Detailed behavioral observations permitted the dimensional analysis of formation processes operative on the Mask site, a Nunamiut Eskimo hunting stand. Activity structure, technological organization, disposal mode, and spatial organization were all seen as behavioral dimensions that could each vary, altering the patterns of assemblage content and spatial disposition at an archaeological site. These ethnoarchaeological experiences were then contrasted with those recently reported by John Yellen (1977), and a critical evaluation of his "conclusions" was conducted from the perspective of the Eskimo experience. It was pointed out that basic differences in philosophy and approach to research largely conditioned the contrasting character of the conclusions drawn from the different experiences.
When a spatial point process is observed through a bounded window, edge effects hamper the estimation of characteristics such as the empty space function F, the nearest neighbor distance distribution G and the reduced second-order moment function K. Here we propose and study product-limit type estimators of F, G and K based on the analogy with censored survival data: the distance from a fixed point to the nearest point of the process is right-censored by its distance to the boundary of the window. The resulting estimators have a ratio-unbiasedness property that is standard in spatial statistics. We show that the empty space function F of any stationary point process is absolutely continuous, and so is the product-limit estimator of F. The estimators are strongly consistent when there are independent replications or when the sampling window becomes large. We sketch a CLT for independent replications within a fixed observation window and asymptotic theory for independent replications of sparse Poisson processes. In simulations the new estimators are generally more efficient than the "border method" estimator but (for estimators of K), somewhat less efficient than sophisticated edge corrections.
"The 1992 publication of Pottery Function applied ethnoarchaeological data collected among the Kalinga and experiments to set forth the principles for the creation of pottery use-alteration traces (residue, carbonization, and abrasion). Analogous to lithic use-wear analysis, this study developed the method and theory making the connections between pottery use traces and function. At the 20th anniversary of the book, it is time to assess what has been done and learned. One of the concerns of those working in pottery analysis is that they are unsure how to "do" use-alteration analysis on their collection. Another common concern is understanding intended pottery function--the connections between technical choices and function. This book is designed to answer these questions using case studies from the author and many others who are applying use-alteration analysis to infer actual pottery function. The focus of Understanding Pottery Function is on how practicing archaeologists can infer function from their ceramic collection."--Publisher's website
R is a scientific programming language that is widely used by archaeologists. This entry briefly describes the history and distinctive characteristics of the language, and how archaeologists have used it. The importance of R for reproducible research in archaeology is outlined, and future directions for the language in archaeology are indicated.
This paper provides a personal account of the challenges of developing digital methods within an interpretive landscape archaeology framework. It reviews current criticisms leveled against the use of model-based tools, e.g., GIS-based, within this framework. Currently, the absence of, or distance between, methods and theory is considered to be an important limitation when adopting such orientation. This gap is largely due to the particular nature of the theoretical sources informing this framework. This paper suggests the need for middle ground/bridging concepts, i.e., concepts that enable the instantiation within concrete archaeological contexts of various aspects discussed within an interpretative framework, as a way to shorten this gap. It also highlights the importance of the nature of representations when applying digital methods and their key role when producing new archaeological information. Finally, it attempts to elevate the role that model-based methods and simulations can play within an interpretive landscape framework, and to insist on the development of new middle ground solutions (methods and concepts) when existing tools do not meet our theoretical challenges.
The K-function is a method used in spatial Point Pattern Analysis (PPA) to inspect the spatial distribution of a set of points. It allows the user to assess if the set of points is more or less clustered that what we could expect from a given distribution. Most of the time, the set of point is compared with a random distribution.
The paper presents a reconsideration of settlement pattern and defensive systems in south-eastern Italy during the Bronze Age, on the ground of the archaeological data coming from the excavations at Coppa Nevigata. In particular, the transformations
In the naturalistic decision-making literature, intuitive cognition is at the heart of a pattern recognition–based decision model called the recognition-primed decision model. Given the importance of intuitive cognition in naturalistic decision-making theory, we explore the question of what makes intuitive cognition effective for decision making and, in so doing, present an extended empirical and theoretical foundation for the intuitive component in naturalistic decision making. We theorize that intuitive cognition is effective because it (1) possesses a capability for grounded, situational meaning making (sign interpretation); (2) is operative over extended work intervals involving interruptions; and (3) is instrumental in handling situated complexities of everyday living. Other characteristics of intuitive cognition and its foundations are discussed. We propose that intuitive cognition represents the core of cognition—grounded, situational meaning making—whereas analytical cognition represents a form of an intellectual exoskeleton that provides added capabilities (e.g., working memory).
