Using Swords to Understand Communities of Central European Bronze Age Smiths

Open Archaeology 2018; 4: 123–144

Original Study

Kristina Golubiewski-Davis* From 3D Scans to Networks: Using Swords to Understand Communities of Central European Bronze Age Smiths

https://doi.org/10.1515/opar-2018-0008 Received May 23, 2017; accepted November 9, 2017

Abstract: This case study uses 3D scans of Central European Bronze Age swords (~1400–800BC) to recreate community networks of knowledge. 3D scans of 111 bronze swords were analyzed, from which measurements including blade profile, hilt profile, and decorative shape data were collected. The data were analyzed using a variety of statistical methods. Cluster analysis was used to create links between the nodes of networks that were modelled. A community detection algorithm was run on the networks to examine potential communities of bronze smiths based on theorized manufacturing decisions. These analyses suggest there were four distinct areas within which craft workers were sharing knowledge.

Keywords: 3D Scanning, Bronze Swords, Network Analysis, Bronze Age, Fourier Analysis

1 Introduction

This paper presents a case study in which 3D scans of later Bronze Age (~1400–800BC) swords from Central Europe (primarily Germany, Austria, and Hungary; see Figure 5 for a map) are used to examine past networks through which knowledge was disseminated. The swords come primarily from the Bz C, Bz D, and Ha A periods in Reinecke’s Central European chronology (Reinecke, 1899). Though this sample draws on material from several chronological phases, the swords have been grouped together for this study to ensure that the size of groups remains statistically significant. The aim of my PhD research, which this study arises from, was to explore a novel means to model social networks in prehistoric societies. This was accomplished by defining measurable data from artifacts and using these to model networks. The methods that were developed allow one to follow a chain of inference that flows from the archaeological object to technical knowledge of craftworkers to an interpretation of the social networks linking sword smiths. This paper will focus on the methodology of creating these networks based on analyses of specific features captured in 3D scans of the swords being analyzed. The methodology might be seen as a thought experiment on how to utilize quantitative methods, theories on craft worker choices and knowledge dissemination routes, and stylistic typologies which may or may not reflect the decisions of craftworkers. As such, there are some natural extensions to the study which would further develop the arguments presented here and provide a more nuanced view of craft worker networks and potentially incorporate other aspects of Bronze Age society.

Article note: This article is a part of Topical Issue on Exploring Advances in the Use of 3D Models of Objects in Archaeological Research edited by Barry Molloy.

*Corresponding author: Kristina Golubiewski-Davis, University of California, Santa Cruz, 95062 CA, USA, E-mail: [email protected]

In all, 111 bronze swords were scanned, and the models generated were analyzed by taking measurements of specific features and cross sections of the swords. These were considered to reflect outcomes of particular specialized craft knowledge, in particular skills which needed to be learned through repeated practice and would have been known only to a small portion of society (Kuijpers, 2017). A variety of statistical methods were then used to examine the data extracted. The primary analysis utilized elliptical Fourier analysis to mathematically represent cross-sections and profiles of the blades and hilts of swords. Principal component analysis was next used to combine related shape data and condense the number of variables so that cluster analysis could then be used to determine which swords clustered with each other based on the different variable types. These cluster analyses provided the key information used to define links in a social network analysis (Knappett, 2013; Terrell, 2013), where the network consists of weapons connected through bi-lateral links based on shared cluster groups. Finally, a community detection algorithm was run on the networks to examine hypothesized communities of bronze smiths defined on the basis of grouping of distinct features that were considered to represent local traditions of manufacturing decisions.

