityduring the Stone Age

Jasiewicz

Sobkowiak‐Tabaka

2015

Open Geosciences

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Abstract:With the increasing availability of data, geoscience provides many methods to model the spatial extent of various phenomena. Acquiring representative, high quality data is the most important criterion to assess the value of any spatial analysis, however, there are many situations in which these criteria cannot be fulfilled. Archived data, collected in the past, for which analysis cannot be repeated or supplemented is a very common information source. Archaeological data collected at a regional extent during years of field work and superficial observations are an additional example. Such data rarely provide representative samples and are usually imbalanced; only very few examples contain useful data, while many examples remain without any archaeological traces. In spite of these limitations archaeological information presented in the form of maps can be a useful and helpful tool to analyse the spatial patterns of some phenomena and, from a more practical point of view, a tool to predict the location of undiscovered occurrences. The primary goal of this paper is to present a methodology for modelling spatial patterns based on imbalanced categorical data which do not fulfil the criteria of spatial representation and incorporates uncertainty in its decision process. This concept will be discussed using a collection of Stone Age sites and set of environmental variables from the postglacial lowlands in Western Poland. We will propose a machine-learning system which adopts CART through bootstrap simulation to incorporate uncertainty into the spatial model and utilise that uncertainty in the decision-making process. Finally, we will describe the relationships between the model and environmental variables and present our results in cartographic form using the principles of decision-tree cartography.

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“…For example, Verhagen and Drăguţ (2012) used Object Based Image Analysis of a DEM from lidar data (5m x 5m posting) to create a landscape geomorphometric classification model to use within an archaeological predictive model; Vaughn and Crawford (2009) used key environmental variables defined from contemporary remotely-sensed data (e.g. vegetation colour and topography/slope) to predictively model Maya settlement in northwest Belize, whilst Rua (2009) attempted to identify potential locations of rural Roman villae in Portugal using a range of topographic, hydrological and slope variables.…”

Section: Archaeological Predictive Modellingmentioning

confidence: 99%

Using geoarchaeological deposit modelling as a framework for archaeological evaluation and mitigation in alluvial environments

Carey

Howard

Jackson

et al. 2017

Journal of Archaeological Science: Reports

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The pace of anthropogenic development on floodplains and adjacent valley floors is still increasing and in many countries this is accompanied by a requirement for heritage mitigation and management.The result is an increased demand for effective and efficient archaeological evaluation and mitigation strategies, which can only be achieved in alluvial environments through the application of geoarchaeological methods. This paper uses lidar data combined with deep geophysical survey (electrical resistivity), gouge coring and limited borehole data to derive a three dimensional geoarchaeological deposit model, which provided a vehicle for archaeological evaluation and mitigation. Significantly, the results of this deposit model are compared to the results from the subsequent archaeological evaluation trenching, a methodological next step that has not received sufficient attention within the (geo)archaeological literature. The deposit model is refined using radiocarbon dating and artefactual evidence derived from the archaeological evaluation trenching.The results demonstrate how geoarchaeological deposit modelling can be integrated with archaeological evaluation trenching and provides discussion of the importance and difficulties of integrating geoarchaeological sediment units (archives) with archaeological contextual excavation data, with conventional stratigraphic matrices.

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A predictive model of archaeological potential: An example from northwestern Belize

Cited by 53 publications

References 9 publications

A locally-adaptive model of archaeological potential (LAMAP)

A locally-adaptive model of archaeological potential (LAMAP)

Geo-spatial modelling with unbalanced data:modelling the spatial pattern of human activityduring the Stone Age

Using geoarchaeological deposit modelling as a framework for archaeological evaluation and mitigation in alluvial environments

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