Modern Statistical Methodology and Software for Analyzing Spatial Point PatternsSpatial Point Patterns: Methodology and Applications with R shows scientific
Paradigmatic examples of stochastic processes are coin-tossing and the sequences of uniform random numbers provided by computer routines. A large number of independent random experiments show nontrivial collective phenomena such as the deterministic behavior of averages, known as the law of large numbers and qualitative changes as a consequence of small quantitative parameter changes known as phase transitions. The behavior of the number of individuals of a population may be described by birth-and-death processes, for which at each unit of time a new individual is born or a present individual dies, and by branching processes, for which each new individual generates a family that grows and dies independently of the other families. These examples are particular cases of Markov chains roughly described by the fact that the probabilistic law of the next experiment depends only on the result of the current one. The main issue for these chains is the study of their long time behavior. Interacting particle systems refer to the time evolution of families of processes for which the updating of each member of the family depends on the current values of the other members. The voter model and the exclusion process are discussed. Hydrodynamics deals with the study of particle systems in large space regions at long times relating the stochastic systems with deterministic partial differential equations.
The notes that follow are the first results of a programme of field-survey undertaken by the writer and by various members of the British School during the autumn of 1954 in the area that lies immediately to the north of Rome, between the Tiber and the sea. This area is one that has been strangely neglected by modern students of Italian topography. Ashby's published work is concerned mainly with those parts of the Campagna that lie to the south and east of Rome; and Tomassetti's work, invaluable as a repertory of manuscript and published sources, lays no claim to be a comprehensive survey of the material remains surviving on the ground. Such a survey is badly needed today. The romantic desolation of Southern Etruria is being transformed from one day to the next under the impact of a scheme of landreform comparable in scale to the great reforms of classical antiquity, and vast estates which for centuries have been used for stock-breeding and seasonal pasture are being broken up and brought into cultivation with all the devastating thoroughness that modern mechanical equipment entails. Whole regions are accessible today as they have never been before, and within them the bulldozer and the mechanical plough are busy destroying whatever lies in their path.
Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from –1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.
Explainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature importance. However, there are several different approaches how feature importances are being measured, most notably global and local. In this study we compare different feature importance measures using both linear (logistic regression with L1 penalization) and non-linear (random forest) methods and local interpretable model-agnostic explanations on top of them. These methods are applied to two datasets from the medical domain, the openly available breast cancer data from the UCI Archive and a recently collected running injury data. Our results show that the most important features differ depending on the technique. We argue that a combination of several explanation techniques could provide more reliable and trustworthy results. In particular, local explanations should be used in the most critical cases such as false negatives.
