Maps representing the distribution of chemical residues over anthropogenic floors are the main diagnostic tools used by archaeologists for addressing the identification of geochemical signatures of past actions. Geostatistics allows producing these maps from a sample of locations by modelling the spatial autocorrelation structure of these kind of phenomena. However, the homogeneity of the prediction regions is a strong assumption in the model. The presence of barriers, such as the inner walls of domestic units, introduces discontinuities in prediction areas. In this paper, we investigate how to incorporate information of a geographical nature into the process of geostatistical prediction. We propose the use of cost-based distances to quantify the correlation between locations, a solution which has proved to be a practical alternative approach for archaeological intrasite analysis. The cost-based approach produces more reliable results avoiding the unrealistic assumption of the homogeneity of the study area. As a working example, a case study of the distribution of two specific chemical signatures in domestic floors is presented within a controlled ethnographical context in Northern Gujarat (India). On a broad disciplinary scale, the benefits of using our approach include improved estimates in regions with complex geometry and lower uncertainty in the kriging predictions.

This work was supported by SimulPast Consolider Ingenio project (CSD2010-00034) and Experimentation and development of advanced artificial intelligence techniques for the computer simulation of social dynamics and historical evolution project (HAR2009-12258), funded by the Spanish Ministry of Science and Innovation. Joan Negre Pérez has worked on this research on a postdoctoral fellowship from the Argentinian Ministry of Science, Technology and Productive Innovation (PICT2012-2148) and partially funded by research grant 2008UAB503 from the Autonomous University of Barcelona (Spain). Facundo Muñoz has worked on this research on a postdoctoral fellowship from the French National Institute for Agricultural Research and partially funded by research grant MTM2013-42323-P from the Spanish Ministry of Economy and Competitiveness and ACOMP/2015/202 from Generalitat Valenciana (Spain). Carla Lancelotti is part of the CaSEs Research Group, a recognised excellence group of the Generalitat de Catalunya (SGR2014-1417). Her work in this paper is partially conducted within the framework of the MoMarq Project (Modelado y Simulación de Marcadores de Actividades Antrópicas: de lo etnográfico a lo arqueológico) funded by the Spanish Ministry of Economy and Competitiveness (HAR2014-55518-P).