Merrill m ma_thesis_2004

INTRASITE SPATIAL ANALYSIS AND INTERPRETATION OF MAPPED
SURFACE ARTIFACTS IN CA-SNI-25, SAN NICOLAS ISLAND, CALIFORNIA
A Thesis
Presented to
The Faculty of the Department of Anthropology
California State University, Los Angeles
In Partial Fulfillment
of the Requirements for the Degree
Master of Arts
By
Michael L. Merrill
June 2004

ACKNOWLEGEMENTS
This project required the assistance and support of several people as well as the
United States Navy. I want to thank Dr. Patricia Martz for suggesting this project to me
and for providing needed encouragement and advice throughout its duration. I could not
have asked for a better mentor at Cal State LA or anywhere for that matter than Dr.
Martz. I will forever appreciate the knowledge you shared and for the doors of
opportunity opened for me, Dr. Martz. I am also thankful for the excellent field assistance
and professional site evaluation provided by Dan Larson and John Romani of Compass
Rose Archaeological Inc. during the initial data collection phase of this project. I would
also like to acknowledge the following Cal State LA students, listed in alphabetical order,
for their excellent and dedicated assistance with the mapping and recordation of surface
artifact positions and types at CA-SNI-25: Charles Cisneros, Lina Flores, Catherine
Girod, Walter Henriguez, Emilio Merino, and Vicki Stossel. I also extend thanks to Rod
McLean for his outstanding and patient instruction, both in the classroom and the field, in
using the theodolite on one visit to San Nicolas Island. This was during the latter
recordation phase of this project. Walter and Emilio also received instruction from Rod
on this visit and assisted in using a theodolite and stadia rod at CA-SNI-25 to set up a site
grid and boundary. Thank you again, Rod, Walter, and Emilio for your sincere and
excellent help. I also am indebted to Steve Schwartz for making this project possible by
providing the financial and logistical support. Thank you Steve for your help and for
sharing some of your knowledge and ideas concerning the prehistory of San
Nicolas Island. I found several of these ideas very insightful, especially the importance of

driftwood to the native people of San Nicolas Island. I would also like to extend my
gratitude and appreciation to the United States Navy for allowing me to conduct research
on San Nicolas Island.
I want to express a deep and heartfelt appreciation to the members of my thesis
committee, Patricia Martz, Ph.D. (Chair), Chester King, Ph.D., James Brady, Ph.D., and
Dwight Read, Ph.D. Patricia Martz, Chester King, and James Brady carefully read the
archaeological sections of my thesis. They provided editorial, logical, and factual
corrections that significantly improved the accuracy and quality of presentation of the
archaeological, ethnographic, and empirical data contained in this thesis. Professor
Dwight Read of UCLA critically reviewed the applied mathematics in my thesis. His
suggestions and corrections significantly improved the quality and presentation of the
statistical and mathematical methods. Dr. Read is currently assisting me in preparing
some of my research work for publication. I plan on continuing my studies in
archaeology at UCLA with the sponsorship of Dr. Read.
Finally, I want to give special thanks to my friend and long time mentor in
archaeology, Dr. Chester King. Chester is a scientist who possesses the rare combination
of remarkable ability, consummate skill, and a genuine concern and respect for the people
and cultures he studies. Without Chester’s friendship, guidance, and encouragement I
would not have become an archaeologist. Thank you Chester.

ABSTRACT
An Intrasite Spatial Analysis and Interpretation of Mapped Surface Artifacts at CA-SNI-
25, San Nicolas Island, California
By
Michael L. Merrill
The focus of this research project is the objectification of the internal organization
in CA-SNI-25, a Late period village site overlooking the northwest coastline of San
Nicolas Island, one of the Southern Channel Islands off the coast of Southern California.
There is a major gap in knowledge pertaining to the internal organization of village sites
on this island. To address this gap, mathematical analyses of typed surface artifact
distributions in CA-SNI-25 as well as provenience-based counts of typed artifacts from
an excavation in a coastal Chumash village site (CA-VEN-27) of similar occupation span
were performed. Archaeological, experimental, ethnographic, and ethnohistoric
information were used to interpret these analyses. Specifically, the location and artifact
composition of areas of organized activity as well as tool kits were inferred by the
analyses of the CA-SNI-25 data. The analysis of the CA-VEN-27 or Pitas Point data was
used to discover tool kits. The Pitas Point data are a more complete sample than the
sample from CA-SNI-25. For this reason the results of the analysis of the Pitas Point data
were used as a predictive model for associations of artifact types not present in the CA-SNI-
25 sample but present in the site.

In addition, other archaeological data such as CA-SNI-11 tarring pebble data are
used in this thesis to buttress the interpretations of the Pitas Point analysis as well as the
results of the analyses of the CA-SNI-25 data.
Surface artifact clusters that are interpreted as four house locations were
discovered in the sampling area of SNI-25, using a nearest neighbor analysis. Each of
these clusters has mortar fragments and core tools such as chopper/hammers.
Ethnography supports interpreting these four artifact clusters as house locations. The
three northern house locations (House Activity Areas #1, #2, and #3) are tightly clustered
at the north edge of the site. The close proximity of these house activity areas to one
another suggests they are contemporaneous. The southernmost house location (House
Activity Area #4) is disjunct (about fifteen meters south) from the northern “house”
cluster. This significant spatial separation may result from House Activity Area #4 being
part of a separate and possibly non-contemporaneous house cluster.
Two additional surface artifact clusters in the SNI-25 sampling area were
identified with the nearest neighbor analysis. These two artifact clusters are interpreted as
outdoor activity areas based primarily on the absence of mortars and pestles (household
kitchen tools) and the presence of large clusters of metavolcanic and quartzite flakes and
retouched flake tools such as scrapers and blades. The northernmost of these two clusters
(Outdoor Activity Area #1) is intermediate between House Activity Area #3 and House
Activity Area #4. It is likely that this activity area is connected to both House Activity
Area #3 and #4, but could have been used at two disparate time periods if the occupations
of House Activity Area #3 and #4 do not temporally overlap. The southernmost cluster