Una magistrale sintesi delle fasi evolutive che nell'arco di meno di duemila anni - dal III al I millennio a.C. - hanno portato il territorio italiano dal pullulare di comunità instabili costituite da gruppi di parentela di poche decine di individui all'emergere di élites aristocratiche e al sorgere di vere e proprie forme di organizzazione protostatale: in una parola, alla nascita di quella particolare entità geografico-culturale che prenderà il nome di Italia.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use:IPython and Jupyter: provide computational environments for data scientists using PythonNumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in PythonPandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in PythonMatplotlib: includes capabilities for a flexible range of data visualizations in PythonScikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Etruria Meridionale: conoscenza: le coste, la pesca. Le acque interne: appunti di archeologia preistorica. Gli Etruschi e le sorgenti termali. Le rocce e le argille dell’Etruria meridionale. La flora e le risorse agricole. La fauna e l’allevamento. Il popolamento dell’Etruria Meridionale e le caratteristiche degli insediamenti tra l’età del Bronzo e l’età del Ferro. La malaria nell’Etruria Meridionale. Il Centro di catalogazione dei beni culturali della provincia di Viterbo. Discussione. Etruria Meridionale: conservazione: La conservazione dell’architettura in tufo. Metodologia della conservazione della pittura parietale. I restauri sulle tombe dipinte di Tarquinia: aspetti metodologici e primi risultati. Conservazione sullo scavo e restauro in laboratorio: alcuni recenti interventi. La tomba dei rilievi in Cerveteri: applicazione della metodologia climatica. Metodo sperimentale per l’analisi di alcuni aspetti conservativi connessi all’uso di trattamenti superficiali. Conservazione e fruizione: analisi ambientale sulla tomba dipinta di Tarquinia. La conservazione del paesaggio. Il laboratorio di restauro dell’amministrazione provinciale di Viterbo. Discussione. Etruria Meridionale: fruizione: Musei e zone archeologiche dalla conoscenza alla fruizione. La parte delle comunità locali. La parte delle associazioni. Gli itinerari archeologici. Turismo e cultura: aspetti e beni di un’unica realtà
Machine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are particularly suited toward rapid identification and classification of archaeological features and objects. The results of these new studies include identification of many new sites around the world and improved classification of large archaeological datasets. ML fits well with more traditional methods used in archaeological analysis, and it remains subject to both the benefits and difficulties of those approaches. Small datasets associated with archaeological work make ML vulnerable to hidden complexity, systemic bias, and high validation costs if not managed appropriately. ML's scalability, flexibility, and rapid development, however, make it an essential part of twenty-first-century archaeological practice. This review briefly describes what ML is, how it is being used in archaeology today, and where it might be used in the future for archaeological purposes.
Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern
Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern
In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and finally supplementary information (supplementary individuals and variables). Moreover, the dimensions issued from the different exploratory data analyses can be automatically described by quantitative and/or categorical variables. Numerous graphics are also available with various options. Finally, a graphical user interface is implemented within the Rcmdr environment in order to propose an user friendly package.
Despite increasing concern with the effects of archaeological data recovery methods on the types and quantity of objects extracted from the material record, archaeologists rarely discuss recovery biases attributable to the most basic excavation procedures. In this study I examine how several factors, including variable artifact identification skills, can affect artifact recovery rates in the field. Data from household-level investigations at the Stó:lō (Coast Salish) village of Welqámex (DiRi 15) are presented to show how interobserver variation can compromise interpretations of past behavior when opportunities for artifact recovery are limited to observations at the excavation unit and single-episode (field-only) sieving. Laboratory sorting of screen residue retained in 3.2-mm (one-eighth-inch) mesh sieves is shown to account for the recovery of as much as 87.5 percent of lithic artifacts and nearly 90 percent of archaeofaunal remains. Rates of artifact recovery in the field are highly variable among excavation crews working at Welqámex, and I argue that the application of correction factors is inappropriate unless the magnitude of recovery bias can be measured for specific excavation teams and particular depositional contexts. The results of this study further highlight the importance of documenting and mitigating the unintended effects of methodological decisions on archaeological assemblages. , Résumé A despecho del aumento en la preocupación con los efectos de la recuperación de datos con métodos arqueológicos en los tipos y cantidad de objetos extractados de la anotación material, arqueólogos rara vez discuten los sesgos en recuperación atribuidos a los procedimientos más básicos de excavación. En éste estudio examino cómo varios factores, incluyendo variación en las habilidades para identificar artefactos, pueden afectar la razón de artefactos recuperados en el campo. Datos de investigaciones al raso doméstico en la aldea Stó:lō (Coast Salish) de Welqámex (DiRi 15) son destacados para mostrar cómo variación en habilidad puede comprometer interpretaciones de un comportamiento pasado cuando la oportunidad para recuperar artefactos es limitada a observaciones en la unidad de excavación y un solo (campo únicamente) acontecimiento de harnero. En el laboratorio, separación de residuo retenido en cedazos de 3.2-mm (un octavo de pulgada) cuenta por la recuperación de hasta 87.5 por ciento de artefactos líticos y casi el 90 por ciento de restos de fauna arqueológicas. La razón de artefactos recuperados en el campo se ha presentado altamente variable entre equipos excavadores, y así arguyo que la aplicación de factores rectificadores es inadecuada menos que el impacto del sesgo en recuperación sea específicamente medida para equipos excavadores y los contextos particulares que producen depósitos. Los resultados de este estudio marcan adicionalmente la importancia de documentar y aminorar los efectos inadvertidamente causadas por las decisiones metodológicas en colecciones arqueológicas.
