Di Palma et alii manuscript delves into applying remote sensing and photogrammetry methods to document and analyze the castrum at the Umm er-Rasas site in Jordan. This research aimed to map all the known archaeological evidence, detect new historical structures, and create a digital archive of the site's features for study and education purposes [1].

Their research has been organized into two phases. The first one consisted of a remote sensing survey and involved collecting historical and modern aerial and satellite imagery, such as:  aerial photographs by Sir Marc Aurel Stein from 1939; panchromatic spy satellite images from the Cold War period (Corona KH-4B and Hexagon KH-9); high and very high resolution (HR and VHR) modern multispectral satellite images (Pléiades-1A and Pléiades Neo-4) [1]. This dataset was processed using the ENVI 4.4 software and applying multiple image-enhancing techniques (Pansharpening, RGB composite, data fusion, and Principal Component Analysis). Then, the resulting images were integrated into a QGIS project, allowing for visual analyses of the site's features and terrain. These investigations provided:

·         a broad overview of the site,

·         the discovery of a previously unknown archaeological feature (the northeastern dam),

·         a stage for targeted ground-level investigations [1].

The project's second phase was dedicated to intensive fieldwork operations, including pedestrian surveys, stratigraphic excavations, and photogrammetric recordings, such as: photographic reconstructions via Structure from Motion (SfM) and laser scanner sessions (using two FARO X330 HDR). In particular, the laser scanner data were processed with Reconstructor 4.4, which provided highly detailed 3D models for the QGIS database. These results were crucial in validating the information acquired during the first phase.

Overall, the paper is well written, with clear objectives and a systematic presentation of the site [2,3,10,11], the research materials, and the study phases. The dataset was described in meticulous detail (especially the remote sensing sources and the laser scanner recordings). The methods implemented in this study are rigorously described [4,5,6,7,8,9] and show a high level of integration between aerial and field techniques. The results are neatly illustrated and fit into the current debates about the efficacy of remote sensing detection and multiscale approaches in archaeological research.

In conclusion, this manuscript significantly contributes to archaeological research, unveiling new and exciting findings about the site of Umm er-Rasas. Its findings and methodologies warrant publication and further exploration.

References:

1.    Di Palma, F., Gabrielli, R., Merola, P., Miccoli, I. and Scardozzi, G. (2024). Study and enhancement of the heritage value of a fortified settlement along the Limes Arabicus. Umm ar-Rasas (Amman, Jordan) between remote sensing analysis, photogrammetry and laser scanner surveys. Zenodo, 8306381, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8306381

2.    Abela J. and Acconci A. (1997), Umm al‐Rasas Kastron Mefa’a. Excavation Campaign 1997. Church of St. Paul: northern and southern flanks. Liber Annus, 47, 484‐488.

3.    Bujard J. (2008), Kastron Mefaa, un bourg à l'époque byzantine: Travaux de la Mission archéologique de la Fondation Max van Berchem à Umm al‐Rasas, Jordanie (1988‐1997), PhD diss., University of Fribourg 2008.

4.    Cozzolino M., Gabrielli R., Galatà P., Gentile V., Greco G., Scopinaro E. (2019), Combined use of 3D metric surveys and non‐invasive geophysical surveys at the stylite tower (Umm ar‐Rasas, Jordan), Annals of geophysics, 62, 3, 1‐9. http://dx.doi.org/10.4401/ag‐8060

5.    Gabrielli R., Salvatori A., Lazzari A., Portarena D. (2016), Il sito di Umm ar‐Rasas – Kastron Mefaa – Giordania. Scavare documentare conservare, viaggio nella ricerca archeologica del CNR. Roma 2016, 236‐240.

6.    Gabrielli R., Portarena D., Franceschinis M. (2017), Tecniche di documentazione dei tappeti musivi del sito archeologico di Umm al‐Rasas Kastron Mefaa (Giordania). Archeologia e calcolatori, 28 (1), 201‐218. https://doi.org/10.19282/AC.28.1.2017.12

7.    Lasaponara R., Masini N. (2012 ed.), Satellite Remote Sensing: A New Tool for Archaeology, New York 2012.

8.    Lasaponara R., Masini N. and Scardozzi G. (2007), Immagini satellitari ad alta risoluzione e ricerca archeologica: applicazioni e casi di studio con riprese pancromatiche e multispettrali di QuickBird. Archeologia e Calcolatori, 18 (2), 187‐227. https://core.ac.uk/download/pdf/33150351.pdf

9.    Lasaponara R., Masini N., Scardozzi G. (2010), Elaborazioni di immagini satellitari ad alta risoluzione e ricognizione archeologica per la conoscenza degli insediamenti rurali del territorio di Hierapolis di Frigia (Turchia). Il dialogo dei Saperi – Metodologie integrate per i Beni Culturali, Edizioni scientifiche italiane, 479‐494.