(Outdoor Activity Area #2) is juxtaposed and to the east of House Activity Area #4. This
outdoor activity area is almost certainly tied to this house activity area alone.
The types of activities inferred by clusters of surface artifacts (activity loci) within
the four household areas and in areas immediately next to these areas are as follows: (1)
Food processing and cooking (inferred by fire-affected rock) and (2) Wood, bone, and
shell working. The types of activities inferred by clusters (activity loci) of surface
artifacts within the two outdoor activity areas are as follows: (1) Butchering and (2)
Wood, bone, and shell working,
Two additional mathematical analyses were used to discover tool kits in CA-VEN-
27 and CA-SNI-25. Both of these analyses use methodology that is entirely original
and applied to archaeological data for the first time in this thesis. The first analysis is a
synthesis of Pearson correlation analysis and graph theory and used typed artifact counts
from four excavated areas in CA-VEN-27 as raw data. Four tool kits were discovered
with this analysis. The second analysis combines a robust and distribution-free non-parametric
statistical method, known as a multi-response permutation procedure (MRPP),
and Graph Theory. This analysis used the point provenience of typed surface artifacts in
the sampling area of CA-SNI-25 as raw data. Three tool kits were discovered with this
analysis. The tool kits identified by these analyses together with the results of an analysis
of CA-SNI-11 tarring pebbles, along with ethnography and information from replicative
experiments and micro-wear analysis on stone tools (Keeley, 1980) suggest additional
types of activities at both CA-SNI-25 and CA-VEN-27. These activities include
groundstone production and the manufacture of several types of basketry (e.g. “water
bottles”).

TABLE OF CONTENTS
Acknowledgements………………………………………………………………………iii
Abstract…………………………………………………………………………………...vi
List of Tables………………………………………………………………………….…..x
List of Figures………………………………………………………...……...…………..xii
List of Tables
Table 1. Excel 4.0 macro…………….……………….…………………….……………25
Table 2. Various probabilities of incorrectly rejecting or correctly rejecting the null
hypothesis for a given number of iterations……………………………….……………..26
Table 3. Details of the computation of an exact multi-response permutation procedure
delta for the observed hypothetical distribution of scrapers and choppers in Figure
10……………………………………………………………………………….……...…36
Table 4. Exact multi-response permutation procedure delta values and corresponding P-values
for each of fifteen possible two- four combinations of hypothetical surface artifact
locations…………………………………………………………………..……………...37
Table 5. Statistically significant results of the nearest neighbor analysis applied to
mapped surface artifacts in the sampling area in CA-SNI-25……………..…………….48
Table 6. Typed artifact counts from four excavated areas in CA-VEN-27………...…....63
Table 7. Pearson correlation coefficient matrix computed from the CA-VEN-27 typed
artifact counts in Table 6……………………………………………………..…………..64
Table 8. Adjacency matrix resulting from the binary coding of the correlation
coefficient matrix in Table 7 using a cut-off of 0.78………………..……………...........65
Table 9. Cliques resulting from a cut-off of 0.77 in the correlation matrix (Table
7)………………………………………………………………………………………....67
Table 10. Cliques resulting from a cut-off of 0.78 in the correlation matrix (Table
7)………………………………………………………………………………….……...67

Table 11 Cliques resulting from a cut-off of 0.79 in the correlation matrix (Table
7)…………………………………………………………………………………………67
Table 12. Resulting cliques that are interpreted as either house or outdoor activity area
tool kits in CA-VEN-27…………………………………………………………….....…69
Table 13. The format used to enter the mass data for CA-SNI-11 tarring pebble samples
into the Blossom statistical software…………………………………………………..…79
Table 14. Radiocarbon dates from two units in Mound B of CA-SNI-11……………….79
Table 15. Exact Multi-response Permutation Procedure delta P-values for each of the
pairs of tarring pebble samples from CA-SNI-11…………………………...…………...81
Table 16. CA-SNI-25 surface artifact types used in the exact multi-response
permutation procedures………………………………………………………………….88
Table 17. Delta P-value matrix constructed using the results of the Exact multi- response
Permutation Procedures used for the pair-wise comparison of the spatial distribution of
nine surface artifact types in the sampling area of CA-SNI-25….....................................88
Table 18. Adjacency matrix resulting from the binary coding of the exact multi-response
permutation procedure
" P-values in Table 17 using a cut-off of 0.05……...…89
Table 19. Cliques resulting from a network analysis using the adjacency matrix in
Table 18 as raw data……………………………………………………...………..…….89
Table 20. Adapted from Keeley (1980:112). Relationship between use and flake length
resulting from experiments using replicated flakes…………………………….………..93
Table 21. ASCII format entered into the Blossom software to test for equality of medians
of the data in Table 20, using Multi-response Permutation Procedures (MRPP) with
absolute deviations (Euclidean Distance)………………..……........................................93
Table 22. Refer to description in thesis………………………………...…….………….94
Table 23. Results of the MRPP performed on the data in Table 20……………………..94
List of Figures
Figure 1. Channel Islands of Southern California………………………….………….….4
Figure 2. CA-SNI-25 Topographic Map…………………………………………………..8