Little attention has been paid by archaeologists to the important problem of replicability of observational units. The studies presented in this paper involve examinations of discrepancy occurring at various levels in lithic and ceramic classification. Standardized typologies, as well as qualitative and quantitative attributes are considered. The results are discussed in terms of observer bias, influence of training, measurement error, and the implications for the statistical treatment of data.
Non-hominid faunal remains associated with cultural deposits have long been of interest to archaeologists. Recent archaeological work (Coutts and Higham, 1971; Daly, 1969; Drew et al. , 1971; Flannery, 1966; Higham and Leach, 1971; Shawcross, 1967; Ucko and Dimbleby, 1969) is showing an increased utilization of these associated faunal remains for detailed analysis of prehistoric man's environment, hunting techniques, dietary habits, the effects of domestication upon animals, changes in these over time, and seasonal dating. As analysis becomes more detailed and the need to extract increased amounts of relevant and sophisticated data from faunal remains grows more demanding, the representative quality of our samples of faunal remains becomes more critical. Many of the demands made upon our samples require that increased attention be paid to the recovery and analysis of some of the less obvious constituents of these faunal assemblages.
Archaeologists increasingly have become aware of the effects of bias and have made strides to identify and correct for error introduced in such areas as sampling and recovery techniques. Much less attention has been paid to the significance of bias introduced during artifact analysis. The potential for analyst-induced error is discussed in terms of: (1) the explicitness of class definitions, (2) differences in perception among analysts, and (3) changes in a single analyst's perception over time. Using a regression-based approach, sources of possible analytic error are detected in an archaeological data set recovered from Steens Mountain, Oregon.
Il presente articolo intende illustrare i risultati preliminari delle ricerche archeologiche di superficie e geofisiche avviate nel 2013 sul sito della città romana, tardoantica e medievale di Salapia (Puglia settentrionale). Le indagini sono parte di un più ampio progetto di studio dei paesaggi storici di uno dei territori più complessi della Puglia settentrionale, ovvero la fascia costiera adriatica, in antico interessata dalla presenza del lago di Salpi. Nonostante le numerose testimonianze fornite dalle fonti letterarie, che attestano l’importanza di Salapia come porto e centro di riferimento per il popolamento di età romana e medievale del comprensorio in esame, il sito non è mai stato oggetto di ricerche sistematiche e organiche, utili per chiarire l’articolazione della città, le dinamiche di vita, il ruolo svolto nel quadro delle relazioni adriatiche.
Funerary landscapes are eminent results of the relationship between environments and superstructural human behavior, spanning over wide territories and growing over centuries. The comprehension of such cultural palimpsests needs substantial research efforts in the field of human ecology. The funerary landscape of the semi-arid region of Kassala (Eastern Sudan) represents a solid example. Therein, geoarchaeological surveys and the creation of a desk-based dataset of thousands of diachronic funerary monuments (from early tumuli up to modern Beja people islamic tombs) were achieved by means of fieldwork and remote sensing over an area of ∼4100 km 2 . The wealth of generated information was employed to decipher the spatial arrangement of sites and monuments using Point Pattern Analysis. The enormous number of monuments and their spatial distribution are here successfully explained using, for the first time in archaeology, the Neyman-Scott Cluster Process, hitherto designed for cosmology. Our study highlights the existence of a built funerary landscape with galaxy-like aggregations of monuments driven by multiple layers of societal behavior. We suggest that the distribution of monuments was controlled by a synthesis of opportunistic geological constraints and cultural superstructure, conditioned by the social memory of the Beja people who have inhabited the region for two thousand years and still cherish the ancient tombs as their own kin’s.
