Figure 4 - uploaded by Alessio Palmisano
Content may be subject to copyright.
Spatial analysis of trigger groups: (a) group 2 triggers shown as triangles and the others as grey dots; (b) a pair correlation function (observed values in black and 95% critical envelope in grey)
Contexts in source publication
Context 1
... difficult question therefore becomes: how do we assess the spatial distribution of the trigger sub-- groups while controlling for the overriding spatial structure of trigger distribution in general? Figure 3. Intra--site spatial distributions: (a) Qin terracotta warriors (grey squares) and bronze crossbow triggers (black circles) in the easternmost parts of pit 1, (b) a photograph of a bronze cross--bow trigger. A good example is the trigger sub--group shown in figure 4a. This is a group that, when studied in detailed, exhibits small but distinct morphological and typological differences from other triggers. ...
Context 2
... do so, we run a Monte Carlo simulation in which the triggers attributed to this particular sub--group are assigned at random amongst the overall trigger assemblage. In fact, the group 2 triggers in the pit are, themselves, visibly clustered, beyond the pattern imposed by the battle formation (Figure 4a) and, again, there may not be a need for a formal method to recognise it in this case. However, it is useful to consider this particularly clear--cut example as a proof of concept, and in the knowledge that such standardised evaluation will be far more important in other less obvious cases. ...
Context 3
... it is useful to consider this particularly clear--cut example as a proof of concept, and in the knowledge that such standardised evaluation will be far more important in other less obvious cases. Figure 4b shows a pair correlation function in which this clustering is very evident in the observed result substantial deviation above the 95% envelope. More precisely, the plot indicates particularly strong clustering of this sub--group up to distances of perhaps 3--4m radius and then up to 7--8m, with further possible clustering at much larger distances. ...
Similar publications
![Figure 2.1: Pang et al.'s [1997] visualization pipeline showing three...](profile/Torre-Zuk/publication/286242934/figure/fig5/AS:669990533492740@1536749599244/Pang-et-als-1997-visualization-pipeline-showing-three-types-of-uncertainty_Q320.jpg)
Citations
... Over the past years, Point Pattern Analysis (PPA) has gained significance in archaeological research [1,2]. PPA examines observations that may include diachronic samples in various environmental settings or socio-cultural variables. ...
... The specificity of the analysis can be enhanced by conducting n number (here n = 999) Monte Carlo simulations [54,55] of random point distributions in the study region and visualising the combined results as a 95 percent (i.e. within two standard deviations) confidence interval (referred to as envelope); where the function falls within this confidence envelope the pattern can be considered as spatially random [1,4,56]. ...
Point Pattern Analysis (PPA) has gained momentum in archaeological research, particularly in site distribution pattern recognition compared to supra-regional environmental variables. While PPA is now a statistically well-established method, most of the data necessary for the analyses are not freely accessible, complicating reproducibility and transparency. In this article, we present a fully reproducible methodical framework to PPA using an open access database of archaeological sites located in southwest Germany and open source explanatory covariates to understand site location processes and patterning. The workflow and research question are tailored to a regional case study, but the code underlying the analysis is provided as an R Markdown file and can be adjusted and manipulated to fit any archaeological database across the globe. The Early Iron Age north of the Alps and particularly in southwest Germany is marked by numerous social and cultural changes that reflect the use and inhabitation of the landscape. In this work we show that the use of quantitative methods in the study of site distribution processes is essential for a more complete understanding of archaeological and environmental dynamics. Furthermore, the use of a completely transparent and easily adaptable approach can fuel the understanding of large-scale site location preferences and catchment compositions in archaeological, geographical and ecological research.
... 'One way to engage more effectively with temporal uncertainty is for us to make the best of all our available temporal information, however fuzzy' (Bevan et al., 2013). The 'aoristic' statistical method is used for a better comparison between quantitative data, for example from pollen diagrams or sum-calibrations, and typochronological classification of archaeological data. ...
Does history repeat itself? What causes and mechanisms of action are at work in prehistoric societies? These are two questions we want to tackle in a longue durée perspective. In order to do so we bring together two main epochs in human history in the central northern European Plain: the Neolithic and the Bronze Age of northern Germany. In this timeframe we want to identify patterns of socio-economic cultural transformations.
Consequently, the reconstruction of the causes and mechanisms of action in prehistoric societies are essential. In order to increase our knowledge of potential triggers and drivers of transformations, different economic, demographic, and socio-cultural data as well as climate data will be combined for a timeline of about 3500 years. As a new approach the concept of capitals by Bourdieu will be applied to construct comparable diachronic measurements for our different data sets of Material Culture. This allows for the first-time statistical analyses and quantitatively tested combinations of driving factors and socio-environmental responses. This will allow the identification of comparable patterns of transformation and how differently organised societies in the Neolithic and the Bronze Age reacted to comparable changes.