1.1 Network Analysis

It is increasingly apparent that the connections and networks we experience are an important part of our everyday lives and for society more generally. The term “social network” has become commonplace with the advent of Facebook, Twitter, and other online tools which allow us to connect with people everywhere from our backyard to halfway around the world. While it may seem like these phenomena emerged overnight, the current forms of human networks have been forming for millennia in particular historical and prehistorical circumstances. A network is comprised of points, or nodes, which are connected by links. The nodes can be individuals, groups of peoples or places such as a town, or other entities altogether, such as websites. In the context of human networks, links often consist of familial ties, work ties, friendships, or other relationships. Nodes can be part of multiple networks and can have different types of links to other nodes (see McCulloh, Armstrong & Johnson, 2013 for a more extensive overview of network analysis). The networks to which people belong are complex and change over time, thus their decisions are informed and influenced by a variety of sources (Christakis & Fowler, 2009; Barabási, 2014). By examining the decisions people make, researchers can begin to comprehend what networks may have influenced those decisions and through this, shed new light on how we conceptually seek to reconstruct, or model, the complex networks which connected past societies. The focus of this research has been to reconstruct meaningful nodes and links to examine potential networks in Bronze Age societies using empirical data derived from ancient artifacts – the product of past skilled acts of production. In this case, the manner in which data is used, and the resulting resolution of the node along with the significance of the links developed must be carefully considered. For the link, data which could be seen to reflect technical choices of smiths were used for each sword, because these data are socially meaningful and are therefore informative about past communication networks (Farbstein, 2011). The choices a craftsperson makes when they create objects are informed by their culture and training. Wrapped up in the object are stylistic preferences, cultural meanings, and manufacturing knowledge; some of these decisions arise from cognizant choices while others would have been embedded through enculturation as behaviors learned through interactions over a lifetime (Lemonnier, 1992; Dietler & Herbich, 1998; Stark, 1998). This choice of data to define links enabled me to explore how networks of craft knowledge compared to the distribution of objects characterized through established typologies. While style can and does influence manufacture, the effects of this can be considered by observing the variety of technical choices which produce the same or similar stylistic outcomes. Technical decisions that are not visible enough to be used for social signaling purposes, such as the distance between two parallel lines on a sword hilt, are more likely to be based on behaviors ingrained through repetitive use and habit (Carr & Neitzel, 1995). This focus on technical decisions removes the necessity of establishing (with a high degree of uncertainty) who From 3D Scans to Networks 125 determines the style (the craft worker or the recipient). Instead, it provides a means to examine actions that can be assigned with confidence to a particular set of individuals – in this case, the smiths. This paper focuses on the creation of networks using each single sword as a node and linking them based on metric data derived from each. One confounding factor in this method is that one craftworker (or group of craftworkers) would have created multiple swords in prehistory. This problem can be tackled in part by looking at large amounts of data concerning the manufacture of objects and considering the importance of learned behavior manifested in manufacturing steps when interpreting the network. Since each object was subject to numerous decisions that went into its creation, and those decisions are, to a certain extent, maintained in the shape of the object, then comparing the morphology of these features alongside other manufacturing choices should provide a baseline of similarity between objects. This similarity can then be used to determine a relationship between the objects, with the potential to suggest that certain objects may have come from the same workshop. Framing this in terms of exploring the point of origin for a sword in a given (theoretical) workshop instead of an individual craftworker was intentional, as this allows for the possibility that more than one individual may have been involved in the crafting process. This means of modelling on the basis of workshops as craft locations is also consistent with other works that deal with these materials (Stockhammer, 2004; Mödlinger, 2011).

1.2 Bronze Age Contextualization

The sword smithing process and knowledge of that craft was informed by the everyday life of the smith and the social constructions around them. The Middle to Late Bronze Age in temperate Europe roughly refers to the years between 1400–800 BC. During this time, bronze was used for axes, swords, shields, sickles, scythes, figurines, and jewelry. The integration of bronze as a medium to make artifacts that had long been made from other materials, such as clay and stone, increased gradually throughout the entire Bronze Age (circa 2000–800 BC). The Late Bronze Age is distinguished from earlier periods by an intensification of agriculture, an increase in metal production, regionally significant shifts in burial practices, and the increased construction of sites that had some defensive features, including hillforts (Wells, 1989). For much of Central Europe, the shift in mortuary practices from inhumation as a burial rite to cremation of individuals deposited in pots is interpreted as a change in religious practices and beliefs (Sørensen & Rebay, 2005). Bronze swords were cast using an alloy of approximately 10% tin and 90% copper. The metal was melted in a crucible and then poured into a two-piece mold, typically sandstone or clay, to make the weapon. For swords that had bronze hilts, these were cast using the lost-wax casting technique. The hilt can be cast directly onto the blade or attached later with rivets. Simple bronze swords with organic hilts and little decoration begin to appear in the archaeological record circa 1600 BC. The use of bronze hilts appears to have increased in tandem with the use of lost wax casting and multi-part molds. For a more detailed discussion on the bronze casting process see Tylecote (1987), Craddock (1995), Kuijpers (2008) or Mödlinger (2011). Kuijpers and Mödlinger also discuss tools used in decorative processes at length. Swords were probably used both as weapons and as status markers. The conduct of warfare using metal weapons is visualized in rock art depicting interpersonal combat, often single combats (Anati, 1961; Coles, 2000), has been explored through experimental and metallurgical analyses (Kristiansen, 2002; Mödlinger & Ntaflos, 2009; Molloy, 2017), and has been recognized through injuries found on bones (Mödlinger, 2011). Weapons also have been interpreted as probable status markers for powerful individuals (Kristiansen, 2011).