10. Piccirillo M., Abela J. and Pappalardo C. (2007), Umm al‐Rasas ‐ campagna 2007. Rapporto di scavo. Liber Annus, 57, 660‐668.

11. Poidebard A. (1934), La trace de Rome dans le désert de Syrie : le limes de Trajan à la conquête arabe ; recherches aériennes 1925 – 1932. Paris : Geuthner.

The paper by Aline Deicke [1] is very readable, and it succeeds in presenting a still unnoticed topic in a well-structured way. It addresses the topic of “how to model social-spatial relations in antiquity”, as the title concisely implies, and makes important and interesting points about their interrelationship by drawing on latest theories of sociologists such as Martina Löw combined with digital tools, such as the CIDOC CRM-modeling. 

The author provides an introductory insight into the research history of funerary archaeology and addresses the problematic issue of not having investigated fully the placement of entities of the grave inventory. So far, the focus of the analysis has been on the composition of the assemblage and not on the positioning within this space-and time-limited context. However, the positioning of the various entities within the burial context also reveals information about the objects themselves, their value and function, as well as about the world view and intentions of the living and dead people involved in the burial. To obtain this form of qualitative data, the author suggests modeling knowledge networks using the CIDOC CRM. The method allows to integrate the spatial turn combined with aspects of the actor-network-theory. The theoretical backbone of the contribution is the fundamental scholarship of Martina Löw’s “Raumsoziologie” (sociology of space), especially two categories of action namely placing and spacing (SC1). The distinction between the two types of action enables an interpretative process that aims for the detection of meaningfulness behind the creation process (deposition process) and the establishment of spatial arrangement (find context). 

To illustrate with a case study, the author discusses elite burial sites from the Late Urnfield Period covering a region north of the Alps that stretches from the East of France to the entrance of the Carpathian Basin. With the integration of very basic spatial relations, such as “next to”, “above”, “under” and qualitative differentiations, for instance between iron and bronze knives, the author detects specific patterns of relations: bronze knives for food preparing (ritual activities at the burial site), iron knives associated with the body (personal accoutrement).

The complexity of the knowledge engineering requires the gathering of several CIDOC CRM extensions, such as CRMgeo, CRMarchaeo, CRMba, CRMinf and finally CRMsoc, the author rightfully suggests. In the end, the author outlines a path that can be used to create this kind of data model as the basis for a graph database, which then enables a further analysis of relationships between the entities in a next step. Since this is only a preliminary outlook, no corrections or alterations are needed. 

The article is an important step in advancing digital archaeology for qualitative research.

References

[1] Deicke, A. (2024). Spaces of funeral meaning. Modelling socio-spatial relations in burial contexts. Zenodo, 8310170, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8310170

In a recent article, Fumihiro Sakahira and Hiro'omi Tsumura (2023) used social network analysis methods to analyze change in obsidian trade networks in Japan throughout the 13,000-year-long Jomon period. In the paper recommended here (Sakahira and Tsumura 2024), Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction they revisit that data and describe additional analyses that confirm the robustness of their social network analysis. The data, analysis methods, and substantive conclusions of the two papers overlap; what this new paper adds is a detailed examination of the data and methods, including use of bootstrap analysis to demonstrate the reasonableness of the methods they used to group sites into clusters.

Both papers begin with a large dataset of approximately 21,000 artifacts from more than 250 sites dating to various times throughout the Jomon period. The number of sites and artifacts, varying sample sizes from the sites, as well as the length of the Jomon period, make interpretation of the data challenging. To help make the data easier to interpret and reduce problems with small sample sizes from some sites, the authors assign each site to one of five sub-periods, then define spatial clusters of sites within each period using the DBSCAN algorithm. Sites with at least three other sites within 10 km are joined into clusters, while sites that lack enough close neighbors are left as isolates. Clusters or isolated sites with sample sizes smaller than 30 were dropped, and the remaining sites and clusters became the nodes in the networks formed for each period, using cosine similarities of obsidian assemblages to define the strength of ties between clusters and sites.

The main substantive result of Sakahira and Tsumura’s analysis is the demonstration that, during the Middle Jomon period (5500-4500 cal BP), clusters and isolated sites were much more connected than before or after that period. This is largely due to extensive distribution of obsidian from the Kozu-shima source, located on a small island off the Japanese mainland. Before the Middle Jomon period, Kozu-shima obsidian was mostly found at sites near the coast, but during the Middle Jomon, a trade network developed that took Kozu-shima obsidian far inland. This ended after the Middle Jomon period, and obsidian networks were less densely connected in the late and last Jomon periods.

