Articles by Lorenzo Cardarelli

Cosine Quantogram Analysis (CQA) is a statistical analysis employed in archaeology for the study of numerical datasets with hypothesized quantal distribution. To verify thesignificance of the results, the analysis is often combined with the execution of Monte Carlo simulations. In this article, we present a freely downloadable Python package (CQArchaeo) that integrates CQA and Monte Carlo simulations in the same environment, making the analysis customizable in the main parameters. We provide a guide that enables the use of this tool even for researchers with limited experience in Python programming and demonstrate the applicability, functioning, and main limitations of the analysis on some archaeological datasets.

This research aims to use quantitative and repeatable GIS techniques, as well as Machine Learning algorithms, to study the settlement patterns in Southern Etruria during the final phase of the Bronze Age (1150-950/925 BC). The region of Southern Etruria is located in present-day Latium, Tuscany, and Umbria. The study, which includes 166 settlements, focuses on identifying the morphological characteristics of these settlements by means of raster analysis. Using a Machine Learning approach, the research will compare real settlements with random points within the region to understand the specific characteristics of the settlement pattern in the landscape. The study will also examine the use of feature selection and features importance methods to select the most significant features of a multivariate dataset.

Size reduction algorithms are essential in the study of multivariate datasets. Many variables make it difficult to visualize data. In Archaeology, this problem often concerns the study of some variables, which can be quantitative or qualitative. In this article, several methods for dimension reduction are applied to a pottery dataset from the protohistoric necropolis Osteria dell’Osa, located 20 km East of Rome. These methods offer the possibility of visualising and analysing large amount of data in a very short time. Our results show that non-linear and non-parametric algorithms such as t-SNE and UMAP are the best choice for visualising and exploring this type of data.

The volume of ceramic vessels provides several information about their use. However, due to the fragmentation of pottery coming from settlement sites, data concerning the volume are rarely published. The first goal of this paper is presenting a new method to calculate the volume starting from the archaeological drawings. The dataset could be extended with a predictive regression analysis. The sample analyzed involves cups and bowls found on the top of Monte Cimino (Viterbo-Italy), a settlement and cult site dated to the Final Bronze Age (ca. 1150-950/925 BC).