1.3 Previous Works on Sword Networks

The organization of sword typologies has gone through several adaptations. Naue’s 1903 publication set an early standard for the classification of bronze swords. Today, many Central European bronze swords are cataloged in the Prähistorische Bronzefunde (PBF) series (Prähistorische Bronzefunde 1970– 2015). Abteilung IV, the volume dedicated to bronze swords, includes 19 volumes organized by modern- 126 K. Golubiewski-Davis day political boundaries. Each item is cataloged with the following information: detailed typology, find location, other objects associated with the sword, length, where the sword is currently housed including accession number, any publications that reference the sword, and a drawing. The weapons are organized by an overall type, a subtype, and in some cases a variant of the subtype. Many of these volumes use an adaptation of Naue’s typology developed by Müller-Karpe (Wüstemann, 2004). Types are defined by the handle shape with subtypes and variants separated out by decorative motifs (Krämer, 1985). The typologies discussed in the PBF can vary based on the sample of the geographic area and the detailed categories of the author. Categories of full or solid-hilted sword in this typological series included in this study are: Riegseeschwerter, Dreiwulstschwerter, Achtkantschwerter, Mörigenschwerter, and Schalenknaufschwerter. These were consistent type categories across the volumes consulted (Vols. 9, 10, 11, 14, 15, and 17) (Krämer, 1985; Kemencezei, 1991; Harding, 1995; Von Quillfeldt, 1995; Wüstemann, 2004; Laux, 2009). For the sake of consistency, I used the archaeological typology as determined in the PBF as a marker for stylistic similarities between swords. Typological studies often focus primarily on stylistic differences between groups of objects. While this is useful for understanding symbolic signaling between groups (which can be either active or passive), it is difficult to use those groupings to understand how craft workers may have interacted with, and therefore reproduced, such style choices. It is important to consider how much of the decorative choice was in the hands of the smith and how much was dictated either by custom, cultural group, or customer. If smiths were highly itinerant, were they making swords of different styles, or did they specialize in certain types? These questions are difficult to answer when the typology is purely based on the visual aspects of the object. Stockhammer’s 2004 re-evaluation of typologies addressed this issue by orienting the hierarchy on craft decisions instead of stylistic combinations of decoration. To do this, he examined micro-styles, or decisions that the craftsperson made that did not affect the stylistic whole or the functionality of the weapon. By considering micro-styles which are beyond the immediately visible portion of the weapon, Stockhammer moved the act of creating a typology away from characteristics that could be driven by decisions made by custom or customer. Instead, the typology developed put a greater emphasis on decisions for knob type, followed by hilt profile, and finally a combination of decorative choices and micro-styles. While Stockhammer’s typology was not used in this study in order to keep the group numbers large enough to be statistically viable, a better integration of the two methods could lead to useful results, particularly in examining the overlap of workshop groups. When considering the geographic recovery patterns of swords, it is important to take account, where practicable, of how weapons were used, traded, and deposited. For example, areas with fewer metal hilted swords may reflect a cultural practice which kept items in circulation rather than burying them in hoards or burials (Sicherl, 2004). It is also important to consider both the overall shape and style of the sword as well as the ways in which decorative elements were combined. Differences in the materials used and the decorative markings of Dutch Achtkantschwerter suggest that local copies of a typically more southern style were probably made by different craftsmen (Sicherl & Brandherm, 2001). In some cases, it is probable that swords were imported into an area and then decorated at a later time (Bunnefeld, 2014).

2 Methods

The dataset consists of 111 swords that were 3D scanned using a David SLS 2 structured light scanner. Additional data for each specimen were collected from the PBF. An online supplementary material to this paper includes a summary of the data collected for each sword, including the PBF catalogue numbers and museum accession numbers. The identification number of each object in the supplementary data is comprised of the Praehistorische Bronzefunde (PBF) volume number followed by the sword number in that volume. The series number is IV for all of the volumes related to swords in the PBF. For example, blade 09.005 can be found in PBF series IV volume 9 under the number 5. Additionally, the statistical codes and network files have been published to the UMN DRUM repository for distribution (Golubiewski-Davis, 2016). From 3D Scans to Networks 127

2.1 Data Collection

The David SLS2 is a structured light scanner that projects patterns of white light on an object and uses the distortion of the patterns from a known point to calculate x, y, z points for the surface of the object. These points are then used to create a 3D mesh; in this case the mesh was of the swords being studied. The highest resolution of the David scanner is .05mm when calibrated using the smallest calibration panel. If there was decoration on the hilt, this was the resolution used for that portion of the scan. In a typical scanning setup, the object is set up on a turn table and rotated to capture a scan from multiple angles. This type of setup was not a viable option given the long, narrow, and pointed nature of swords. Instead, a series of notched foam blocks were used to rotate the swords approximately every 30 degrees by turning the object backwards and following the numbers on the blocks (Figure 1). The scanner was then moved along the length of the hilt to capture the next portion before turning the sword, since the higher resolution wouldn’t capture the entire area all at once. This produced a large number of scans (typically a minimum of 36 scans for the hilt alone). While scans for the blade were collected, they were ultimately not usable due to an inadequate amount of overlap on the blade portion causing alignment issues. The scans were then edited by hand to remove miscellaneous data, aligned, and merged in Geomagic X.

