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Remodelingthe Past

PreliminaryExcavation andAnalysis of the ArtifactDistribution, andRecreation of AncientLiving Floor UsingArcGIS

James McGinty

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During the summer of 2013 a preliminary excavation of Na Včelách, a hypothesizedGreat Moravian Era (9th century) site in the hinterlands of Pohansko, Czech Republic wasconducted. Investigation of this site is part of an ongoing research project which that focuses onthe first Slavic state and one of the first principalities in central Europe to be converted toChristianity. During the early 10th century the Moravian State swiftly collapsed; a betterunderstanding of the hinterlands will provide insight into the collapse of the empire. Thisproject sought to understand what the two and three dimensional spatial trends in the artifactdisposition could tell us about the site. During the excavation we used a total station to pointprovenience the excavation data, including artifacts and feature lines. The analyses wereconducted using the ArcGIS mapping software to analyze and interpret the preliminaryexcavation data. The density distribution analysis identified several trends in the clustering,while interpolations with respect to the artifact elevations showed a clear north to south trendin the deposition of the artifacts. The results of this project support the hypothesis that NaVvčelách is in fact a domestic hinterland site of Pohansko. While the preliminary excavation didnot provide enough evidence to support one theory of collapse it provides a solid foundation toguide future investigations of the site.

Brief History of Great Moravia:

Great Moravia was a state-level polity in Central Europe during the early Medieval era. The

main area of political control is in modern Czech

Republic and Slovakia, with a fluctuating periphery

encompassing parts of Hungary, Poland, and Austria

(Macháček 2013:235)(figure 1) . Great Moravia,

unfortunately, is woefully undocumented

considering its historical relevance, beyond

mentions of border conflict with the Carolingians,

and a sprinkling of other documents such as a Papal

letter from Pope John VIII (Štefan 2011:333). These

documents fail to provide much detail about Great

Moravia, leaving archaeological evidence as the primary source of information on the

Figure 1: The Empire of Great Moravia the coreterritory is red and the green is the height ofexpansion. Photo Courtesy Jiří Macháček(Macháček 2013:3)

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state (Seton-Watson 1965:12). Even though Great Moravia remained relevant for roughly a

century, it is widely considered to be the first Slavic state, and is thus a source of pride and

nationalism for many Slavic peoples (Steinhubel 2011:18). The lack of solid historical

documentation has led to debates between scholars ranging from the organization of the society,

to the cause of the collapse of the society (Štefan 2011:340-349). It is known that in the early

800’s C.E. Great Moravia became a Christian nation under frankish rule. However in the mid

800’s Rastislav the prince of Great Moravia expelled the Frankish clergy and was awarded a

mission from the Byzantine church. It

was in 863 C.E. That Cyril and

Methodius arrived in Great Moravia

and paved the way for Great Moravia to

have its own Bishop, thus solidifying its

place as an independent state. The

Cyrillic language is also linked back to

this mission, as Cyril brought with him

some of his selected translations from

the bible during this mission (Sláma

1996: 38-50). By 869 C.E. Methodius had

been appointed the Archbishop of

Pannonia and Great Moravia had truly

become a formidable political entity in

Central Europe. (Sláma 1996: 54)

Perhaps the most significant statement of the independence of Great Moravia though came

from a Papal Bull issued in 879 that allowed the preaching of the gospels in Slavonic and

Figure 2: Photo showing the enclosure ofPohansko with the former wall hilighted inblue and the location of the churches hilightedin red. It also is showing where past researchhas been done in the area, both geophysicalprospection and excavations. Photo Courtesy:Petr Milo (edited by author) (Milo 2011:81)

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recognized Great Moravia as an independent state. (Sláma 1996: 62) Great Moravia reached its

zenith by 890 C.E. Under the rule of Svatopluk. By 894 Svatopluk died, although the decline of

Great Moravia’s power had already begun. By the early 900’s C.E. Great Moravia as a unified

state had fallen apart, with the church losing its monopoly on the religious practices of the

region, as pagan cults began to reemerge (Sláma 1996:68-74).