Figure 3. Photograph of SNI-25……………………………………………...…………..10
Figure 4. Plot of the probability of making a Type I error using an approximate sampling
distribution for a given number of iterations, when the P-value of the actual distribution is
at the 10% level of significance………………………………………………...………..27
Figure 5. Plot of the probability of making a Type I error using an approximate sampling
distribution for a given number of iterations, when the P-value of the actual distribution is
at the 6% level of significance………………………………………………...…………27
Figure 6. Plot of one minus the probability of making a Type I error using an
approximate sampling distribution for a given number of iterations, when the P-value of
the actual distribution is at the 4% level of
significance……………………………………………………………………….……...28
approximate sampling distribution for a given number of iterations, when the P-value of
the actual distribution is at the 2.5% level of significance……………………...…….…28
Figure 8. Approximate sampling distribution for the Clark & Evans nearest neighbor
statistic in a 20 x 20 area containing 3 points…………………………………………....30
Figure 9. Approximate sampling distribution for the Clark & Evans nearest neighbor
statistic in a 20 x 20 area containing 8 points…………………………………………....31
Figure 10. All
"I, J (Euclidean distances) indicated as edges connecting nodes in the
graph, where the nodes represent hypothetical surface artifact locations………….…….36
Figure 11. CA-SNI-25 sampling area with designated activity areas……………...….…49
Figure 12. House Activity Area #1……………………………………………..……..…50
Figure 13. House Activity Area #4 and two adjacent small outdoor activity areas……...52
Figure 14. Outdoor Activity Area #2……………………………………………...……..53
Figure 15. Outdoor Activity Area #1 and two small outdoor activity areas……………..54
Figure 16. House Activity Area #2……………………………………………..………..55
Figure 17. House Activity Area #3………………………………………………..……..55
Figure 18. Photograph of CA-SNI-25 donut stone fragment………………………..…...58
Figure 19. Photograph of CA-SNI-25 sandstone abrader………………………..……....59

Figure 20. Plot of the frequencies of the correlation coefficients from Table 7………....66
Figure 21. CA-VEN-27 Artifact Association Network Graph…………………….…….70
Figure 22. Tarring pebble maximum length verses mass scatter plot and linear
regression…………...………………………………………………………….………...81
Figure 23. Plot of the mass of two samples of tarring pebbles from CA-SNI-11…...…...82
Figure 24. Plot of the mass of a sample of small and large tarring pebbles from CA-SNI-
11…………………………………………………………………………………….…...82
Figure 25. Plot of the mass of a second sample of small and large tarring pebbles from
CA_SNI-11………………………………………………………………………..……..83
Figure 26. Plot of the mass of a third sample of small and large tarring pebbles from CA-SNI-
11…………………………………………………...……………………………….83
Figure 27. Plot of the mass of a fourth sample of small and large tarring pebbles from
CA-SNI-11………………………………………………………………………..……...84
Figure 28. Photographs of a small water bottle and tarring pebbles…………………..…86
Figure 29. CA-SNI-25 Artifact Association Network Graph………………….……...…90
Figure 30. Plot of the flake length frequencies corresponding to two activity types for
each of the six length classes in Table 13……………………………………….……….95
Figure 31. Relative frequency plot of metavolcanic flake length from a sample
taken in an outdoor activity area in the CA-SNI-25 sampling area……………...…...….97
Figure 32. Relative frequency plot of quartzite flake length from a sample taken in an
outdoor activity area in the CA-SNI-25 sampling area……………………………..…...97
Figure 33. Relative frequency plot of metavolcanic flake length from a sample taken
in a house activity area in the CA-SNI-25 sampling area………………………..……..102
Figure 34. Relative frequency plot of quartzite flake length from a sample taken in a
house activity area in the CA-SNI-25 sampling area…………………….………..……102
Figure 35. Theoretical energy flow and storage model of a possible subsystem in CA-SNI-
25……………………………………………………………….………………….112

Chapter
1.
Introduction…………………………………………..………………………...…...……..1
Importance of Studying Internal Site Structure…………………………..……..……...1
Description and Location……………..…………...……………………....…...….……2
Geology, Topography, and Environmental Setting………...………….………......…...3
CA-SNI-25……….…………………………………………………………….…...…..7
Overall Aims and Potential Contribution to Future Research……………...……....…..9
Background and Significance…………………………….………….…….....…….…12
Research Goals…………………………...….…….………………..…..…….……….13
Importance of this Research………………………………......……………....……….13
Pre and Post Depositional Disturbance……………………………..……....….......….14
Research Questions and Hypotheses…………………………..…...……………....…15
2. Methodology………………………………….…………………….……………...….18
Sampling Procedure………………………………………………..……………….…18
The Applied Mathematical Methods Used in this Study……..………….……………19
The Clark & Evans Nearest Neighbor Statistic and its Previous Use in
Archaeology…………………………………………………………….……………..20
A Monte Carlo Test of Spatial Randomness……………….………....…….……..…..21
Calculating Probability p from an Approximate Sampling Distribution……………...29
Discovering Features Within a Site……………..……….…..………..…....…………29
Correlation Analysis……………………..………………..…..…...……..…………...31
Exact Multi-Response Permutation Procedures (EMRPP)………...………….………33
Graph Theory and Network Analysis……………..…………..…………………..…..35

3. Intrasite Spatial Analysis……………………………………...….…………………...43
Definition of Activity Area…………………………...…………............…...………..43
Dichotomy of House and Outdoor Activity Areas……………………………..……..43
Criteria for Identifying House and Outdoor Activity Areas………………...….……..44
Locations of House and Outdoor Activity Areas in the Sampling Area of CA-SNI-
25………...………………….…………………..…………………....……….….47
Interpretation of Results…………...……...…………………..…………………..…...48
Tool Kits and Activity Areas in the Pitas Point Site, CA-VEN-27………..........….....57
Methods of Analysis……......…………...………………..…….….…….…...……….61
The Network Graph of Artifact Types in CA-VEN-27………...………….………….68
Interpretation of Results…………….....…..…………….……………….......………..71
Tool Kits and Activity Areas in CA-SNI-25…….……….……….……..…...…...…..87
Methods of Analysis….....…………………...…………………...……….......………87
Interpretation of Results……………..…………...…………………….……..…..…...90
Comparison of Tool Kits in CA-VEN-27 and CA-SNI-25…………….......…….….103
Conclusion…………..………..………………………………………….…..………104
4. Summary, Conclusions, and Recommendations for Future Research……………….109
Summary and Conclusions……………….…..…………...…………………..……..109
Recommendations for Future Research……………….…….……..…….…………..109
References Cited….…………...……………….…………...…………...…….………..114
Appendices……………………………………………………………....……………...123
A. Excel 4.0 Macros……………………………………………..…………..……….123
B. CA-SNI-25 Artifact Types and Locations…………….….……………...……….126
C. CA-SNI-25 Flake Length Tables...……………...…………..………...………….130

D. CA-SNI-11 Tarring Pebble Data Table...….………...……………….…………..131
E. CA-SNI-25 Artifact Pictures…………………..……………………....………….132