ABSTRACT Study of the climate in the Mediterranean basin during different historical periods has taken on a particular importance, particularly regarding its role (together with other factors) in the evolution of human settlement patterns. Although the Roman age is traditionally considered a period with a favourable climate, recent studies have revealed considerable complexity in terms of regional climate variations. In this paper, we compare the hydrological change from speleothem proxy records with flood reconstructions from archaeological sites for Northern Tuscany (central Italy). We identify a period of oscillating climatic conditions culminating in a multidecadal dry event during the 1st century bc , followed by a century of increased precipitation at the beginning of the Roman Empire and subsequently a return to drier conditions in the 2nd century ad. The period of rainfall increase documented by the speleothems agrees with both the archaeological flood record as well as historical flood data available for the Tiber River, ca. 300 km to the south. These data also suggest a return to wetter conditions following the 3nd and 4rd centuries ad.
Abstract This paper presents the results of a change‐detection study of the historical agricultural terraced landscape in “Costa Viola” (Calabria, South Italy). During the last century, because of the loss of economic competitiveness, it has undergone progressive abandonment, followed by landscape degradation. Taking into consideration the very steep slopes of Costa Viola and the need to analyse with high precision the historical evolution of the terraced landscape, research methods were implemented coupling advanced geomatic techniques with in situ detailed surveys. Based on historical aerial photographs, orthophotos, and numeric cartography, we analysed the land use/land cover change in the period 1955–2014 using photogrammetric and geoprocessing techniques, focusing particularly on trajectories in agricultural terraces. Area covered by active terraces decreased dramatically between 1955 and 2014, from 813.25 to 118.79 ha (−85.4%). The implemented spatial database was built in a free open‐source software taking into consideration spatial accuracies and completeness. Spatial comparison among land use/land cover maps was carried out using a postclassification comparison technique that can provide complete cross‐tabulation matrices. These data were compared with socio‐economic statistics concerning demography and trends of farms with vineyards. The evolutionary dynamics of the active agricultural terraces were also analysed trough the definition of 6 types of spatio‐temporal patterns recognised in the analysed period. These methods allowed to highlight the ongoing dynamics of abandonment of agricultural terraces in relation to their main causes and effects. Although tailored for the specific case study, they can be applied to many other terraced agricultural landscapes presenting similar characteristics and problems.
Archaeological site predictive modeling is widely adopted in archaeological research and cultural resource management. It is conducive to archaeological excavation and reveals the progress of human social civilization. Xiangyang City is the focus of this paper. We selected eight geographical variables as the influencing variables, which are elevation, slope, aspect, micro-landform, slope position, plan curvature, profile curvature, and distance from water. With them, we randomly obtained 260 non-site points at the ratio of 1:1 between site points and non-site points based on the 260 excavated archaeological sites and constructed a sample set of geospatial data and the archaeological based on logistic regression (LR). Using 10-fold cross-validation, we trained and tested the model to select the best samples. Thus, the quantitative relationship between the archaeological sites and geographical variables was established. As a result, the Area Under the Curve (AUC) of the LR model is 0.797 and its accuracy is 0.897 in the study. A geographical detector unveils that the three influencing variables of Distance from water, elevation and Plan Curvature top the chart. The archaeological under LR is highly stable and accurate. The geographical variables constitute crucial variables in the archaeological.
We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.
Point pattern analysis (PPA) has gained momentum in archaeological research that models large-scale distributions of sites and explanatory covariates. As such, there has been increased interest in the bias of archaeological distributions, which mostly have an impact due to modern land-use change. These interactions, however, have not yet been fully explored. In order to better understand archaeological point patterns as functions of explanatory covariates, we offer three different approaches: (i) environmental preference modelling of archaeological records in different chronological phases; (ii) a custom bias surface that represents the variability of the regional landscape; (iii) an R-package (rbias) allowing the generation of a fuzzified bias surface based on Open Street Map (OSM) data.