Pohansko was a walled enclosure site that occupied about 28 hectares and was the

purported local center for trade, and production of a wide variety of crafts, including

metalworking and textile manufacturing (Machaček et al. 2007: 131-135). Pohansko was also the

seat of the local Magnate who resided in a court that was located in the middle of the site with a

surrounding palisade (Sláma 1996:36). It was also a local center of religious power, as there are

two churches associated with the site, one in the central enclosure and one just north of the wall

(Macháček 2011: 19-27). (Figure 2) Pohansko also served another role as a defensive outpost and

regulating center for the long-distance trade that moved along the Dyje and Morava rivers

(Macháček 2011: 27-30). The excavation that took place in the summer of 2013 was undertaken

in an effort to determine if the area Na Včelách was in fact a hinterland settlement location that

participated in Great Moravian society, perhaps as a farming village. If we can learn more about

the population that lived outside of the central enclosures, we will not only be able to fully

support the hypothesis that the enclosures were receiving essential goods, such as grain from

the hinterlands, we will also gain insight into identifying why Great Moravia was such a short

lived society.

Brief Summary of Past Research at Pohansko:

Past archaeological research has been conducted at Pohansko for several decades,

starting in the late 50’s and has yielded a wealth of information on the many facets of society

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there. In the first decade of research the most significant finds included several hundred graves

around the foundations of a church, while several other excavations investigated the walls of

the enclosure and several locations within the enclosure, totaling about 10% of the entire area

contained within the walls (Macháček 2011:18-20). For the first several decades, research was

primarily focused on investigating the enclosure site and area immediately around it (Macháček

2011 :18-21). These investigations have provided invaluable insight into the function and

activities that were concentrated around the center, such as identifying a second church with

another graveyard just outside of the enclosure, (Macháček 2011: 25) metal working centers

(Macháček 2007: 176-178), and residential zones (Přichystalová 2011: 76-81). However, there is a

distinct lack of knowledge about how the people who lived in the center were sustained. It was

not until the past few years of research that the hinterland has become a larger target of

investigations. Limited past rescue excavations have yielded some evidence of farmsteads,

supporting the hypothesis that the hinterlands supplied foodstuffs, among other essentials, to

the main enclosure (Macháček 2011 :24). In recent years, in addition to the increased geographic

scope of interest, new techniques are being employed, such as dendrochronology (Dresler 2008)

and geomagnetic prospecting (Milo 2011). With the increased interest in new methodologies,

GIS has become an essential tool used to collect and analyze the current research that has been

and is being conducted (Macháček 2011 :25).

Summary of the 2013 Excavation at Na včelách:

In the summer of 2013 I was a teaching assistant for the Czech American Archaeological

Field School, run by the College of DuPage, which was tasked with the preliminary excavation

of the hinterland area called Na Včelách. During the five weeks two 5x5m units and two 2x2m

units were excavated; however one of the 5x5m units was abandoned in order to focus the

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limited resources in time and manpower in completing the other three units. The abandoned

unit was dubbed “Brian”, the remaining 5x5 was named “Stewie”, the 2x2 units were named

“Lois” and “Cleveland”.

The first task upon arrival to the site was to conduct a test pit survey of the area in order

to identify where to place the units. The location of the units was chosen from a combination of

the results of the test pits and a need for a clear line of visibility for the laser transit station that

was used with a stadia rod. These aided in the recording of excavation floor levels and artifact

depth (it is worth noting that all of the test pits returned some artifactual material). The

excavation units were subdivided into 1x1m squares with excavation teams made of two to

three people who either excavated or sifted the excavated material. After the sod layer was

stripped the units were primarily dug using shovel skimming or hand trowels. The excavated

material was screened with a quarter inch mesh to maximize recovery of cultural material.

However, due to the high clay content coupled with a season of heavy rain, excavation was

slow and the soil had to be forced through the screens rather than sifted through.

At every level or when a feature was recognized a standard field assessment sheet was

completed, including a sketch map and Munsell soil description, along with any particular

anomalies that were encountered during the level. Elevations were recorded in the four corners

and center of each unit using a laser transit and stadia rod. The topsoil was a plow zone that

was stripped without screening. After the topsoil was removed the excavation proceeded at

10cm arbitrary levels until the Moravian cultural layer was encountered. The Moravian cultural

layer was identified by changes in the color and texture of the soil (fromMunsell 10YR 2/2 to

10YR 3/2 Silty Clay), and encountered throughout the site at a depth of roughly 36cm below

surface level. The 5x5m units were broken into four 2x2m squares in the corners partitioned by

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a 1x1m bulk cross section. The 2x2m unit was excavated in 1x1m sub units following the same

pattern of 10cm arbitrary depths until the cultural layer was encountered. Once the four

corners of the 5x5m units were brought to the cultural depth, the 1x1m bulks were removed and

brought to level following the same procedure.