CHAPTER 1
INTRODUCTION
Importance of Studying Internal Site Structure
The spatial distribution of cultural materials within an archaeological site results
to a large degree from past human activity. It can be postulated that the various types of
activities that occurred within an archaeological site are the dynamic components or
subsystems of a larger social system. In considering the nature of a social system Harary
and Batell (1981) introduce a general graph-theoretic model that is applicable to any
hierarchical system. In this model a social system can be formally defined as a nested
social network whose underlying structure is a nested graph. The components of the
underlying structure clearly are the various subsystems that collectively compose the
structure of the entire system, which in turn influences the structure of the various
subsystems. All considered it is expected that the traces of past human activity within an
archaeological site when viewed as the remnants of specific social subsystems must be
recurrent and have identifiable structure. Therefore, discovering the internal structure of
an archaeological site is a necessary step toward understanding how the entire social
system of a particular prehistoric society functioned and was maintained.
There is a major gap in knowledge concerning the organization of activities
within prehistoric archaeological sites in coastal southern California.
Gamble comments on this deficit of knowledge of prehistoric site organization in coastal
southern California.

Although houses in the Chumash area have been excavated, little is known
about the organization of activities within houses (Gamble 1983:103).
In this thesis information gained from the intrasite spatial analysis of mapped surface
artifacts at CA-SNI-25, a Late period village site on San Nicolas Island, California, and
counts of typed artifacts from an excavation directed by Chester King of a late Middle
period to Late period coastal mainland village site CA-VEN-27 (hereafter referred to as
VEN-27) in southern California will be used to reduce this gap in knowledge. In fact in
the VEN-27 excavation the primary research goal was to discover and understand the
spatial organization within the site (King 2003, personal communication). Few other sites
have been excavated in this way in southern California, which further justifies using data
from this site in a spatial analysis.
Description and Location
San Nicolas Island is approximately 120 km southwest from Los Angeles and 98
km from the closest point on the southern California shoreline in the vicinity of Point
Mugu. (Figure1). This distinguishes San Nicolas Island as the most distant of the
California Channel Islands from the mainland. San Nicolas Island is small, with a
variable length of 14 to 15.3 km depending on the presence or absence of the sand spit at
the eastern end. The maximum width of the island is 5 km (Martz 2002). San Nicolas
Island is one of four islands, which constitute the southern group (Figure 1). The southern
group and the northern island group have remained disjunctive from one another as well
as the mainland throughout their entire geological history. The Southern Channel Island
group also establishes the western end of the Peninsular Ranges (California Coastal
Commission 1987).

Geology, Topography, and Environmental Setting
The geology of San Nicolas Island has been described as “a faulted asymmetric
anticline composed of Pleistocene sediments lying unconformably on Eocene sandstone
and shale (Meighan and Eberhart 1953:109). Erosion has resulted in the formation of 11
recognized terraces (Vedder and Norris 1963). San Nicolas Island is 276 meters at it
highest point and is mostly unprotected from frequent and strong northwesterly winds,
which average 25 km per hour. The low-lying topography of the island also contributes to
a very low average annual precipitation of 17.8 cm. This results in a xeric terrestrial
environment, which in the absence of fog drip would be classifiable as a desert on the
basis of less than 25.4 cm of rainfall in an average year.
Reinman and Lauter (1984) divided the island into three zones: (1) northern
coastal terrace; (2) southeast coastal terrace; and (3) central plateau (above the 400-foot
contour).
The plateau is characterized by being open and flat, and by the presence of
stabilized dunes. This area also contains eroded dunes, sand and sandy loam soils, cobble
outcrops, and deflated areas of caliche.

Eroded cliffs and dry canyons surround the plateau and drop abruptly to the sea. The
shoreline of the island is mostly rocky intertidal, though sandy beaches, dunes, and
coastal flats are also present. Fresh water on the island exists as springs, seeps, water
catchments, and an intermittently perennial watercourse, Tule Creek (Martz 2002).
The native flora of San Nicolas Island consists primarily of decumbent and low
growing herbaceous perennial shrubs along with annual and biennial flowers and grasses.
Trees with the possible exception of Salix lasiolepis (arroyo willow) are not native to this
island (Foreman 1967). Coreopsis gigantea and a common low growing lupine are
keystone perennials in the dune and coastal strand plant communities of the island. Two
native plants may have been managed as crops on the island. The small bulb
Dichelostemma capitatum (blue dicks) is well adapted to the harsh environment of San
Nicolas Island and is locally abundant on the plateau in the spring. This diminutive
member of the lily family may have been managed as a crop by the native inhabitants and
was undoubtedly an important food plant. Bulbs of this plant were likely roasted in earth
ovens and stored in baskets and pits (King 2002). The small annual flower Calandrinia
ciliata (red maids) blooms on the island during the spring. Seeds of this annual are known
to have been an important food source in coastal southern California and it has been
hypothesized that red maids were managed as a crop (King 2002). The fruit and pads of
the prickly pear cactus Opuntia littoralis were also predictably an important crop on San
Nicolas Island and the seasonal fruit was likely stored in baskets and/or pits as a rich and
long-term source of carbohydrates. The pads of prickly pear cactus were also likely used
for an unobvious purpose on San Nicolas Island, namely as fish bait. Fages (1775)