The methods and conclusions are all previously published (Sakahira and Tsumura 2023). What Sakahira and Tsumura add in Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction are:

·       an examination of the distribution of cosine similarities between their clusters for each period

·       a similar evaluation of the cosine similarities within each cluster (and among the unclustered sites) for each period

·       bootstrap analyses of the mean cosine similarities and network densities for each time period

These additional analyses demonstrate that the methods used to cluster sites are reasonable, and that the use of spatially defined clusters as nodes (rather than the individual sites within the clusters) works well as a way of reducing bias from small, unrepresentative samples. An alternative way to reduce that bias would be to simply drop small assemblages, but that would mean ignoring data that could usefully contribute to the analysis.

The cosine similarities between clusters show patterns that make sense given the results of the network analysis. The Middle Jomon period has, on average, the highest cosine similarities between clusters, and most cluster pairs have high cosine similarities, consistent with the densely connected, spatially expansive network from that time period. A few cluster pairs in the Middle Jomon have low similarities, apparently representing comparisons including one of the few nodes on the margins on the network that had little or no obsidian from the Kozu-shima source. The other four time periods all show lower average inter-cluster similarities and many cluster pairs have either high or low similarities. This probably reflects the tendency for nearby clusters to have very similar obsidian assemblages to each other and for geographically distant clusters to have dissimilar obsidian assemblages. The pattern is consistent with the less densely connected networks and regionalization shown in the network graphs. Thinking about this pattern makes me want to see a plot of the geographic distances between the clusters against the cosine similarities. There must be a very strong correlation, but it would be interesting to know whether there are any cluster pairs with similarities that deviate markedly from what would be predicted by their geographic separation.

The similarities within clusters are also interesting. For each time period, almost every cluster has a higher average (mean and median) within-cluster similarity than the similarity for unclustered sites, with only two exceptions. This is partial validation of the method used for creating the spatial clusters; sites within the clusters are at least more similar to each other than unclustered sites are, suggesting that grouping them this way was reasonable.

Although Sakahira and Tsumura say little about it, most clusters show quite a wide range of similarities between the site pairs they contain; average within-cluster similarities are relatively high, but many pairs of sites in most clusters appear to have low similarities (the individual values are not reported, but the pattern is clear in boxplots for the first four periods). There may be value in further exploring the occurrence of low site-to-site similarities within clusters. How often are they caused by small sample sizes? Clusters are retained in the analysis if they have a total of at least 30 artifacts, but clusters may contain sites with even smaller sample sizes, and small samples likely account for many of the low similarity values between sites in the same cluster. But is distance between sites in a cluster also a factor? If the most distant sites within a spatially extensive cluster are dissimilar, subdividing the cluster would likely improve the results. Further exploration of these within-cluster site-to-site similarity values might be worth doing, perhaps by plotting the similarities against the size of the smallest sample included in the comparison, as well as by plotting the cosine similarity against the distance between sites. Any low similarity values not attributable to small sample sizes or geographic distance would surely be worth investigating further.

Sakahira and Tsumura also use a bootstrap analysis to simulate, for each time period, mean cosine similarities between clusters and between site pairs without clustering. They also simulate the network density for each time period before and after clustering. These analyses show that, almost always, mean simulated cosine similarities and mean simulated network density are higher after clustering than before. The simulated mean values also match the actual mean values better after clustering than before. This improved match to actual values when the sites are clustered for the bootstrap reinforces the argument that clustering the sites for the network analysis was a reasonable result.

The strength of this paper is that Sakahira and Tsumura return to reevaluate their previously published work, which demonstrated strong patterns through time in the nature and extent of Jomon obsidian trade networks. In the current paper they present further analyses demonstrating that several of their methodological decisions were reasonable and their results are robust. The specific clusters formed with the DBSCAN algorithm may or may not be optimal (which would be unreasonable to expect), but the authors present analyses showing that using spatial clusters does improve their network analysis. Clustering reduces problems with small sample sizes from individual sites and simplifies the network graphs by reducing the number of nodes, which makes the results easier to interpret.

Reference

Sakahira, F. and Tsumura, H. (2023). Tipping Points of Ancient Japanese Jomon Trade Networks from Social Network Analyses of Obsidian Artifacts. Frontiers in Physics 10:1015870. https://doi.org/10.3389/fphy.2022.1015870

Sakahira, F. and Tsumura, H. (2024). Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction, Zenodo, 10057602, ver. 7 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7969330

This is a fascinating paper for anyone interested in network analysis or the chronology and cultures of the case study, namely the Late prehistoric burial sites in Dorset, for which the author’s approach allowed a new perspective over an already deeply studied area [1]. This paper's implementation of Similarity Network Fusion (SNF) is noteworthy. This method is typically utilized within genetic research but has yet to be employed in Archaeology. SNF has the potential to benefit Archaeology due to its unique capabilities and approach significantly. 