Heavy rains were persistent and hindered the excavation in multiple ways; the first and

most obvious delay was an inability to excavate during the rainstorm. However, the long term

issue was the saturation of the soil to the point where excavation was a safety hazard. The soil

was slow to dry because the tree canopy blocked much of the sunlight that would help to dry

out the excavation units. After several days of heavy rain, Brian became saturated to the point

where excavation had to be indefinitely suspended in order to attempt to mitigate the potential

loss of information, and ensure the safety of the excavators. After this, the 2x2 unit Cleveland

was opened to the east of Lois. After all units were excavated to the cultural layer, less

emphasis was placed on the 10cm arbitrary levels, and the natural form of the cultural

landscape was followed. This allowed for a more realistic measuring of the living floor, which

is particularly important for the current project. The units were all photographed at the end of

the field season with an overhanging camera and then recorded using a Total Station and Prism,

ensuring the highest degree of accuracy possible. Once the recording of the artifacts was

completed, the units Lois and Cleveland were backfilled and all of the collected materials and

records were handed over to our Czech colleagues for analysis and storage. The artifacts were

cleaned in the lab after they were collected. However, due to the large volume of cultural

material collected, many were put directly into storage and some had to be discarded due to

lack of storage space (Shaw, 2013).

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The Analysis: Once the total station data was received the goal was to not only catalog it in a

GIS, but also to conduct an analysis to identify any striking trends in the deposition of the

artifacts. The initial analysis was a standard density distribution. A Nearest Neighbor

Hierarchal Analysis was also conducted using the CrimeStat spatial statistics program, in order

to determine whether the clusters identified in the density distribution for select artifact types

were, in fact, statistically significant. This was then followed by interpolations based off of the

artifact elevations. Finally, a three dimensional surface was created that represents the living

floor of the 5x5 unit Stewie. Once all of the methods were completed they were compiled and

used to aid in the interpretation of the area’s function in the past.

Theoretical Concerns/Background: As archaeology is intrinsically entwined with issues of

space and place, the construction of models based off spatial relationships is a constant theme,

and a foundational basis of these models is the construction of maps that represent the

distributions of artifacts (Hodder 1976: 1-4). However, there has always been a controversy in

how accurately and truthfully the distribution maps represent the reality of the artifact

distribution. These criticisms once again were been brought to the forefront with the rise in GIS

spatial modeling. Specifically, in the 1990’s criticisms were leveled against the use of GIS to

analyze excavations. It was considered too mechanical and a marker of the rebirth of

environmental determinism as it is can easily reduce the results of the excavation into numbers,

resulting primarily in economic interpretations while disregarding other possibilities (Lock

2003:173).

In addition to these critiques, the argument that, since they are socially constructed, the

analysis of raw data is never objective. Data are intrinsically biased because the researcher must

create the data, which are further influenced by interpretation. This is not the first time these

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arguments have been used in archaeology. As Clarke (1972:6) explains, any operational model

is the product of the archaeologist’s aims and philosophy while working within their particular

paradigm and is further limited by their methodology. Thus, the dissent is not against models

themselves, or the use of new technology in models, but the potential to purport the model as

the only true representation of the past.

In reality, the model is merely a tool to understand the past. Lock (2003:177) believes

that the two dimensional GIS is not capable of capturing many of the subtleties of a place. Clark

(1972:15) again identifies this issue, realizing that a two dimensional map projection does not

reflect the reality of the landscape as it contains variations in topography, which in many cases

will affect the clustering of sites and artifacts. This project aims to overcome some of these

pitfalls of standard GIS operational models through the use of three dimensional data sets and

analysis. As computers, and thus GIS, become more powerful, the construction of extremely

high resolution Digital Elevation Models (DEMs) is beginning to find salience in the

archaeological community as a standard procedure as it, in a sense, seeks to preserves the

geomorphological condition of the site. The DEM then can be used to further analyze the

spatial patterning of the site, helping to partially overcome the destructive nature of

archaeology. The construction of DEMs is not limited to a singular function, but can be

achieved through several techniques used in conjunction or individually. The most common

techniques are Triangulated Irregular Network (TIN), Krging, Inverse Distance Weighting

(IDW), and Spline. These methods all require highly precise data to produce a high resolution

interpolation of the study area, and thus laser total stations or remote sensing techniques, such

as GPS or LIDAR are the preferred method of compiling the raw data (Forte 2000:199-202).