provides an ethnohistoric description of the use of cactus in sardine (Sardinops sagax
caeruleus) fishing by the Chumash of southern California.
For catching sardines they use large baskets, into which they throw the
bait, which these fish like, which is the ground up leaves of cactus, so that
they come in great numbers; the Indians make their cast and catch great
numbers of the sardines (Priestly 1972: 51).
Terrestrial mammalian and reptilian fauna native to the island are Peromyscus
maniculatus (white footed deer mouse) and Xantusia riversiana (island night lizard). It is
suspected that people brought Urocyon littoralis (island fox) to the island from the
northern Channel Islands before 5000 B.P. (Collins 1982; Vellanoweth 1996). A land
snail (Helix sp.) is also believed to be native.
Zalophus californianus (California sea lion) and Mirounga angustirostris
(northern elephant seal) are common on the beaches. Enhydra lutris (sea otter) are no
longer extant but were common in the kelp beds off the island prior to being locally
extirpated in the 19th century by fur hunters. Numerous marine birds such as pelicans,
gulls, and cormorants are resident species on the island. The assemblage of fishes found
in the waters off the island is both large and diverse. Sebastes sp. (rockfish),
Semicossyphus pulcheri (sheephead), and Thunnus thynnus (bluefin tuna) are examples of
the many fish species that were taken by the native inhabitants in the waters surrounding
the island. Bluefin tuna may have been traded to other islands and the mainland for items
such as banded chert, fused shale, deer meat, etc. Shellfish species found in the rocky
intertidal and kelp forest habitats of the island include Haliotis sp. (abalone), Mytilus sp.
(mussel), Strongylocentrotus sp. (sea urchin), Tegula sp. (turban snail), and Lottia sp.
(limpet). The native inhabitants heavily exploited all of these shellfish species.

CA-SNI-25
CA-SNI-25 (hereafter the site will be called SNI-25) is located on the northwest
plateau (Figure 2) and is considered to be a substantial habitation site (Martz 2002). The
expectation that SNI-25 is a substantial habitation site is strengthened by Malcolm
Rogers’ description of houses in SNI-25 in his unpublished field notes:
The community houses here are large about 35 ft. in diameter - one (No.
1) measured 40 ft. with much whale ribs in it. In working over the old
diggings here we removed beads, steatite, arrowheads, carved and inlaid
bone (hematite) and one painted mortar (Steve Schwartz 2003, personal
communication).
The research potential for SNI-25 is considered good and the research domains
identified for the site are: (1) Settlement; (2) Technology; (3) Subsistence; (4)
Chronology; (5) Trade; (6) Post Depositional Processes (Martz 2002: 29). The time of
occupation for SNI-25 is ca. AD 1225 to 1445 based on calibrated radiocarbon dates
(Martz 2003, personal communication). SNI-25 has a maximum length of approximately
600 meters along a northwest to southeast line and a maximum width of about 300 meters
along a northeast to southwest line, based on measurements from a topographic map of
the site. The contour interval of this map is 5 feet and the scale is 8.25 inches per 280

The contour interval of this map is 5 feet and the scale is 8.25 inches per 280 meters. An
ellipse with semi-axes a=150 meters and b=300 meters gives an estimate of the site area
as ! ∗ ! ∗ ! ≈ 141,372 square meters. The topography of SNI-25 (Figures 2 and 3) is
level and a steep slope marks the northern boundary of the site. The substrate of the site is
mostly sand and shell, which supports a dense cover of low growing herbaceous
perennials that include the insular endemics Astragalus traskiae and Lotus argophyllus
subsp. ornithopus. SNI-25 is situated about a hundred meters above the northern
shoreline of San Nicolas Island and offers a commanding view of the northern Channel
Islands, especially Santa Cruz Island. Extensive rocky intertidal and kelp forest habitat
are only a few hundred meters north of the site. These habitats were important sources of
protein and raw materials such as shell and sea grass cordage for the inhabitants of SNI-
25. Also, an excellent location for launching and landing rafts and canoes is located a few
hundred meters northeast of the site.
Overall Aims and Potential Contributions to Future Research
Information concerning the spatial relationships among stone artifact types permits the
identification of tool associations or "tool kits” and their relationship to discrete locations
within a site where organized activities took place. Areas of organized activity (called
activity areas) collectively define the internal organization of a site. Knowledge of the
internal organization of a site can then be used in conjunction with data obtained from
subsistence studies, local chronologies of artifact types, ethnographic analogy, and so on
to assist in building site-specific models of social systems and subsystems.

Such site-specific models can be used to test hypotheses concerning social organization,
subsistence and mobilization strategies, emergy flows and storages (Odum 1996, 2000),
and population dynamics. Social systems and subsystems can be quantitatively modeled
and simulated using the methods developed by Odum (1971), Odum and Peterson (1996),
and Odum and Odum (2000). For example it is expected that there were activity areas in
SNI-25 involved in the manufacture and maintenance of circular shellfish hooks, fishing
line, fishing nets, and other technology used in the capture of inshore, pelagic, and deep-water
fishes. Such activity areas are assumed to have required specific tool kits whose
remnants persist in part as non-random clusters of surface artifacts. It is further assumed,
in lieu of pre and post depositional disturbances, that non-random clusters of surface
artifacts are amenable to discovery using applied mathematical techniques termed pattern
recognition methods. The types of artifacts and specifically the intrasite surface locations
as well as the surface and/or subsurface frequencies of artifact types are assumed to be
sufficient to identify tool kits using methods such as correlation analysis and multi-response
permutation procedures in combination with graph theory and network analysis.
Once identified the tool kits corresponding to a specific type of activity area can be used
in replicative experiments aimed at measuring the parameters of energy flow models.
Clearly human behavior is the dynamic component of the formation, function, and
maintenance of activity areas.
Background and Significance
A variety of different mathematical techniques have been applied to the study of
areas of organized human activity in archaeological sites. One of the better techniques is
an unconstrained methodology, which was developed specifically for spatial analysis in

archaeological sites and is discussed and applied by its creator (Whallon 1984). Of all the
lessons learned in applying mathematical techniques to archaeological data one of the
most important has been that one technique may both reveal as well as obscure patterns
in the data, whereas another analytical approach will reveal and obscure different patterns
in the data. More than one mathematical technique is therefore often needed to perform
an analysis on archaeological data, especially an intrasite spatial analysis. Archaeological
data sets are not simple and are often multimodal, multilayered, and highly complex. As
Whallon (1984: 243) points out in applying analytical methods to archaeological data,
methods should be developed “…which operate specifically in accord with the problem
being investigated, the models believed to represent the processes involved, and the
consequent structure of the data which bear on these problems”.
Research Goals
San Nicolas Island is a model location for conducting archaeological research. A
primary goal of this thesis is to identify and analyze activity areas in SNI-25 using
mapped and typed surface artifacts in conjunction with sophisticated mathematical
analyses, intersite comparison, replicative studies, and ethnographic analogy. For
example, as previously mentioned observed spatial relationships between artifacts in
SNI-25 and VEN-27 will be used to identify tool associations and relate these to specific
types of activity areas. Also, information from experimental studies will be used in
correlating artifact morphology with function (Keeley 1980). Ethnographic analogy as
available will also be used to make inferences about artifact function. In addition, some
of the mathematical techniques (e.g. graph theory and network analysis) that will be used
in this thesis along with other methods in the attempt to objectively identify elements of

