Case Studies in Archaeological Predictive Modelling

Abstract

In 2006 Leiden University has initiated a series Leiden Dissertations at Leiden University Press. This series affords an opportunity to those who have recently obtained their doctorate to publish the results of their doctoral research so as to ensure a wide distribution among colleagues and the interested public. The dissertations will become available both in printed and in digital versions. Books from this LUP series can be ordered through www.lup.nl. The large majority of Leiden dissertations from 2005 onwards is available digitally on www.dissertation.leidenuniv.nl.In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing predictive models using both old and new archaeological data. Furthermore, an attempt is made to reconcile the worlds of expert judgment and quantitative analysis by means of multicriteria decision making techniques and Bayesian statistics. The thesis also offers some alternative approaches to predictive modelling, like using prehistoric land use reconstructions, and the integrating of social and cultural factors into the models. It also gives an up to date review of the international and Dutch state of affairs in archaeological predictive modeling. Philip Verhagen graduated in Physical Geography at the Vrije Universteit Amsterdam in 1989. Since 1992, he has worked at RAAP Archeologisch Adviesbureau as a specialist in Geographical Information Systems, and has specialized in archaeological predictive modelling and the application of statistics in archaeological prospection. As such, he has worked on various archaeological projects in and outside the Netherlands, doing geographical analysis and quantitative research. Since October 2005, he is employed at the Hendrik Brunsting Stichting of the Archeologisch Centrum of the Vrije Universiteit Amsterdam as a specialist in archaeological computing.

GIS and Remote-Sensing Application in Archaeological Site Mapping in the Awsard Area (Morocco)

“…The cultural variables such as distance to the main settlement and/or ancient road proximity were used based on their availability for better accuracy of the model. However, as highlighted by Verhagen et al [33] "cultural variables are rarely included in the models, thus resulting in predictions based on physical properties of the current landscape". This may be explained by the fact that social and cognitive factors seem to be difficult to model as they are regarded as being too abstract and intangible for use in a predictive mode.…”

The Dynamics of Land Use/Cover and the Statistical Assessment of Cropland Change Drivers in the Kabul River Basin, Afghanistan

“…Within the regression modeling, there are two particular approaches in assessing the spatial dependency-firstly, autoregressive structure (consisting of many models) and, secondly, spatial sampling of plots [24]. In spatial samplings, logistic regression modeling is one of the most utilized approaches in recent years, particularly when predicting land uses [13,16,17].…”

“…Under the full market competition condition, the conversion of a land use from one type to another will continue until the marginal benefit (MB) and marginal cost (MC) curves of land use change intersect each other. To trace the contribution of different socio-economic and biophysical factors in land use change (i.e., from one value to another), researchers use different models, such as regression, and machine learning algorithms, such as support vector machines (SVM) [13][14][15][16][17]. LULC change models deal with land issues that range from simple system representations to simulation systems based on a deep understanding of situation-specific problems with consideration of a large number of drivers at different spatio-temporal scales [18][19][20].…”

Predictive modeling for preventive Archaeology: overview and case study

“…These are: (i) land use, (ii) elevation and (iii) the proximity to the water bodies [16]. Other parameters, such as environmental and social elements, are less frequently employed [17], [18], [19], as: 1) the sun exposure ( that is the average of solar radiation hours received by a site in one day or in a season or during the year); 2) viewshed analysis and viewshed indexes, able to take in count the presence of vegetation and of sediments that increase the visible surface; 3) the distance from different elements such as the coast or reliefs; 4) euclidean distance or cost distance between sites. Another important aspect in predictive models is the choice of the method used to combine including and excluding factors.…”