While the construction of DEM’s for a micro-topographic interpretation of a site is not a novel

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Figure 3: The histogram of the artifact elevations for Stewie shows a normal distribution.

concept, they have almost always been used to identify trends at a larger, site-wide or regional

resolution. This project proposes to take the scale down a step further to a resolution of 1:40

and use the methods of micro-topographic analysis as outlined by Forte to assess whether the

analysis and recreation of the living floor of several units of a preliminary excavation is a viable

technique to better understand the results of the excavation and guide further investigation into

the study area.

Data Sources:

All of the artifact data was obtained in raw form in a notepad document from Dr.

Dresler at Masaryk University. The base map and Geodatabase the artifact database was

projected into was also obtained from Dr. Dresler. However, it was obtained earlier during the

field season in 2013.

Methods:

The first step was identifying if the data recorded with the total station was being projected

correctly. This

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Figure 4: Display showing the general density distribution map forthe 5x5m unit Stewie, with n as the sample size.

Figure 5: Display showing the density distribution map for ceramicartifacts.

issue was compounded as ArcGIS did

not recognize the projection that the

Geodatabase was in, and therefore it

required a close examination of the

dataset for Na Včelách in relation to the

known features in the area to ensure

that the data was being projected as it

should be. Once the projection was

established as true the identification tags

for the different artifact types

needed to be decoded as they

were in Czech or abbreviated Czech. Google Translate was used for the tags that included full

words; however, Dr. Dresler had to be

consulted with on the abbreviated

words. Once there was a working key

for the tags, the next step was to

symbolize the artifact types as

appropriately as possible. In order to

perform analyses on the three units

separately, distinct datasets were

created for for Stewie, Lois, and

Cleveland. In order to gain a more

clear understanding of the

distribution of the data in Stewie, a

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Figure 6: The density distribution map for the stones recovered.

histogram of the data with respect to the elevation was examined. This histogram showed three

distinct peaks, which were initially interpreted as three separate strata. This, however, was

found to be inaccurate as a later re-examining of the data showed the feature outlines and geo-

reference points were erroneously included. Once the non-artifactual data was struck from the

dataset, the histogram for the elevation of artifacts for Stewie became meaningful (Figure 3).

The initial analysis created a series of density distribution maps using the Calculate Density

function in ArcView3x. This was done using a method of quadrant analysis similar to what

Hodder outlines in the book Spatial Analysis in Archaeology (Hodder 1976: 33). The parameters

used in this method overlaid a 10cm grid on Stewie, which then had a 1m search radius and

produced a shape file displaying the intensity of the distribution of the variable(s) in question.

This was done for a general distribution (considering all artifact types), ceramic sherds, animal

bones, mill stones, burned stones, stones, and iron (Figures 4-9). The next step was to extract

the ceramic points from the total artifact

dataset for use with the CrimeStat

spatial statistics package; as I

wanted to get a better

understanding of the clustering of

the ceramic artifacts. This was

done using the select by attribute

function in the attribute table, and

then exporting the selected records

as a separate dBase table (dbf).

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Figure 7: The density distribution map of iron artifacts.

This new dbf table was used in the

Nearest Neighbor Hierarchal Clustering

Hot Spot Analysis 1 function (NNH) in

the Crime Stat program under the spatial

description tab, to analyze the

probability that the “events” happened

by chance due to the clustering of the

“events”. With “event” as defined by

ceramic sherd for the purposes of this

study. Nearest Neighbor methods, are

similarly described, as quadrant methods are,

in the book Spatial Analysis in Archaeology (Hodder 1976:38). The result of this analysis is ellipses

in the form of ArcView shape files that signify the grouped events that have a statistical

probablility (99.99%) of not occurring by chance. The parameters were set to the smallest search

radius, one standard deviation and a minimum of 10 points per cluster, as these parameters

produce the least chance of identifying a cluster that has happened by chance. As the initial dbf

contained the ceramic data for all three units the resulting ellipses were affected due to the

empty space in between the units. To account for this “empty space” issue, the analysis was

then ran again on the ceramic data exclusively from Stewie. To further verify the density

distribution map for the ceramics, the ceramic data for Stewie and Lois, the two units that

clusters were found in, were extracted and the analysis was re-run using the ceramic data

specific to the units. However, to produce ellipses, the minimum number of points per cluster

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Figure 9: Display showing the density distribution foranimal bones in Stewie.