activity sets or tool kits in SNI-25 and VEN-27 will see their first application to the study
of California prehistory. This adds both to the development and rigor of archaeological
methodology.
Importance of this Research
This thesis provides important information concerning the internal organization of
a substantial habitation site of a prehistoric hunter-gather society. Investigations into the
internal organization of an archaeological site provide important information pertaining
to the spatial behavior of the former occupants of the site. Spatial behavior is a function
of culture (Kent 1984), which in turn is shaped and forms an adaptation to the natural
environment under the paradigm of cultural ecology. Knowledge of the internal
organization of a site can be used to build theories pertaining to social organization, trade,
as well as subsistence and mobility strategies. Learning about the internal organization of
SNI-25 will add to the general knowledge concerning cultural adaptations of island
hunter-gatherer societies. It is in this capacity my thesis will add to general
archaeological theory.
Finally, this thesis will reduce some of the data gaps that relate to the functional
use of space in prehistoric substantial habitation sites on San Nicolas Island. The use of
space in any human society is determined by variables such as the environment, status,
skills, age, gender, and time. In addition, part of the analysis in this thesis will be used to
extract additional insight from provenience-based archaeological data collected over
thirty years ago in VEN-27. Discovering commonality in the spatial and compositional
structure of activity areas in SNI-25 with activity areas in local and distant hunter-gather

settlements of any time period can help answer broader questions concerning regional
patterns in settlement systems and social organization.
Pre- and Post-Depositional Disturbance
In searching for activity areas in a site using mapped surface artifacts in
conjunction with mathematical analysis the confounding effects of both pre and post-depositional
disturbances are a major concern. The effect of pre-depositional disturbance
on site structure on San Nicolas Island is a research domain greatly in need of attention.
Many of the sites on the island do not appear to have been significantly degraded by post-depositional
disturbance. Some damage to archaeological sites has been attributed to
post-depositional disturbance. Erosion, construction, and collecting are the principle
types of disturbance, but in general site preservation is perceived to be good (Schwartz
and Martz 1992). Also, the absence of bioturbating animals such as pocket gophers on the
island means that post-depositional size sorting of cultural materials within a site is not as
significant a concern as would be the case in a coastal village site on the mainland.
However, during the occupation of a site both discard activities and movement of people
cause unintended size sorting and dispersal of artifacts. Such processes may result in non-random
clusters of surface artifacts that are subject to misinterpretation as areas of
organized human activity. Movement of people in a site results in scuffage (horizontal
displacement) and trampling (vertical sorting) of artifacts. SNI-25 contains a loose sandy
substrate, which appears to be the most effective type in reducing the confounding effect
of scuffage (Gifford-Gonzalez et al. 1985).

Research Questions and Hypotheses
Internal Site Organization
!!: What types of activity areas are present at SNI-25?
Hypotheses
!!: It is hypothesized that the following activities were conducted at SNI-25: (1)
Activity areas outside of houses consisting of three primary types. Type 1a Areas:
Where fishing equipment was manufactured and repaired. Type 1b Areas: Where
butchering of fish and marine mammals took place. Type 1c Areas: Where flake tools
as well as bone and shell tools were manufactured. (2) Activity areas inside or just
outside of houses consisting of four primary types. Type 2a Areas: Where food
was prepared. Type 2b Areas: Where food was cooked. Type 2c Areas: Where
ground stone tools were manufactured. Type 2d Areas: Where baskets, bone awls,
and asphaltum containers were manufactured.
Non-random clusters of surface artifacts result from organized human activities
and identify activity areas in SNI-25. Each type of activity area in SNI-25 has a
distinctive and structured association of constituent artifacts.
Expectations
An excavation at a coastal Chumash village site CA-VEN-27 which is
contemporaneous and which has a remarkably similar stone artifact assemblage to
SNI-25 provides a means for predicting activity area types at SNI-25. Based on the
results of excavations at VEN-27 it is expected that fishhook blanks, fishhook drills,
and domed scrapers will occur in significantly higher relative frequencies in Type 1a

Areas than in other types of activity areas. It is expected that Type 1b Areas will have
significantly higher relative frequencies of flake knives and butchered bone than other
types of activity areas. It is expected that Type 1c Areas will have significantly higher
relative frequencies of flaking hammers (small end-battered stones) as compared to
other activity area types. It is expected that Type 2a Areas will have significantly
higher relative frequencies of bowl mortar fragments and pestles than other activity
area types. Type 2b Areas are clearly expected to have fire affected rock (FAR) and
possibly FAR recognizable as a rock-lined hearth. Type 2c Areas are expected to
have significantly higher relative frequencies of heavy and dense stone (quartzite or
porphyritic igneous rock) hammers as well as cobble choppers as compared to other
activity area types. Type 2d Areas are expected to have significantly higher relative
frequencies of tarring pebbles and/or asphaltum applicators than other activity area
types.
The Clark and Evans (1954) nearest neighbor statistic in conjunction with
randomization tests are used in this study to locate non-random clusters of surface
artifacts in SNI-25. Graph theoretical methods in conjunction with network analysis
are used to identify “cliques” or associations of surface artifacts in SNI-25 and
associations of excavated artifacts in VEN-27. Ethnographic and historic data in
combination with data from the archaeological record is used to place each “clique”
of artifacts into a specific type of activity area. This process elucidates the presence or
absence of the hypothesized types of activity areas in SNI-25 and VEN-27 and can
also infer the presence of types not included in the hypothesis.

