2. 1
During the summer of 2013 a preliminary excavation of Na Včelách, a hypothesized
Great Moravian Era (9th
century) site in the hinterlands of Pohansko, Czech Republic was
conducted. Investigation of this site is part of an ongoing research project which that focuses on
the first Slavic state and one of the first principalities in central Europe to be converted to
Christianity. During the early 10th
century the Moravian State swiftly collapsed; a better
understanding of the hinterlands will provide insight into the collapse of the empire. This
project sought to understand what the two and three dimensional spatial trends in the artifact
disposition could tell us about the site. During the excavation we used a total station to point
provenience the excavation data, including artifacts and feature lines. The analyses were
conducted using the ArcGIS mapping software to analyze and interpret the preliminary
excavation 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 trend
in the deposition of the artifacts. The results of this project support the hypothesis that Na
Vvčelách is in fact a domestic hinterland site of Pohansko. While the preliminary excavation did
not provide enough evidence to support one theory of collapse it provides a solid foundation to
guide 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 core
territory is red and the green is the height of
expansion. Photo Courtesy Jiří Macháček
(Macháček 2013:3)
3. 2
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 of
Pohansko with the former wall hilighted in
blue and the location of the churches hilighted
in red. It also is showing where past research
has been done in the area, both geophysical
prospection and excavations. Photo Courtesy:
Petr Milo (edited by author) (Milo 2011:81)
4. 3
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
5. 4
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
6. 5
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 (from Munsell 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
7. 6
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).
8. 7
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
9. 8
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
10. 9
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
11. 10
Figure 4: Display showing the general density distribution map for
the 5x5m unit Stewie, with n as the sample size.
Figure 5: Display showing the density distribution map for ceramic
artifacts.
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
12. 11
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).
13. 12
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
14. 13
Figure 9: Display showing the density distribution for
animal 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
15. 14
Figure 10: The two
NNH analyses
displayed over the
ceramic artifacts
for Stewie. The
larger grey ellipses
are from the
analysis that
considered data
from the three
units, while the
smaller black
ellipses are from
the analysis that
only considered
data found within
Stewie.
Figure 11: These two scatter
plots are the trend analysis
function, the top image is
viewing the elevation data of
Stewie from the east, to more
clearly show the downward
trend from north to south.
While the bottom image is
viewing the data from the
south showing the relative lack
of deviation in elevation from
east 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
16. 15
Figure 12: Display showing all
three interpolations constructed
from the elevations of the artifacts
for Stewie. The contour lines are
the results of the IDW, the Shaded
contours are the result of the
Kriging, and the “hot spots” are
the results of the Spline. The
warmer colors are higher in
elevation with red being the
highest.
Figure 13: Display
showing a view of the
TIN and has the artifact
points 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,
17. 16
Figure 14: Display showing the TIN is looking at Stewie from the same vantage
point as the first image in the Trend Analysis (Figure 11). While it does show the
decreasing elevation from north to south it does not exaggerate the difference in
elevation, thus producing a more realistic visualization of the living floor.
Figure 15: Display
showing the 5x5m unit
Stewie has the ellipses
produced from the hot
spot analysis that
considered data from all
three units superimposed
over the ceramic density
distribution map. This
supports the idea that the
higher density zones are
representative of the
activity areas with
regards to an artifact
type.
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
18. 17
Figure 16: Display showing the 2x2m unit Lois
with an ellipse superimposed on it which is
the result of the Hot Spot analysis that
considered data from all three units.
Figure 17: Display showing the 5x5m
unit Stewie with several ellipses
superimposed over which were the
result of the Hot Spot analysis that
only considered artifacts found within
Stewie.
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
19. 18
Figure 18: Display is of
the 2x2m unit Lois
showing the ellipse in
the southern portion of
the unit that was
produced from the hot
spot analysis that
considered only
ceramic artifacts found
within Lois.
Figure 19: Display is focusing on the
northern feature in Stewie with the
three interpolations showing that
the artifact elevations were lower
for the artifacts found within the
feature.
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
20. 19
Figure 20: Display of Stewie is only showing the Iron
and Millstone artifacts over the three interpolations that
shows the relationship between elevation and where the
artifacts were found.
Figure 21: This is a map of
the 2x2m unit Cleveland
with the two interpolations
displayed 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
21. 20
Figure 22: This is a
view of the 2x2 unit
Lois from the east
using the trend
analysis function,
which shows the two
separate groups,
with the southern
cluster being slightly
lower than the
northern 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
22. 21
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 picture
of the excavation floor, both are from the perspective of the southwest corner looking northeast.
23. 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
24. 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
25. 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
26. 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.
27. 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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