Figure 8: The density distribution map showing mill stones.

in Stewie and Lois had to be reduced to four and three, respectively. These ellipses were then

brought into the ArcMap

work session and displayed

over the ceramic artifact and

density distribution maps to

identify if there was a

correlation between the two

types of analyses, and what

information it provided (Figure

10). The third phase of the

analysis was to produce

interpolated surfaces using the

elevations of the artifacts.

As previously stated, the

histogram of the artifact elevations was

already in a normal curve, allowing the

interpolations to be conducted without

any initial transformation to be

conducted. Before the interpolations

were conducted, the data were

visualized using the “trend analysis”

tool in the explore data menu on the

geostatistical analyst extension (Figure

11), which allows the user to display and

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Figure 10: The twoNNH analysesdisplayed over theceramic artifactsfor Stewie. Thelarger grey ellipsesare from theanalysis thatconsidered datafrom the threeunits, while thesmaller blackellipses are fromthe analysis thatonly considereddata found withinStewie.

Figure 11: These two scatterplots are the trend analysisfunction, the top image isviewing the elevation data ofStewie from the east, to moreclearly show the downwardtrend from north to south.While the bottom image isviewing the data from thesouth showing the relative lackof deviation in elevation fromeast to west.

manipulate data

points in a three

dimensional cloud,

aiding in the

identification of trends

in the data. This is

where the north to

south downward

sloping of the artifact

elevations was first discovered, and what

was used to judge the accuracy of the interpolations which were later produced. As the Trend

analysis function is displaying the exact position of the artifacts as they were recorded, and

therefore if the interpolation did not show similar results it could be determined that it was not

producing an

accurate DEM of the

artifact elevations.

The first

interpolation method

was an Inverse

Distance Weighting

(IDW). The geostatistical wizard was used, and the dataset consisted of all of the artifacts in

Stewie. The resulting map that was produced was symbolized as the solid contour lines, as

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Figure 12: Display showing allthree interpolations constructedfrom the elevations of the artifactsfor Stewie. The contour lines arethe results of the IDW, the Shadedcontours are the result of theKriging, and the “hot spots” arethe results of the Spline. Thewarmer colors are higher inelevation with red being thehighest.

Figure 13: Displayshowing a view of theTIN and has the artifactpoints projected onto it.

this allows for both the Kriging and

IDW to be displayed simultaneously.

After the IDW interpolation was

made, I made the kriging prediction map. This was also done in the geostatistical wizard, using

the simple Kriging method. In order to round out the interpolation a spline with barriers was

also made using the spatial analyst extension. The same dataset of artifact points in Stewie

were used and analyzed with respect to the Z axis, while the unit outline for Stewie was the

barrier for the analysis (Figure 12). The construction of the Spline served a double purpose, as it

is a raster layer it was easily converted to a TIN (Triangular Irregular Network) layer (Figure 13).

The TIN was then opened in ArcScene and represents the final stage of analysis for the project,

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Figure 14: Display showing the TIN is looking at Stewie from the same vantagepoint as the first image in the Trend Analysis (Figure 11). While it does show thedecreasing elevation from north to south it does not exaggerate the difference inelevation, thus producing a more realistic visualization of the living floor.

Figure 15: Displayshowing the 5x5m unitStewie has the ellipsesproduced from the hotspot analysis thatconsidered data from allthree units superimposedover the ceramic densitydistribution map. Thissupports the idea that thehigher density zones arerepresentative of theactivity areas withregards to an artifacttype.

as it displays topographical information in 3-D, and thus allows for the interpretations based off

the interpolations to be visualized in a more realistic way. As the two dimensional views of the

interpolations (IDW, Kriging, and the Spline), and the trend analysis function can exaggerate

the difference in elevation. (Figure 14).

Results: The results of the first density distribution maps (see figures 4-9) identified several

different apparent patterns in the artifacts. First, the general artifact distribution showed that

the artifacts were more clustered and numerous in the north and east portions of the unit and

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Figure 16: Display showing the 2x2m unit Loiswith an ellipse superimposed on it which isthe result of the Hot Spot analysis thatconsidered data from all three units.

Figure 17: Display showing the 5x5munit Stewie with several ellipsessuperimposed over which were theresult of the Hot Spot analysis thatonly considered artifacts found withinStewie.

some areas of medium artifact density in the

southeast and central portions of the unit (Figure 4).