CHAPTER 2
METHODOLOGY
Sampling Procedure
The artifact location data analyzed in this thesis required four visits to SNI-25 to
collect. The first visit was aimed at precisely defining the four edges of the 20 x 45 meter
sampling area, as well as referencing the northwest corner of this area to the site datum.
A theodolite and stadia rod were used to measure linear distances and angles. Sixteen
hours, and a crew of four (including myself) were needed to complete this task. The 2nd
and 4th visit to SNI-25 was directed at the location and intensive recordation of surface
artifact positions in the 20 x 45 meter sampling area using a hand held Global Positioning
System (GPS) unit together with a metric tape. The metric tape was needed to measure
inter-artifact distances too small to be distinguishable with the available GPS unit. Once
located, a surface artifact was marked with a numbered pin flag, digitally photographed,
and its type (material and morphological) and position recorded. The field number of
each recorded artifact is the same as the number on the pin flag used to mark its location.
Sixteen hours and two people (myself and a student assistant) were needed to accomplish
this. The desired goal was to locate and map all surface artifacts in the sampling area. I
believe a majority of the surface artifacts in the sampling area were found, because of
high surface visibility over much of this area. However, low-lying vegetation (especially
perennial lupine) did reduce the sample size. Removal of vegetation from SNI-25 in the
interest of surface artifact mapping was not allowed because of well-founded concerns
pertaining to the potential for long-term damage to the sensitive island ecology as well as

to SNI-25 itself through increased erosion. The confounding effect of reduction in sample
size, as the result of plant cover does not appear to be significant based on the results of
the intrasite spatial analysis.
The flake length data analyzed in this thesis required approximately two hours
and a single visit to SNI-25 to collect. One person measured the flake lengths with a
vernier caliper and another person recorded these measurements on spreadsheet form.
The Applied Mathematical Methods Used in this Study
What follows is a detailed development and discussion of the mathematical
methods that are used in this thesis to analyze mapped and typed surface artifacts in the
20 x 45 meter sample area in SNI-25. The Clark and Evans nearest neighbor statistic is
used in conjunction with randomization tests. This type of data exploration procedure
falls into the applied mathematical category termed pattern recognition. (Hietala and
Stevens 1977) discuss a number of other pattern recognition procedures and their
potential for recognition and interpretation of cultural pattern represented by distributions
of artifacts on the surfaces of archaeological sites. Multi-response permutation
procedures (MRPP) (Mielke, Berry, and Johnson 1976) are recommended for detecting
“the intrasite patterning of artifact class distributions in an archaeological space” (Berry,
Kvamme, and Mielke 1980). Refinements in the application of MRPP to the intrasite
spatial analysis of artifact distributions are given in Berry, Kvamme, and Mielke (1983)
and Berry, Mielke, and Kvamme (1984). MRPP will be used in this thesis to study the
patterning of nine surface artifact types in the sampling area of SNI-25. Graph theory and

network analysis is used in conjunction with correlation analysis (VEN-27) and MRPP
(SNI-25) to identify tool kits.
The Clark & Evans Nearest Neighbor Statistic and its Previous Use in Archaeology
Numerous workers in archaeology over the past 30 years have used the (Clark and
Evans 1954) nearest neighbor statistic in the attempt to identify non random patterns at
all scales, from the level of large regional center or village (Earle 1976) down to the
small scale of stone tools distributed on occupation floors (Whallon 1974).
For example, in his 1974 paper, Whallon applies a Clark and Evans nearest neighbor
analysis to four tool types distributed on a Protomagdalenian occupation floor at the Abri
Pataud in southwestern France. The four types are: endscrapers, worked bone and antler,
retouched blades, and partially backed blades. He found that in the site, the mean nearest
neighbor distances of each tool type was much less than the average nearest neighbor
distances expected in a random distribution. In his test of significance for clustering at the
five percent level he assumes that the statistical distribution of nearest neighbor distances
is approximately normal. For his significance test Whallon uses a chi-square standard
normal deviate of the form:
! = 2!! − 2! − 1, where ! = 2! > 30 is the number of degrees of freedom.
Whallon found all four tool types to be significantly clustered at the five percent level.
However, he acknowledges a potential problem with assuming that the distributions of
the observed nearest neighbor distance are approximately normal:
The distributions of the observed nearest neighbor distances certainly look far
from normal in most cases. Indeed, from these four cases plus numerous others from this
same occupation, one gets the impression that

the distribution of actual nearest neighbor distances in a clustered pattern
may be positively skewed, multimodal, and may frequently have several
high, outlying values far greater than the bulk of the distances. Exactly
how to handle this and to adequately and reasonably define a “cut-off”
point is obviously in need of further work (Whallon 1974:33).
It is clear that unlike some who have used the Clark and Evans nearest neighbor statistic
in the spatial analysis of archaeological data Whallon realized that the exact sampling
distributions of this statistic are complicated. What follows is the description of a method
from computational mathematics, which provides a means to accurately approximate the
exact sampling distributions of the Clark and Evans nearest neighbor statistic.
A Monte Carlo Test of Spatial Randomness
A Monte Carlo test as a method for detecting spatial randomness is described as follows:
Given a simple null hypothesis !! and a set of relevant data, Monte Carlo
testing consists simply of ranking the value !! among a corresponding set
of values generated by random sampling from the null hypothesis of !.
When the distribution of ! is effectively continuous, the rank of the
observed test statistic !! among the complete set of values !!: ! =
1,⋯,! determines an exact significance level for the test since, under
!!, each of the ! possible rankings of !! are equally likely. To obtain an
exact assessment of the significance of !!we need only carry out ! − 1
simulations of events distributed uniformly and independently in a given
finite region ! and hence calculate the corresponding quantities!!,⋯, !!.
The significance level is then evaluated from the rank of !! among the
order-statistics ! ! < ⋯ < ! ! . Note that any shape of region can be
accommodated and that no correction for edge effects is required,
although some degree of conditioning on the locations of events near the
boundary of ! may be desirable (Besag and Diggle 1977: 327-328).
How should the significance of a measured Clark and Evan’s nearest neighbor statistic
! in a sampling window or area containing ! > 1 surface artifacts be determined? A
practical choice is a square quadrat as a “sampling window” on the surface of an
archaeological site. It is true that a square has a shorter perimeter and is therefore less