Figure 1. Blade propped up on notched foam blocks for 3D scanning.

Both continuous and categorical data were collected. The categorical data collected from the PBF series include the archaeological type (or style), the find type, whether the sword is broken or not, and the museum accession number. The style of the sword used was the highest level of archaeological type classification available for each Vollgriffschwerter. This level of classification was chosen as it was the level most likely to remain consistent across books. The typologies included in the sample included Dreiwulstschwerter, Schalenknaufschwerter, Achtkantschwerter, Riegseeschwerter, Mörigenschwerter, Scheibenknaufschwerter, and Regály schwerter. Some of the swords were not categorized with specific types. Find contexts included cremations, inhumations, hoards, dryland (non-specific), in rivers, as single finds, and other or unknown contexts. Continuous data include all of the numerical data collected for this study. Data from the PBF series include length of the blade, location information, profile data, and cross section data. The remaining continuous data were collected from the 3D scans as described below. All measurement data from the 3D scans were recorded in millimeters. 128 K. Golubiewski-Davis

Cross sections and profiles from the hilts were extracted from the 3D scans. External hilt profiles were extracted from the 3D scans in 2 perpendicular orientations: one from the side perspective of the hilt and one from the top perspective. Information about the inner cavity of the hilt, while useful, cannot be extracted from this type of scanning method. Each hilt was aligned so that intersection of the hilt and the blade defined the z plane. The x and y plane were aligned so that it best bisected the hilt (Figures 2 and 3). An additional plane was inserted to take the top profile which was defined by the point of inflection on the profile. This alignment configures each sword in the 3D space enabling a consistent placement and thus comparison between the swords.

Figure 2. Placement of the z plane.

Figure 3. Placement of the x and y plane. From 3D Scans to Networks 129

Cross-sections and profiles for the blade shape were extracted from images in the PBF books. As the images were drawn by hand, these data are subject to variances by the original illustrators. Additionally, cross sections were not defined and drawn in a consistent manner between, or sometimes within, books, nor are all swords accompanied by an illustration of the cross-section. These limitations are noted, and in ideal circumstances, the blade information would come from 3D scans as well. Ultimately, the blade profiles were used and the cross sections were not. While this does provide for problematic, or at least incomplete, data, these details were left in as a way to examine how hilt and blade shapes might provide similar or different network patterns based on distinct portions of the sword potentially crafted separately from each other with the caveat that a higher fidelity of data would provide more accurate results in a more statistically significant manner. The extracted profiles were processed using SHAPE v1.3 (Iwata & Ukai, 2002) to determine the Fourier transform descriptors. A Fourier transformation represents a shape mathematically as a series of sines and cosines. These data can then be used to approximate the shape of the profiles and cross sections as numerical, continuous data (Kuhl & Giardina, 1982). For an example of this type of analysis on stone tools, see Ioviţă (2009). To do this, each illustration was scanned on a flatbed scanner and edited to a black and white representation of the outside of the blade. The profile was stopped at the widest part of the blade before it cuts in for the tang. The top part of a hilt profile was defined by the widest part of the pommel disc. For the bottom portion, the shoulder of the hilt was kept up until the point where the shoulder and the blade intersected. These images were processed through the ChainCoder program and converted to a total of 20 Fourier harmonics (A-D for each, making a total of 80 variables per profile) based on the first harmonic option using the CHC2NEF program. Both programs are part of the SHAPE suite of software. Decorations incised on the hilt were documented as numerical data, termed “decorative data”. For each sword hilt, the presence or absence of different types of decorative shapes was recorded. In the end, the following categories appeared on more than 20 hilts: concentric circles (34), dashes (27) parallel curves (21), parallel straights (66), and waves (21). Refer to Figure 4 for an example of each of the types of measurement used to document and thereafter differentiate each of these. Note that these are unique descriptors and not based on the manner in which they are categorized in the PBF series. For each decoration type, I determined a number of variables that I felt quantified the shape in systematic fashion. Table 1 summarizes the measurements taken. In order to help take measurements, I used the curvature feature in Geomagic X. This feature uses a heat map visualization to differentiate the peaks and valleys of the geometry for better accuracy during measurement (Figure 4). Decorative data were only taken from one side of the hilt. No decorative data was extracted from the blade portion.