... There is significant information-inspired archaeological literature effectively invoking entropy that is a precursor to our approach (e.g. Bevan et al., 2013;Barjamovic et al., 2019;Crema, 2015;Diachenko et al., 2020Diachenko et al., , 2022Dickens and Fraser, 1984;Drost & Vander Linden, 2018;Furholt, 2012;Gjesfjeld et al., 2020aGjesfjeld et al., , 2020bGronenborn et al., 2014Gronenborn et al., , 2018Gronenborn et al., , 2020Kandler & Crema, 2019;Neiman, 1995;Paige & Perreault, 2022;Premo & Kuhn, 2010;Shott, 2010;Hegmon et al., 2016;Wiśniewski et al., 2022), but in some cases, internal consistency is not clear. ...
The main objective of this paper is to develop quantitative measures for describing the diversity, homogeneity, and similarity of archaeological data. It presents new approaches to characterize the relationship between archaeological assemblages by utilizing entropy and its related attributes, primarily diversity, and by drawing inspiration from ecology. Our starting premise is that diachronic changes in our data provide a distorted reflection of social processes and that spatial differences in data indicate cultural distancing. To investigate this premise, we adopt a parsimonious approach for comparing assemblage profiles employing and comparing a range of (Hill) diversities, which enable us to exploit different aspects of the data. The modelling is tested on two seemingly large datasets: a Late Bronze Age Cretan dataset with circa 13,700 entries (compiled by PG); and a 4th millennium Western Tripolye dataset with circa 25,000 entries (compiled by AD). The contrast between the strongly geographically and culturally heterogeneous Bronze Age Crete and the strongly homogeneous Western Tripolye culture in the Southern Bug and Dnieper interfluve show the successes and limitations of our approach. Despite the seemingly large size of our datasets, these data highlight limitations that confine their utility to non-semantic analysis. This requires us to consider different ways of treating and aggregating assemblages, either as censuses or samples, contingent upon the degree of representativeness of the data. While our premise, that changes in data reflect societal changes, is supported, it is not definitively confirmed. Consequently, this paper also exemplifies the limitations of large archaeological datasets for such analyses.
... If the values shown by the set of real points (stelae and statues-menhir) were located within the acceptance interval (gray area), these would be consistent with CSR. One of the virtues of this and other similar methods is their multi-scalar perspective, which makes it possible to identify if a given set of points shows different types of distributions at specific scales (e.g., clustered at short distances versus dispersed or even independent at larger scales) (Bevan et al. 2013;Bevan and Conolly 2006). ...
The warrior stelae, also called southwestern stelae or western stelae, emerge as one of the most characteristic manifestations of the Bronze Age in Iberia. Since the earliest findings more than a century ago, these monoliths have received great attention from scholars, becoming the subject of an intense debate, without a consensus having been reached on their meaning and sense. A slow but steady trickle of new findings, as well as the implementation of new approaches to their study, has only enriched these discussions in recent years. One of the most successful lines has been the spatial analysis focused on the relationship of these monuments with routes, transit areas, and resources of great value. It is within this line that this article explores the potential relationship that the stelae may have had with a critical mineral resource: the tin ores distributed in western Iberia, which is the highest concentration of this mineral in Europe. To do this, a detailed spatial analysis has been conducted in order to explore if the uneven density of these monuments across western Iberia may be linked with the presence of tin ores or, alternatively, with the control of the routes that allowed the circulation of this mineral by land.
... A suite of scale-sensitive techniques, or functions, has been developed to overcome this problem. Here, we deploy the three most commonly used in archaeological investigations: the K-function, the L-function, and the Pair Correlation Function (Bevan et al. 2013: 32-42, Bevan 2020, Nakoinz & Ritter 2016. The oldest is the K-function which was developed by B. D. Ripley (thus sometimes also referred to as Ripley's K: Ripley 1977). ...