The ceramic distribution map (Figure 5) showed a

higher density of artifacts in the north western

portion of the unit, and an area of medium density in

the eastern portion of Stewie, however both of these

zones that had higher density were more localized

than the general artifact density distribution. The

stone density distribution map (Figure 6) displayed

a series of high-density zones that seem to be close to

the same as the general artifact high-

density zones. However in the stone

density map displays an area of medium

to high-density zone on the central

western border of Stewie that was not

displayed in the general artifact density

map. The distribution map of the iron

artifacts (Figure 7) showed a higher

degree of clustering in the south-eastern

portion of the unit, and gets less dense the further

north-west in the unit one gets. However due to the

rarity of the iron artifacts and size of the unit, it

would be hard to say that anywhere besides the central eastern portion shows any clustering of

iron artifacts. The Millstone distribution map (Figure 8) showed a high-density zone in the

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Figure 18: Display is ofthe 2x2m unit Loisshowing the ellipse inthe southern portion ofthe unit that wasproduced from the hotspot analysis thatconsidered onlyceramic artifacts foundwithin Lois.

Figure 19: Display is focusing on thenorthern feature in Stewie with thethree interpolations showing thatthe artifact elevations were lowerfor the artifacts found within thefeature.

north eastern portion of Stewie

with quickly decreasing density

of millstone the further away

from the main high-density zone

in any direction. The animal

bone density distribution (Figure

9) showed that the highest density of animal bones was

in the south-eastern portion of Stewie, while there was also a medium density zone in the

central northern potion of Stewie.

The NNH analysis that included data from all three units (Figure 15) produced three

ellipses in Stewie, which when displayed over the ceramic density distribution map highlighted

both of the high density zones, and several of the medium density zones. This suggests that in

the case of the ceramics the density distribution map produced meaningful results. The same

NNH analysis also produced an ellipse in Lois (Figure 16). The NNH analysis on just the

ceramic data for Stewie (Figure 17) produced

three smaller ellipses that overlay the high-

density zones, and one ellipse that overlays a

medium density zone. However only two of the

three ellipses that resulted from the NNH

analysis that considered all three units had

ellipses found within their area when the data

were localized to Stewie. The NNH analysis on the Lois

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Figure 20: Display of Stewie is only showing the Ironand Millstone artifacts over the three interpolations thatshows the relationship between elevation and where theartifacts were found.

Figure 21: This is a map ofthe 2x2m unit Clevelandwith the two interpolationsdisplayed over the artifacts.

ceramic data (Figure 18) also produced

an ellipse in the southern portion of the

unit, this ellipse was also found within

the area of the NNH that considered data

from all three units.

The IDW produced an interpolation that

was slightly less accurate than the

Kriging map. This is determined from

the Root Mean Square (RMS) statistic,

where the IDW’s RMS was

0.03341312423753533, whereas the Kriging

RMS was 0.032971996641534766. The

method that was used to create the Spline did not output a specific statistic that identified how

accurate it was, but when compared to the results from the

IDW and Kriging, it can be inferred to be

of at least similar accuracy (Figure 12). The

three interpolations all showed the north –

south downward sloping trend identified

in the trend analysis. In addition to

identifying that the artifacts in and around

the area of the northern feature were found

at a lower elevation (Figure 19), which was

not apparent when the north-south sloping

trend was observed using the Trend

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Figure 22: This is aview of the 2x2 unitLois from the eastusing the trendanalysis function,which shows the twoseparate groups,with the southerncluster being slightlylower than thenorthern group.

analysis function. When the artifacts are projected onto the map with the interpolations, other

breaks in the elevation become more relevant for interpretation (Figure 20). For example, how

the mill stones were found at a lower elevation in the middle of the large cluster of regular

stones in the north east corner of the unit, or how the iron artifacts seem to be found at a lower

elevation than the objects surrounding them (Figure 20). The Trend analysis was able to

identify a south to north downward sloping trend in the Cleveland unit, which was confirmed

by the Kriging and Spline interpolations. However, there did not seem to be a strong

connection between the artifacts and their elevation and relationship to each other. The most

significant information that could be gained was the three ceramic sherds that are near the

southeast corner of the unit could potentially be from the same vessel as they are quite near in

elevation; however they are not statistically clustered (Figure 21). The Trend analysis for the

artifacts in Lois (Figure 22) showed two distinct groups of artifacts: one in the northern portion

of the unit and one in the southern. The Southern cluster is slightly lower in elevation

compared to the northern unit, and it is in the southern cluster that the NNH cluster that only

considered the data from Lois was found.