Table 1. Variables measured for each type of decoration. Cross section data shows the number of principle components that make up 95% of the variation.

Data Type Variables

Blades Prin1-Prin2 of Fourier co-efficients Cross Sections Prin1-Prin5 of Fourier co-efficients Hilts Prin1-Prin11 of Fourier co-efficients Rivets Prin1-Prin5 of Fourier co-efficients Circles Radius, distance between circle centers, distance between circle lines Dashes Length, distance between dashes Parallel Curves Distance between curves Parallel Straights Distance between parallel straights Waves Wave width, wave height, distance between wave centers Length Length Tang Height Tang Height 130 K. Golubiewski-Davis

Figure 4. Examples of each type of decorative shape data measured. From 3D Scans to Networks 131

Location data were used in this study to compare differences based on the distance between where the swords were found. Each find location was converted to a latitude and longitude data point using the Latitude/Longitude Finder tool provided by My Nasa Data (Latitude / Longitude Finder). Latitude and longitude values are based on the center of the town rather than the exact location; however, these data are sufficient for comparing swords across different areas of Central Europe. Figure 5 shows a map of where the 89 swords with known locations were found, based on the latitude and longitude of the city or town of their find locations. The remaining 22 swords with unknown locations were given a null value for statistical analysis, though their location was estimated based on correlations between the hilt profile and location as described below. Rivet placement was taken in an XYZ form from the 3D data. XYZ placement for the 2 rivets were recorded for each relevant item as well as the linear distance between the two points. An average point was taken from all of the geometry that comprises the rivet in Geomagic X. The final piece of data extracted from the 3D data was the tang height. This measurement is the width of the blade where the hilt meets the blade along the central axis of the blade.

Figure 5. Find spot of all swords in the sample with known find locations. The map was generated using CartoDB (CartoDB). 132 K. Golubiewski-Davis

2.2 Statistical Analysis

A large number of variables were collected per weapon. For example, a single blade profile Fourier transform includes 80 variables. Given the maximum number of swords per comparison as 111, which is further split between several groups for analysis, it is important to narrow down the number of variables used. One way of doing this is through principal components. Principal component analysis (PCA) is a rigid rotation of the data. This means that the data are rotated in such a way that the variability is maximized on the first axis. The relationship between the variables does not change, only the way that they interact with the axes (Holland, 2008). PCA was applied to reduce the number of variables for the blade profile, cross section profile, the hilt profiles (both top and side were considered together for analysis), and rivet placement (x, y, z and the linear distance between the rivets). The PCA outputs the amount of cumulative variance that can be explained with each new axis. For this study, I used only the principal components that make up 95% of the variation for a particular set of variables. Cluster analysis was used as an exploratory method for determining how the data cluster along different variables. Cluster analysis is a type of analysis that quantifies where the data vary and splits the data into groups based on which points are more similar to each other. The first step of cluster analysis is to prepare the data by determining how each point differs. Part of this process includes transforming the data to remove scaling and account for covariance. The following assumptions were made in relation to the data: –– variance between groups is not equal –– clusters are more likely elliptical in nature.

These assumptions were made based on the limitations of the sample and the unlikeliness that all groups varied exactly the same across the geographic region. Clusters were determined using the ACECLUS procedure in SAS software (approximate covariance estimation for clustering). The ACECLUS procedure can be done without the need to know cluster membership or the number of clusters in advance of analysis. One of the outputs of this procedure is a graph of the cubic clustering criterion (CCC), pseudo F (PSF), and t2 (PST2) statistics. These are used to help determine how many clusters should be used (SAS Institute Inc. 2016). CCC variables and pseudo F suggest clusters when the numbers peak or where they level out. The t2 statistic is indicative of clustering at the number just after a peak. Figure 6 shows where the blade profile data may indicate significant numbers of clusters. Numbers that appear in two or three of the graphs are the ones chosen for a first look at the number of clusters. In this case, 7 clusters were used for the analysis. If outliers were present in the scatterplot of the first two canonical values (calculated during the ACECLUS procedure), they were removed. The focus of this project was to look at how objects are similar, so outliers like these were removed and the cluster analysis was re-run. It would be beneficial to examine outliers in a separate study. Clustering analysis was done based on location, blade profiles, cross-section profiles, hilt profiles, rivet placement, and the decorative data (both individually by type as well as combined). The relationship between profiles and location was used for regression analysis. While blade profile did not significantly correlate to location, the hilt profile did with a p value of <.0001. Due to this correlation, known locations could be used as a baseline to create a regression formula which can be used to estimate the location of a sword with an unknown find spot. This estimation uses all of the swords with known locations to develop a formula that will create a best guess of coordinates for swords with an unknown find locations. The estimated latitude and longitude were used only for displaying the swords on a map. If the artifact did not have a known find spot, it was not included in the location clusters or any other analysis that used latitude and longitude. The formula is presented below. When running swords with known locations through the formula, latitude has an average error of .9661(+/-1.0474) degrees and longitude has an average error of 2.5634(+/- 2.1428) degrees. Lat=48.31197-3.13234*HPrin1+8.63701*HPrin2-10.03307*HPrin3+2.88708*HPrin4-7.02584*HPrin5+4.620 69*HPrin6+14.51688*HPrin7-9.39206*HPrin8+24.01061*HPrin9-12.70149*Hprin10+7.26966*HPrin11 Lon2=13.78801+9.35802*HPrin1-21.78429*HPrin2+18.06027*HPrin3+26.10363*HPrin4-27.50622*HPrin5+ 3.90810*HPrin6+14.97578*HPrin7-53.59689*HPrin8-33.28697*HPrin9+23.31321*HPrin10-4.56811*HPrin11; From 3D Scans to Networks 133