The author exhibits a deep and thorough understanding of previous investigations concerning material and similarity networks while emphasizing the innovative nature of this particular study. The SNF approach intends to improve a lack of the most used (in Archaeology) similarity coefficient, the Brainerd-Robinson, in certain situations, mainly in heterogenous and noisy datasets containing a small number of samples but a large number of measurements, scale differences, and collection biases, among other things. The SNF technique, demonstrated in the case study, effectively incorporates various similarity networks derived from different datatypes into one network. 

As shown during the Dorset case study, the SNF application has a great application in archaeology, even in already available data, allowing us to go further and bring new visions to the existing interpretations. As stated by the author, SNF shows its potential for other applications and fields in archaeology coping with similar datasets, such as archaeobotany or archaeozoology, and seems to complement different multivariate statistical approaches, such as correspondence or cluster analysis.

This paper has been subject to two excellent revisions, which the author mostly accepted. One of the revisions was more technical, improving the article in the metadata part, data availability and clarification, etc. Although the second revision was more conceptual and gave some excellent technical inputs, it focused more on complementary aspects that will allow the paper to reach a wider audience. I vividly recommend its publication.

References

[1] Geitlinger, T. (2024). Similarity Network Fusion: Understanding Patterns and their Spatial Significance in Archaeological Datasets. Zenodo, 7998239, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7998239

Studies of the economy of the Roman Empire have become increasingly interdisciplinary and nuanced in recent years, allowing the discipline to make great strides in data collection and importantly in the methods through which this increasing volume of data can be effectively and meaningfully analysed [see for example 1 and 2]. One of the key aspects of modelling the ancient economy is understanding movement and transport costs, and how these facilitated trade, communication and economic development. With archaeologists adopting more computational techniques and utilising GIS analysis beyond simply creating maps for simple visualisation, understanding and modelling the costs of traversing archaeological landscapes has become a much more fruitful avenue of research. Classical archaeologists are often slower to adopt these new computational techniques than others in the discipline. This is despite (or perhaps due to) the huge wealth of data available and the long period of time over which the Roman economy developed, thrived and evolved. This all means that the Roman Empire is a particularly useful proving ground for testing and perfecting new methodological developments, as well as being a particularly informative period of study for understanding ancient human behaviour more broadly. This paper by Page [3] then, is well placed and part of a much needed and growing trend of Roman archaeologists adopting these computational approaches in their research. 

Page’s methodology builds upon De Soto’s earlier modelling of transport costs [4] and applies it in a new setting. This reflects an important practice which should be more widely adopted in archaeology. That of using existing, well documented methodologies in new contexts to offer wider comparisons. This allows existing methodologies to be perfected and tested more robustly without reinventing the wheel. Page does all this well, and not only builds upon De Soto’s work, but does so using a case study that is particularly interesting with convincing and significant results. 

As Page highlights, Northern Italy is often thought of as relatively isolated in terms of economic exchange and transport, largely due to the distance from the sea and the barriers posed by the Alps and Apennines. However, in analysing this region, and not taking such presumptions for granted, Page quite convincingly shows that the waterways of the region played an important role in bringing down the cost of transport and allowed the region to be far more interconnected with the wider Roman world than previous studies have assumed.  

This article is clearly a valuable and important contribution to our understanding of computational methods in archaeology as well as the economy and transport network of the Roman Empire. The article utilises innovative techniques to model transport in an area of the Roman Empire that is often overlooked, with the economic isolation of the area taken for granted. Having high quality research such as this specifically analysing the region using the most current methodologies is of great importance. Furthermore, developing and improving methodologies like this allow for different regions and case studies to be analysed and directly compared, in a way that more traditional analyses simply cannot do. As such, Page has demonstrated the importance of reanalysing traditional assumptions using the new data and analyses now available to archaeologists. 

References

[1] Brughmans, T. and Wilson, A. (eds.) (2022). Simulating Roman Economies: Theories, Methods, and Computational Models. Oxford. 

[2] Dodd, E.K. and Van Limbergen, D. (eds.) (2024). Methods in Ancient Wine Archaeology: Scientific Approaches in Roman Contexts. London ; New York. 

[3] Page, J. (2024). Rivers vs. Roads? A route network model of transport infrastructure in Northern Italy during the Roman period, Zenodo, 7971399, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7971399

[4] De Soto P (2019). Network Analysis to Model and Analyse Roman Transport and Mobility. In: Finding the Limits of the Limes. Modelling Demography, Economy and Transport on the Edge of the Roman Empire. Ed. by Verhagen P, Joyce J, and Groenhuijzen M. Springer Open Access, pp. 271–90. https://doi.org/10.1007/978-3-030-04576-0_13