Remote sensing technologies and spatial analytical approaches hold proven potential for generating completely new quantitative information on, as well as assisting the qualitative interpretation of, archaeological phenomena. They have been, however, under-utilized in Finland. This paper presents an illustrative case study of applying a sequence of well-established computational spatial analyses to data derived from a very precise LiDAR survey of the Nuuttilanmäki Iron Age site near the village of Kalho in Päijät-Häme. Previous fieldwork has identified a group of several stone cairns on a field and forested area near the settlement. Many more cairns, hidden by vegetation and almost invisible to fieldwork inspection , are revealed by the LiDAR data and add to our understanding of the area. Initial fieldwork assessment has suggested that the cairns in the field area and the cairns in the immediately adjacent forest have been produced by different historical processes, with the former presumed burial cairns and the latter the result of agricultural land clearance. An archaeological excavation to verify this interpretation would be prohibitively expensive and might yield uncertain results. But a computational examination of the cairn pattern shows that the spatial interactions between the two groups significantly differ from each, thereby
... However, the use of spatial statistical analysis is almost absent from research produced in nautical and underwater contexts in recent decades. This is quite different from what occurs in the study of terrestrial contexts, where the use of spatial statistics is prolific (Bevan et al. 2013;Theunissen et al. 2014;Carrer 2015;Rodriguez-Rellan and Valcarce 2015;Thacher et al. 2017;Salgueiro et al. 2018;Verbrugghe et al. 2020). This lack of statistical methods in underwater archaeology is surely a methodological issue, when compared with terrestrial archaeology, where accurate spatial data recording has been greatly helped by methods such as total station or DGPS. ...
Spatial statistical analysis is almost absent from research on underwater archaeological
contexts. However, the information obtained using this approach would allow the reconstruction
of depositional dynamics and the exploration of distribution patterns related to
the ships’ on-board organization. This paper proposes a six-step methodology that will
contribute to reducing the current gap in the use of spatial statistical analysis of shipwreck
sites. This methodology will be tested in two distinct case studies, the Uluburun and the
Tortugas wrecks, showing that the same protocol can be useful in the interpretation of
different shipwrecks, in sites with a coherent distribution during their formation process.
Using statistical tools, this methodology will strengthen context awareness, confirming,
refuting, or adding new perspectives to previous interpretations. Finally, the way the framework
was built will allow its replication in other sites.
... However, the use of spatial statistical analysis is almost absent from research produced in nautical and underwater contexts in recent decades. This is quite different from what occurs in the study of terrestrial contexts, where the use of spatial statistics is prolific (Bevan et al. 2013;Theunissen et al. 2014;Carrer 2015;Rodriguez-Rellan and Valcarce 2015;Thacher et al. 2017;Salgueiro et al. 2018;Verbrugghe et al. 2020). This lack of statistical methods in underwater archaeology is surely a methodological issue, when compared with terrestrial archaeology, where accurate spatial data recording has been greatly helped by methods such as total station or DGPS. ...
Spatial statistical analysis is almost absent from research on underwater archaeological contexts. However, the information obtained using this approach would allow the reconstruction of depositional dynamics and the exploration of distribution patterns related to the ships' on-board organization. This paper proposes a six-step methodology that will contribute to reducing the current gap in the use of spatial statistical analysis of shipwreck sites. This methodology will be tested in two distinct case studies, the Uluburun and the Tortugas wrecks, showing that the same protocol can be useful in the interpretation of different shipwrecks, in sites with a coherent distribution during their formation process. Using statistical tools, this methodology will strengthen context awareness, confirming, refuting, or adding new perspectives to previous interpretations. Finally, the way the framework was built will allow its replication in other sites.
... statistically) analysed, and it is possible that those clustered materials identified by O'Connell et al. (1992) may actually represent a different type of spatial pattern (i.e. random or regular patterns) (Bevan et al. 2013;Sánchez-Romero et al. 2022). ...
Kill/butchering sites are some of the most important places for understanding the subsistence strategies of hunter-gatherer groups. However, these sites are not common in the archaeological record, and they have not been sufficiently analysed in order to know all their possible variability for ancient periods of the human evolution. In the present study, we have carried out the spatial analysis of the Early Middle Palaeolithic (MIS 9–8) site of Cuesta de la Bajada site (Teruel, Spain), which has been previously identified as a kill/butchering site through the taphonomic analysis of the faunal remains. Our results show that the spatial properties of the faunal and lithic tools distribution in levels CB2 and CB3 are well-preserved although the site is an open-air location. Both levels show a similar segregated (i.e. regular) spatial point pattern (SPP) which is different from the SPP identified at other sites with similar nature from the ethnographic and the archaeological records. However, although the archaeological materials have a regular distribution pattern, the lithic and faunal remains are positively associated, which is indicating that most parts of both types of materials were accumulated during the same occupation episodes, which were probably sporadic and focused on getting only few animal carcasses at a time.
