skip to content

GIS and Spatial Analysis

GIS (Geographical Information Systems) are software packages that facilitate the creation, integration, manipulation, and analysis of different classes of spatial and geographic data. In archaeology, GIS has played an increasingly important part of data synthesis over the last 30 years, especially as the basis for mapping local sites, as well as regional and global datasets. Objects retrieved from archaeological excavations can be visualised in 2 or 3 (and occasionally 4) dimensions, and their patterning assessed via spatial analysis and computational statistics. Similarly, the distribution of sites or find densities across the landscape, produced by survey projects, can be compared with environmental data (e.g. topographic data derived from remote sensing or ground-survey techniques; aerial or satellite imagery), in order to describe settlement patterns or even to predict the location of new sites for cultural resource management purposes. GIS can also be used to model human movement by combining a series of assumptions about energy expenditure budgets within human mobility strategies with a variety of physical variables to infer the location or prehistoric routes, or even measure portions of landscapes that can be observed from a given location, enabling for example the possibility to evaluate hypotheses concerning the visual prominence of symbolic monuments.

There is a large number of free and open source GIS software packages; the most noteworthy ones are QGIS and GRASS. Google earth has also some basic GIS functionalities, and R statistical computing language (see also computational statistics) is also used for map making and for spatial statistics in general. An excellent textbook for GIS and Spatial Analysis in Archaeology are Wheatley and Gillings’ Spatial Technology in Archaeology and Conolly and Lake’s Geographic Informations Systems in Archaeology. Computational Approaches to Archaeological Space and Mathematics and Archaeology are both edited volumes with a large collection of articles with applied case studies as well critical reviews on spatial analysis and more in general computational and quantitative archaeology. Finally, the Computer Applications and Quantitative Methods in Archaeology (CAA) Conference have usually a large number of sessions on Spatial Analysis and GIS in Archaeology.

Remote Sensing

Remote Sensing (RS) in archaeology includes the set of techniques directed towards the acquisition of information about an object or feature of archaeological interest without establishing direct contact with it. RS data can be acquired from multiple platforms and at a variety of scales: satellite, aerial, terrestrial and ground-based RS (usually referred to as geophysics). These techniques include passive RS methods such as aerial photography, aerial and satellite multispectral and hyperspectral imagery but also active sensors such as LiDAR/Laser Scanner and RADAR/SAR.

The use of RS in archaeology permeates most geospatial applications. Digital elevation models and other types of large-scale environmental information, which are basic inputs for most GIS-based analyses, are usually derived from RS data. With the wider availability of automated digital photogrammetric techniques RS-based techniques are also becoming a standard in the recording and analysis of the excavation process and the material culture recovered during excavation and survey.

Comer, D.C. and Harrower, M.J. 2013. Mapping Archaeological Landscapes from Space. Springer.

Lasaponara, R. and Masini, N. 2012. Satellite remote Sensing. A new tool for Archaeology. Springer.

Lillesand, T., Kiefer, R.W. and Chipman, J. 2015. Remote sensing and Image Interpretation. Wiley.

Wiseman, J. and El-Baz, F. 2007. Remote Sensing in Archaeology. Springer.

Computer Simulations

Archaeologists routinely create models that seek to link the pattern they observe to a range of possible presumed processes. This endeavour is often limited by the complexity of human behaviour, its interaction within a wider ecological, social, and cultural context, and by the limiting amount of information we can retrieve from the archaeological record. Computer simulations are increasingly supporting archaeologists to overcome these issues creating a bridge between pattern and process, supporting the testing of various hypotheses but also as basis for building new theories. We are now able to create computational models to determine how long a given level of hunting pressure was sustainable, establish what form of social learning can generate an observed change in the decoration of pottery design, or even try to explain how and why past societies collapsed. Several members of CDAL have expertise in computer simulations, in particular in the use and development of agent-based models (ABM), a particular form of computational model that is designed for exploring the interaction between individuals and their environment.

Despite a long tradition of studies on agent-based models and more in general computer simulations in archaeology there are no entry-level textbook. Agent-based and Individual Modelling: a practical introduction by Grimm and Railsback is however an excellent manual for a broader audience which also offers a good introduction to NetLogo, a widely used platform for executing and developing ABMs. Several archaeological simulations coded in Netlogo, but also Python and R, can be found on openABM repository. The collection of papers on Agent-based Modeling and Simulation in Archaeology edited by Wurzer, Kowarik and Reschreiter, as well as Mark Lake’s review article, provide a good insight on the current themes of ABM and computer simulations in archaeology. The Computer Applications and Quantitative Methods in Archaeology (CAA) Conference hosts a couple of sessions on ABM and computer simulations in archaeology every year, and has also a special interest group.

Computational Statistics

There is a wide range of manual for introductory statistics for Archaeologists, from Stephen Shennan’s seminal Quantifying Archaeology to the more recent Van Pool and Leonard’s Quantitative Analysis in Archaeology. Michael Baxter’s Statistics in Archaeology present a range of intermediate applications in Archaeology, while Buck, Cavanagh, and Litton’s Bayesian Approach to Intrepreting Archaeological Data provides an excellent introduction to Bayesian statistic a growing paradigm in statistical inference. There is a wide range of books (e.g. Springer’s Use R! series) and online resources for learning R (including this excellent online tutorial). This webpage contains a comprehensive list of R programmes (known as packages) relevant to archaeology divided into different categories. The page includes also a bibliography of journal articles in archaeology where codes are provided.

Open Science

Computational and Digital archaeology generally involves the use of highly specialised, and sometimes even bespoke softwares. Archaeologists often rely on digital workflows that are becoming more complex and based on computer programs designed for different fields and applications. This means that without an access of the raw digital data (e.g. radiocarbon dates, isotope data, but also measurements of bones and tools), and without a precise description of the methods implemented it is near impossible to reproduce a piece of scientific work. There is an increasing awareness of this issue across many disciplines, and many archaeologists are now promoting the idea of the open science movement, aiming to make the dissemination of data, methods, and results as large and wide as possible. Members of CDAL are strongly committed to this agenda, by using open source and free softwares, and various repositories for data and workflows of their research.

Several online repositories hosts freely accessible archaeological data, the primary one being ADS (Archaeological Data Service) in UK and tDAR (the Digital Archaeological Record) in North America. The Journal of Open Archaeology Data and Internet Archaeology, publish Data Papers where detailed descriptions of specific, freely accessible, datasets are described in the format of a journal article.   The combination of open data with open methods, supported by the use of free and open source software packages (see this comprehensive list), is the only way to ensure reproducible research in archaeology and any other field.