Figure 6. Graphs returned using the ACECLUS analysis for determining the number of clusters to use. Vertical lines were added by the author for demonstration purposes. This example uses principal components for the blade profile and shows a likely number of 7 significant clusters.

2.3 Network Creation

Links of the network were established using similar clusters as an indication of a network link. Two types of networks were created; one network used individual swords as the nodes and shared clusters as the links, and the second network used the blade or hilt clusters as the node and the inverted minimum spanning tree (MST) value of the distance between the means of the clusters. For the clusters, the distance between the group means were calculated using ANOVA (Analysis of Variance) or MANOVA (Multivariate Analysis of Variance) values as appropriate. From these, the differences in means were analyzed using a tukey adjustment. This type of adjustment is used to help eliminate Type 1 error across unequal groups and is appropriate for unequal groups of unknown size (Dallal, 2012). A Type 1 error is a false rejection of the null hypothesis. These numbers were used to create a minimum spanning tree. Since the MST interprets higher numbers as objects that are weakly connected, and network analysis interprets higher numbers as a stronger bond, those measurements had to be flipped. The matrices were inverted using the following formula: New variable = largest value + smallest value – original value. This formula results in a change where the largest number becomes the smallest number and each value in between inverts according to that scale. A weight of 1 was assigned every time two swords shared a cluster. Thus, every sword included in the cluster containing all of blade profile 1 shares a link with a weight of 1. Likewise, every sword included in the first cluster of hilts would contain a separate weight of 1. Separate networks were created for each decoration using clusters based on shape data for blade profiles, hilt profiles, concentric circles, dashes, parallel curves, and parallel straight lines; in this case, each link was given a weight of 1. Secondary matrices included a combination of the original matrices where a weight of 1 represented either a shared blade or hilt cluster and a weight of 2 represents both a shared hilt and shared blade cluster. Finally, combined 134 K. Golubiewski-Davis matrices were created using blade and hilt data, all decorative data, and a combination of blade and hilt plus decorative data where the link weight ranged from 1–8 based on the number of clusters shared. These data are available in the supplemental DRUM files (Golubiewski-Davis, 2016).

3 Results

Two types of network depictions appear in the following section. The first places each node on a map using latitude and longitude. Clusters are represented by the mean latitude and longitude of component swords with known find spots. Individual swords are represented by the latitude and longitude of their known find spot or placed using the regression formula discussed previously. The second type of network depiction is a force atlas display. This is a method of displaying a network such that links that are more related are closer together and those that are different are further apart. Additionally, groups that are highly connected and cluster pull together towards each other and push away from other points that are not in the cluster. This presents a visual representation of similarities and differences both spatially as well as through the placement and strength of the lines connecting them.

3.1 Blade Clusters Vs. Hilt Clusters

The MST of the hilt network is expressed as a classic hub and spoke system, with hilt group 10 at the center (Figure 7). In comparison, the blade network is weakly connected and sprawls along in a linear fashion (Figure 8). This is reinforced when the network is mapped out using all of the connections and not just the minimum distance between groups (Figure 9). In this case, the shape of the networks is similar, but the links between the blade clusters remain weaker with no clearly stronger links across the nodes. These divergences in the hilt and the blade network may suggest that they were utilized differently. It is also possible that the data on the blade profiles have obscured any network information due to inaccuracy, though it should be noted that there were significant differences between the blade clusters after a MANOVA test suggesting some level of fidelity to the data.

Figure 7. Network graph using the MST of hilt clusters using the Force Atlas layout. From 3D Scans to Networks 135

Figure 8. Network graph using the MST of blade clusters using the Force Atlas layout.