The three interpolations(IDW, Kriging, Spline) produced maps that all showed the north- south

sloping trend that was

identified in using the trend analysis function, and therefore I

elected to use all three of the results together to produce a general distribution of the artifact

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elevations. The most striking results from the interpolations were gained when compared to the

density distribution maps, as the majority of the high-density zones were found at a higher

elevation than the rest of the artifacts. The primary exception to the north-south downward

sloping trend is the northern most feature, (Figure 19) which shows some of the most clustering,

yet the artifacts are found at a lower elevation than those around it. While the trend analysis

and Kriging can potentially exaggerate the difference in elevation, the TIN does a better job of

realistically representing the surface of the living floor of Stewie (Figure 14). It still retains the

advantages of the other interpolation models as it is still shaded according to elevation.

Although the primary advantage of the TIN is that the elevation data is actually represented,

instead of being represented as a series of contours. Then, when the artifact cloud is projected

into the same work session as the TIN in ArcScene, the spatial distribution in terms of elevation

is highlighted in the most realistic way out of any of the models created for this project. As the

relationship between the TIN and the actual excavation can be seen (Figure 23) in a side-by-side

comparison with a picture of Stewie on the last day of the field season right before pictures and

elevations were shot. Figure 23 shows the advantages that the TIN has in terms of being able to

observe the distribution of artifacts, as it more clearly highlights where the differences in

elevation are than if one were observing pictures of the excavation.

Figure 23: These two images are of the TIN created from the recorded artifact elevations for Stewie and a pictureof the excavation floor, both are from the perspective of the southwest corner looking northeast.

22

There were some artifact types, such as the loom weights, where we only uncovered

three, and therefore a NNH analysis was not feasible as it requires a minimum number of at

least five points in the data set to show clustering, therefore an analysis similar to those

undertaken for other more numerous artifact types in this project would not have been useful or

an efficient use of time. Two of the spindle whorls were found within a millimeter of the same

depth but several meters apart in the unit. When viewing the position of the spindle whorls one

is intuitively drawn to the conclusion that they are not clustered, and while the similarity in

depth of two of the three is striking, more spindle whorls would have to be found at a similar

depth to show that the similarity is more meaningful than chance. Also it is worth noting that

the spindle whorls do not appear to be clustered when viewing them within the context of

Stewie alone and this may change as more area is investigated in the future.

Discussion/Conclusion: This project’s main goal was to identify if methods of spatial

analysis and Micro Topography as identified by Forte (Forte 2000: 205) were applicable to a

smaller scale analysis. The density distribution maps have been used in the spatial analysis of

artifact distributions for several decades by this point (Hodder 1976: 33) (Wheatley (2002: 128-

129) and are thus the basis to which all other models are compared in terms of revealing spatial

information about the site. The density distributions identified that the artifacts were deposited

with increasing frequency as one moved north, and east in Stewie. They also identified the

general areas in which the artifacts were more densely located. These clusters were particularly

informative in regard to the ceramics and stones, as these were the most numerous artifacts and

also appear to be the most clustered. Because the NNH analysis reflected the patterning in the

density distribution maps, it confirms that CrimeStat, at least in regards to the NNH analysis, is

23

capable of producing at least a rough map of the areas of significant clustering for an artifact

when analyzing the site’s data. Then, when restricting the data set to a smaller area, a NNH

analysis is capable of identifying when a number of sherds from similar production and

potentially the same vessel were found together (the small ellipses). The larger ellipses created

by three unit NNH analysis can be interpreted to indicate areas of general activity, especially

when combining the density distribution maps. When the general artifact density distribution

map is then overlaid with the feature lines, it appears that the northernmost feature could have

been a midden or small storage pit of some sort, as animal bones and ceramic sherds were

found both in close proximity and within the identified feature. However, as these methods

only considered the X and Y positions of the artifacts, different models were required to more

accurately visualize and interpret the artifact distribution. The issue of a lack of orientation to

the Z axis was resolved with the interpolations that were conducted, as they interpolate the

points using the Z axis. The three interpolations that were produced did show approximately

equal results, but the Kriging and Spline were more accurate in terms of identifying the local

variation from within the units. This was demonstrated mathematically in the comparison of

the RMS between the IDW and Kriging methods. Displaying both of the methods over the unit

at the same time, though, showed that the IDW produced a more general analysis of the

elevation trend, while the Kriging identified a higher resolution in the changes from one artifact

to another. The higher resolution of the Kriging was equated if not surpassed by the Spline

method, which supports the theory that Kriging and Spline are roughly equivalent in terms of

interpolation methods (Loyd 2007:152). These methods of spatial analysis (interpolation) have

traditionally been used for predictive modeling (Zubrow 1979:115-121 and Lock 2003:168). Since

the development of more advanced computer systems and more powerful GIS, the ability to

recreate the ancient landscape through the employment of DEMs has become possible (Zubrow