... Although intra-site spatial approaches are not new in archaeology, in most cases, these have been based mainly on visual, descriptive, and qualitative analyses, where subjective evaluations prevail (Bevan et al. 2013;Giusti and Arzarello 2016;Giusti et al. 2018;Domínguez-Rodrigo et al. 2018). This fact might limit the validity of the conclusions and, in addition, makes it extremely difficult to compare different assemblages. ...
... This fact might limit the validity of the conclusions and, in addition, makes it extremely difficult to compare different assemblages. However, some authors have pushed the boundaries of these techniques, implementing statistical and, more recently, geographic information system (GIS) methods in their intra-site spatial studies (e.g., Whallon 1973Whallon , 1974Doran and Hodson 1975;Hivernel and Hodder 1984;Baxter et al. 1997;Bevan and Conolly 2006;Lenoble et al. 2008;Alperson-Afil 2008Crema et al. 2010;Keeler 2010;Moseler 2011;Gallotti et al. 2011;Nigst and Antl-Weiser 2012;Bosch et al. 2012;Bevan et al. 2013;Domínguez-Rodrigo et al. 2014Baxter 2015;Giusti and Arzarello 2016;Sánchez-Romero et al. 2016Spagnolo et al. 2016Spagnolo et al. , 2019Spagnolo et al. , 2020aDomínguez-Rodrigo and Cobo-Sánchez 2017;Organista et al. 2017;de la Torre and Wehr 2018;Giusti et al. 2018;Gonçalves et al. 2018;Discamps et al. 2019;Mendez-Quintas et al. 2019;Marín et al. 2020;Saladié et al. 2021). New areas of research, such as palimpsest dissection and spatial taphonomy, are employing these techniques in search of a more quantitative and objective approach. ...
Although intra-site spatial approaches are considered a key factor when interpreting archaeological assemblages, these are often based on descriptive, qualitative, and subjective observations. Currently, within the framework of research into spatial taphonomy and palimpsest dissection, several studies have begun to employ more quantitative and objective techniques, implementing tools such as geostatistics and geographic information system (GIS) methods. This is precisely the approach that the Abric Romaní team is following. In this work, we present GIS and geostatistics methods applied to the faunal and lithic assemblages from archaeolevel Ob, including an analysis of the spatial structure, the identification of clusters and sectors, size and fabric analyses, the projection of vertical profiles, and the reconstruction of a digital elevation model of the paleosurface. The results obtained indicate a clustered distribution, primarily concentrated into four dense accumulations. The predominance of remains < 3 cm in length and the absence of preferential orientations make it possible to rule out a generalised postdepositional movement affecting most of the site, although some local movement has been identified. The horizontal and vertical spatial analyses allow us to identify accumulations of a single material (lithic or faunal) in addition to mixed accumulations (lithic and faunal). Integrating all this data with the results of previous studies (zooarchaeological, refits, combustion structures, and partial lithic technological analyses), we evaluate and combine the interpretations proposed previously using different approaches, thereby improving the overall interpretation of the archaeolevel Ob. Finally, we also develop a preliminary comparison between Ob and some other levels at the same site (in particular M and P).
... We use archaeological data covering the Neolithic period in three time slices to test site location parameters as functions of underlying explanatory covariates. We deploy multivariate statistics and point pattern analysis (PPA) of large site databases (Bevan et al., 2013;Crema, Bevan, and Lake, 2010;Knitter and Nakoinz, 2018) and integrate focal environmental and socio-cultural variables as explanatory factors to observe whether Early (EN), Middle (MN), or Late Neolithic (LN) site distributions are functions of underlying control mechanisms or whether they are randomly dispersed across the study area. PPA has broadly entered archaeological research and aims at understanding site location patterns, interdependencies, and preferences in the landscape (Baddeley et al., 2012;Brandolini and Carrer, 2020;Carrero-Pazos, 2019;Costanzo et al., 2021). ...
Computational methods recently gained momentum in archaeological science, particularly affecting large site distribution samples and environmental explanatory parameters. However, quantitative and environmental archaeology are still considered to be limited to a small number of experts and thus less ready to use in general research. Here, we present a case study that integrates computational methods and environmental data into archaeological spatial analyses using Point Pattern Analysis (PPA). We introduce a basic approach to model, visualise, and interpret archaeological site distributions as functions of explanatory covariates in a regional setting of the Neolithic period in the Carpathian Basin. The integration of environmental and socio-cultural variables in a multicomponent analysis allows to distinguish site location parameters and preferences across different chronological periods. Using the code to this article and open-access spatial data, the workflow can be adapted to different regional contexts and chronological periods, making it particularly suitable for spatial pattern comparison.