Figure 9. Network graph of hilt clusters (blue) and blade clusters (red) using the Force Atlas layout. 136 K. Golubiewski-Davis

3.2 Individual Sword Comparisons

Relationships between swords can also be shown by looking at the shared decorative clusters (Figure 10). This network graph was created by applying a force atlas layout based on all of the decorative data. Next, the links were filtered to only show connections between swords that share two or more clusters. Groups that appear clustered, but have no nodes, are a cluster that share only one type of clustered group. The Schalenknaufschwerter appear in this network as having a stronger connection than other swords. The stylistic elements of the Schalenknaufschwerter typology are distinctly different in overall morphology than the other types in this study; thus the most likely explanation is that the style influences the shape to a point where the subtle differences of function and craft skill are no longer discernible between this particular style and others. The thick lines indicate three shared clusters between the groups, with one pair sharing four cluster groups. Swords that have more than three decorative links are mapped out in Figure 11. While shared typological style certainly contributes to multiple shared clusters, it is notable that some of the shared clusters cross stylistic boundaries. All network visualizations and statistics were undertaken using Gephi 0.8.2 (Bastian et al., 2009). Artifacts 10.098 and 9.223 are marked on the map, as they share a total of four decorative clusters. When combining the decorative data with profile data, swords 11.040 and 11.042 are also closely related, sharing a total of four clusters (two profile clusters and two decorative clusters). These two sets of swords are the most likely to have come from a single workshop, with the distance between them being the product of long distance trade.

Figure 10. Force atlas projection of individual blades connected by shared decorative data clusters. From 3D Scans to Networks 137

Figure 11. Map of individual blades that share three or four decorative clusters.

3.3 Community Detection

Network Modularity (Blondel et al., 2008) is a measurement of how sections within a larger network form into smaller sections that are more closely connected with each other than other parts of the network. In the case of this present study, this measurement is a way to examine smaller communities that may be present within the larger community of bronze smiths. A large, interconnected network of all the swords was created using the profile information and the decorative cluster information. For this network, a link could have a weight strength between one and seven. A link weight of seven between two swords would mean that those two swords shared the same cluster for every type of measurement. Three pairs of swords had a link weight of five, the highest of the sample: 9.201 & 9.207, 9.223 & 10.098, and 11.033 & 11.035 (Figure 12). These objects share a significant number of clusters and are more likely to have come from the same smaller community of smiths, potentially even the same smith. 138 K. Golubiewski-Davis

Figure 12. Swords which contain five shared manufacture based clusters. Each vertical box contains one pair with shared clusters.

At a resolution of one, four communities are detected in the network (Figure 13), with the unconnected sword making a fifth group. When the resolution is decreased to show smaller groups, 8 communities are detected (Figure 14). Groups one and two from the original set remain mostly intact, corresponding with two and six respectively. The additional four communities are composed of subsets of the original groups three and four. This shows that groups one and two are more strongly connected than groups three and four. Group one stretches across modern day Hungary (Figure 15). Group two is composed primarily of Achtkantschwerter. The strength of group one may be an indication of a strong long distance trade group with fewer smiths than the other groups. At this point, it is worth examining how archaeological typologies and stylistic elements of those typologies, collectively called style here, are represented in the above groupings. Given that some styles are grouping together, it is clear that the stylistic elements do play a role in how these statistical communities are formed; however, all of the networks include multiple archaeological style types. This combination of styles suggests that there are functional elements of crafting technique that span across these styles. Therefore, it is the opinion of the author that smiths would have worked on blades of different style groups instead of specializing in one type. Given that the chronology of the swords in this study spans multiple centuries, it is probable that these groupings represent crafting techniques that would have been unique to groups of craftworkers and passed down through time. A larger sample would disambiguate chronology by allowing statistically significant groupings within smaller time groupings and provide clarity into how space and time affect these groupings, though the variables are by necessity of sample size combined in this particular study. From 3D Scans to Networks 139

Figure 13. Communities in the network detected using a resolution of 1.

Figure 14. Communities in the network detected using a resolution of .8. 140 K. Golubiewski-Davis

Figure 15. Communities mapped geographically. Colors represent the sub community in the network and shapes represent style typologies.