24

1990:307 and Forte 2000:199-203), although the use of these techniques have not been fully

realized, as they are seemingly only employed in larger regional studies, and not in a

microtopographic resolution. The interpolations of this project showed that the geostatistical

and spatial analyst tools can be employed to model the ancient landscape accurately and

identify the trends in the elevation that are present in the artifact distribution. However, that is

not to say that the interpolation can be considered carelessly as the method if employed

carelessly can exaggerate the difference in elevation as the tools often are employed in order to

make the identification of trends easier; and therefore the results must be carefully scrutinized

(Wheatley 2002: 108). For example, if one were to look at the results of these interpolations

without knowing the elevations, they potentially could think that Stewie is located on a

meander scar as it appears to be a steep rise in elevation from north to south. In reality the

‘steep rise in elevation’ is comprised of roughly 30cm. While Kilmo (2013:2) has indicated that

the site is in the flood plain, the minimal degree of apparent disturbance from flooding suggests

that it is more likely that the site is located on a point bar (Brown 1997:39-40,73). Because of the

limited scope of the excavation during the 2013 season, identifying the particular use of the site

cannot be done with sufficient confidence. The large quantity of stones clustered around a mill

stone with outlying clusters of ceramics and animal bones supports the idea that food

processing or storage could have been among the primary uses of the area. Although the

spindle whorls suggest that textiles were being manufactured in the area which could mean that

Na Včelách was a site where more industrial activities were being undertaken. As the northern

feature is not as diagnostically similar to the other storage pits, it potentially could be a midden

or another variety of a small in-house storage area (Přichystalová 2011:80). All of the other

features that were discovered did not have a similarly high density of artifacts within their areas.

It is also unique because the northern feature is the only feature that does not appear to

25

continue outside of the excavation unit. Identifying if the other features are the result of human

activity and then what activity is much more difficult until the full feature is uncovered.

Although, as they do appear to continue beyond the excavation unit, they do support the idea

that Stewie did not uncover the full living floor and that further excavations to the east of

Stewie could potentially identify what the features were used for. The two clusters in Lois are

found within what appears to be the feature area, although as the total number of recorded

artifacts is not as extensive, and there are no distinguishing features that were found in the soil,

it is difficult to confidently identify what the area was used for. As the the number of artifacts

recorded are relatively small and only part of the apparent feature as identified by the soil stain

has been uncovered. The same can be said for Cleveland, although Lois and Cleveland do

provide support for the idea that the hinterland settlement at Na Včelách was not a singular

settlement. Overall the preliminary excavation has supported the hypothesis that Na Včelách

was in fact a hinterland site. The analysis of the artifact distribution has shown that Stewie

found the floor of a building where several types of activities were being conducted, as

evidenced from the loom weights, ceramics, animal bones, and iron artifacts. If further

investigations are to be undertaken at Na včelách, the results of this project would suggest that

a unit should be placed next to Stewie to the east, and north in order to determine whether the

features on the eastern portion of the unit continue and, if so, what purpose they served. And to

see if the high density of artifacts continues to the north and northeast. This project provides

another benefit to the field of archaeology as it shows that spatial analysis and geostatistical

tools can be employed with success at very fine resolutions in order to highlight trends in the

data that otherwise would be much more difficult to identify.

26

Acknowledgements: I would like to thank Dr. Petr Dresler for allowing me to use the

Geodatabase that he compiled as the basis for which I was able to conduct my project in

addition to giving me access to the total station data that he had collected from our excavation.

Without him this project would not have been possible. Dr. Robert Hasenstab for giving me

guidance and advice on how to wrestle with the data in GIS in order to produce valid analyses

on my data. Dr. Michael Dietz for supporting me and my project through the entire time from

inception to completion in addition to allowing me to return to Pohansko and giving me the

opportunity to conduct this project. Matt Shaw for allowing me to access his field notebook

which provided invaluable insight into the excavation at Na Včelách as well as all of the advice

he has given me while in the field. And Dr. John Staeck, for allowing me to return to Pohansko

to work with his crew and run this project. In addition I would like to thank Dr. Joel Palka, for

his help and advice, as without his guidance I would not have had the funding to return to

Pohansko. And finally the Honors College at UIC for their continued support of this project.

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