Predictive modeling-the practice of building models that in some way indicate the likelihood of archaeological sites, cultural resources, or past landscape use
This study aims to reconstruct the dynamics of population that characterized and conditioned the Versilia’s land, after the arrival of Romans, through a comprehensive analysis of archaeological remains, about to the period from the II century B.C. to
The Apuane Alps region of the Northern Apennines provides exceptionally clear exposures of continental margin rocks deformed during collision of the Corsica-Sardinia microplate with Italy. Detailed structural mapping reveals a large scale crustal shear zone in which major isoclinal folds were rotated as much as 90°, bringing their axes into parallelism with the direction of nappe transport. A large tectonic window through the allochthonous cover sequences exposes, in ascending structural order, the following sequences: (1) the Apuane metamorphic sequences, which have been repeatedly deformed and metamorphosed to greenschist fades. They consist of continental margin, sedimentary rocks of Permian to Oligocene age deposited on Paleozoic continental basement. (2) The Tuscan nappe, which consists of an essentially unmetamorphosed, and only slightly deformed sequence of similar lithologies and ages. The Tuscan nappe has been thrust over the Apuane metamorphic sequences along an evaporite layer. (3) The Liguride sequences, which consist of deep water pelagic sediments and ophiolites. Structures formed during several phases of compressive deformation followed by a late stage of extension. During the first phase ($D_{1}$), the Tuscan nappe, together with the overlying Liguride sequences, was emplaced over the metamorphic sequences, which were ductiley deformed into tight, recumbent folds with flat axial surfaces. A second phase ($D_{2}$), and third phase ($D_{3}$) refolded all pre-existing structures and formed crenulation cleavages and conjugate schistosities. Both $D_{2}$ and $D_{3}$ phases are post-nappe emplacement. The late stage uplift of the region is related to regional extension in Tuscany. During the $D_{1}$ deformation of the metamorphic sequences, simple shear strain was dominant. This is demonstrated by the presence of schistosities oriented at a low angle to shear zone boundaries, strongly developed mineral elongation lineations parallel to the likely transport direction of fold nappes, and numerous strain discontinuities with geometries typical of simple shear zone boundaries. A progressive 90° change in the orientation of $A_{1}$ fold axes from directions parallel to the strike of the mountain belt into directions parallel to the mineral extension lineation can be seen within the metamorphic sequences. This is interpreted as an example of passive fold rotation during progressive simple shear. The above features suggest that the Apuane Alps region was deformed and metamorphosed within a large scale low-angle crustal shear zone with an overthrust sense of movement. The regional, greenschist facies metamorphism is confined to the rocks within the proposed shear zone and it is likely that frictional (shear) heating was significant in its evolution. The development of the shear zone within continental crust, the penetrative deformation and metamorphism, and the emplacement of the allochthonous sequences are the effects of compressive deformation of the Northern Apennine continental margin from the Oligocene to the Miocene. This is attributed to rotation of the Corsica-Sardinia microplate and its subsequent collision with the Italian continental crust.
Located in northern Tuscany, the city of Lucca represents a perfect case study to understand the development of the late antique funerary landscape. Although numerous burials are known for the 5th-7th century timespan, few studies have explored the factors that led to the formation of several cemetery areas within urban and suburban spaces. This study thus aims to investigate spatial interactions among burials and assess the role of urban and suburban elements in creating funerary landscapes through Point Pattern Analysis.
Amongst the many research projects dealing with the occupation of alpine landscapes, very few directly deal with the Roman period. Older research projects often emphasised the study of the major trans-Alpine routes, or the administrative organisation of the urban zones in the Alps. The settlement of these regions was primarily based on data from earlier excavations, mainly from the valley bottoms of the northernAlps. Rescue (or salvage) archaeology has enhanced our knowledge of lowland alpine archaeology, but it is the research undertaken during the past decade in the Southern Alps that increased our understanding of mountain populations covering all altitudes from valley bottoms to the high altitudes.,Parmi les nombreux travaux portant sur l’occupation du milieu alpin, très peu concernent la période antique. Les recherches, souvent anciennes, se sont focalisées sur des questions telles que les grandes voies transalpines, l’organisation administrative des régions alpines ou le développement urbain. L’occupation de ces régions était principalement appréhendée à travers quelques découvertes ou des fouilles anciennes, principalement dans les fonds de vallées des Alpes du Nord. Le développement de l’archéologie préventive a permis d’acquérir de nouvelles connaissances dans les zones basses mais ce sont surtout les recherches entreprises, depuis une dizaine d’années dans les Alpes méridionales, qui ont fait progresser les connaissances sur le peuplement de la montagne, en appréhendant ce milieu dans son ensemble, depuis les vallées jusqu’à la haute montagne.