4 Discussion

Communities in the Bronze Age were made up of multiple layers of networks between individuals and groups of people maintained over time. Smiths existed within a small-scale settlement community, and participated in the day to day activities of Bronze Age life. They also would have been part of the larger-scale networks required for acquisition of copper and tin. Bronze smith communities likely consisted of individuals from different villages that met up on occasion, but spent the majority of their time working alone or in small groups (Rowlands, 1971). This type of fission- fusion community, where individuals spend most of their time apart (fission) but come together on certain occasions (fusion), is supported through ethnographic research of modern day metal workers, such as the Awka iron workers (Neaher, 1979). Activities such as the acquisition of ore and conditioning wood to be used as fuel through drying and burning that required greater effort would benefit from economies of scale. From 3D Scans to Networks 141

The networks presented here are a representation of the larger groups that would have gathered for these events. These communities existed in overlapping geographic areas and span stylistic groups. This type of overlapping is similar to the workshop circles discussed by Mödlinger (2011) and Stockhammer (2004). The intermixing of shared manufacturing networks, specifically in the choice of how the shape is expressed on a micro-scale, suggests that there was a sharing of knowledge between craft workers as opposed to just the weapons (or users thereof) moving around. In the case of Achtkantschwerter, Bunnefeld (2014) and Sicherl and Brandherm (2001) have shown that there are probably cases of swords traveling northwards into the Netherlands and being copied or decorated at a separate time. The swords in this sample come primarily from further south in Germany, Austria, and Hungry; it would be useful to expand the collection to study how those networks would interact using the framework provided here. Multiple networks would have been maintained through different group activities. In particular, the divergence between hilt and blade networks could be interpreted as an indication that different groups were making each of these elements of swords independently. Of the two parts of the sword, the blade is easier to reproduce and create in bulk. It is not unlikely that multiple blades were cast in a single session when the materials were available. Alternatively, the blade portion could have been re-used from earlier swords with newly added hilts as has been demonstrated in a number of cases by Mödlinger (2011). Further examination of my existing data, along with more data collection covering a longer time-span, would help to test these scenarios. The examination of networks maintained by bronze smiths can be used to illustrate how interaction occurred between communities, integrating into discussions of long-distance networks observed through the trade of exotic materials (Kristiansen, 1998 p. 63–123). The networks and correlations shown here have scope to be enhanced through further development, first and foremost of which would be to obtain a larger sample size of decorated swords. This would provide higher group numbers, higher fidelity when comparing overall shape to decorative shape, and enough observations to justify subdivision into smaller temporal groups. The data set might also be expanded and improved by obtaining reliable 3D scans of the blades and of organic hilted swords (both contemporaneous as well as earlier). If the networks remain constant when comparing between different subdivisions of the Bronze Age, that would show network maintenance and stability over time. Additional measurements that relate to the smith’s skill in bronze casting can include the width of the hilt walls (obtainable by CT scans) and the balance point of the sword. Finally, there are a number of social network measurements that were not examined here (such as centrality) that could be further developed. The methods presented here are rooted in material culture theory and network theory. These methods can be used on multiple types of objects that require specialized craft knowledge to produce. While the intention of this project was to use 3D scan data in a novel way, network analysis is not limited to this type of data (Blake, 2014; Brughmans, 2013; Knappett, 2013a; Mills, 2017). Similar analysis could be made using other methods of quantifying decision making to reconstruct connections based on shared knowledge of specialized production techniques. In conclusion, through the use of quantifiable data collected from publications and 3D scans of the swords, it was possible to distinguish statistically meaningful clusters of swords which span typological groupings. These comparisons offer insights into how craft practices constitute observable groups that are not always evident in modern typological studies. Looking at swords from Central Europe, four groups were determined based on craft similarities, and only in one case did the craft similarities and typological category overlap completely. Though a provisional and exploratory study, these analyses demonstrate the ability to explore social relationships through artifacts in a manner beyond conventional typological methods and related ways of thinking, specifically intended to leverage the application of new technologies such as 3D modeling.

Acknowledgements: Dr. Peter S. Wells, Dr. Gilbert Tostevin, Dr. John Soderberg, and “Ironguy” Wayne E. Potratz provided invaluable guidance for this project. Thank you to the following institutions for allowing access to the items in their collections: the Landesmuseum Württemberg; the Archäologische Staatssammlung München; the Arheološki Muzej U Zagrebu; the Landesmuseum Hannover; the Magyar Nemzeti Múzeum; the Universalmuseum Schloss 142 K. Golubiewski-Davis

Eggenberg; the Tiroler Landesmuseen Ferdinandeum; the Oberösterreichisches Landesmuseum Linz; and the Landesmuseum für Vorgeschichte Halle. Thank you to Barry Molloy for inviting me to be a participant in the workshop and the publication as well as his invaluable help and guidance during the publication process. The work presented here was funded by the following organizations: the University of Minnesota Anthropology Department; the University of Minnesota Center for Austrian Studies; the Hella Mears fellowship from University of Minnesota Center for German and European Studies; and the Wenner-Gren Foundation.

Abbreviations

PBF: Prähistorische Bronzefunde PCA: Principal Component Analysis MST: Minimum Spanning Tree

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Supplemental Material: The online version of this article (DOI: 10.1515/opar-2018-0008) offers supplementary material.