subject to edge effects than a rectangle. But as was stated above, correction for edge
effects is not a concern with this test and the choice of a square quadrat for sampling
surface artifacts is mainly one of convenience. Using a computer, pairs of pseudo random
numbers are generated within a !"! square, ! times. This is accomplished for each
random point !, ! by multiplying both computer generated pseudo random numbers
!and ! by !. Note that 0 ≤ ! ≤ 1 and 0 ≤ ! ≤ 1 . Therefore each computer
simulated random point in a !"! quadrat will have the form ! ∗ !, ! ∗ ! . The Clark
and Evans nearest neighbor statistic is then computed. Next an approximate sampling
distribution (Eddington, 1969) for the Clark and Evans nearest neighbor statistic is
computed for ! points in a !"! square from the entire sampling distribution of the
statistic. This is done by iterating or simulating the above procedure a large number of
times. But how many times? The procedure for answering this question is found in
Marriot (1979). The procedure follows.
It must be decided whether to accept or reject the null hypothesis !!. In this study
the null hypothesis is that ! surface artifact locations in a !"! quadrat are randomly
distributed. As is usual in statistical practice the null hypothesis is rejected at the five
percent level of significance. This means that if the null hypothesis is true there is a
probability of no greater than 0.05 of rejecting it.
Next the probability ! of rejecting the null hypothesis using a Monte Carlo test at
the five percent level given a specific number of iterations ! is considered. Ninety-one
different values of ! at seven different levels of significance were calculated using the
Excel macro (Table 1). The values of ! from these calculations are listed in Table 2. It is
necessary to determine the number of Monte Carlo simulations ! before testing

whether the spatial pattern of ! surface artifacts in a !"! quadrat is nonrandom. To
accomplish this a Clark & Evans statistic ! is calculated from real data. Then suppose it
is desired to carry out a one-tailed significance test of size !. It has already been decided
that ! = 0.05. Therefore values of ! and ! must be chosen so that ! ! = ! and
following this ! Monte Carlo simulations are performed. This gives ! random samples
!!,⋯, !!. If ! is among the ! largest values of the statistic then the null hypothesis !!
that the ! surface artifacts in the !"! quadrat have a random planar distribution is
rejected. The probability of rejecting !! using the Monte Carlo test is:
!
! !!!!!! !!
!! , where !
! = !!
!! !!! !, and ! = ! ∗ !
As is apparent in Table 2 increasing the number of iterations produces ever-smaller
values of ! in the columns 0.9, 0.925, and 0.94. Therefore, as the number of
iterations increases so does the chance of correctly accepting the null hypothesis. For
columns 0.96, 0.975, and 0.99 in Table 2 the opposite is true; ! increases in accord with
an increase in the number of iterations. Therefore as the number of iterations increases so
does the likelihood of correctly rejecting the null hypothesis. From Table 2 and Figures 4
and 5 it is clear that the probability of rejecting the null hypothesis using a Monte Carlo
test at the five percent level of significance becomes negligibly small for the three values
in Table 2 in the interval [0.9,0.95), after a thousand iterations. Table 2 was constructed
using the following Excel 4.0 Macro (Table 1), which I wrote. The opposite is true for the
three values in Table 2 in the interval (0.95,0.99]. As is clear in Figures 6 and 7 the
probability of rejecting the null hypothesis using a Monte Carlo test at the five percent

level of significance is well over 0.9, after a thousand iterations. Based on the results in
Table 2, in most cases one thousand iterations will produce an approximate sampling
distribution of the Clark and Evans nearest neighbor statistic that will give a correct result
when used to test the null hypothesis at the five percent level of significance. Examining
Table 2 one thousand five hundred iterations will produce an approximate sampling
distribution of the Clark and Evans nearest neighbor statistic that should correctly test the
null hypothesis at the five percent level of significance in almost every case.
Table 1. Excel 4.0 macro for computing !
!
!!
!! !!!!!!.
Row
Column of Spreadsheet is A
1 =SELECT(OFFSET(ACTIVE.CELL(),0,1))
2 =INPUT("Enter the value of p",1)
3 =INPUT("Enter the value of n",1)
4 =INPUT("Enter the value of alpha",1)
5 =SET.NAME("Counter",0)
6 =SET.NAME("Q",0)
7 =FOR("countb",0,A3*A4)
8 =COMBIN(A3,Counter)
9 =A2^(A3-Counter)
10 =1-A2
11 =A10^Counter
12 =A8*A9*A11
13 =SET.NAME("Q",Q+A12)
14 =SET.NAME("Counter",Counter+1)
15 =SELECT(OFFSET(ACTIVE.CELL(),1,0))
16 =NEXT()
17 =SELECT(OFFSET(ACTIVE.CELL(),-Counter+1,0))
18 =FORMULA(Q)
19 =RETURN()

Table 2. Various probabilities of incorrectly rejecting (Actual P-value < 0.95) or correctly
rejecting (Actual P-value ≥ 0.95) the null hypothesis for a given number of iterations.
alpha=0.05 Actual P-value
m/n=alpha 0.9 0.925 0.94 0.95 0.96 0.975 0.99
Iterations (n) m
100 5 5.76E-02 2.31E-01 4.41E-01 6.16E-01 7.88E-01 9.600841477E-01 9.994654655E-01
125 6.25 2.83E-02 1.64E-01 3.72E-01 5.65E-01 7.65E-01 9.618475847E-01 9.997147459E-01
150 7.5 1.40E-02 1.18E-01 3.17E-01 5.23E-01 7.47E-01 9.643657741E-01 9.998504429E-01
250 12.5 2.13E-03 6.01E-02 2.60E-01 5.18E-01 7.95E-01 9.890019749E-01 9.999980641E-01
350 17.5 3.46E-04 3.21E-02 2.19E-01 5.15E-01 8.32E-01 9.964365184E-01 9.999999732E-01
500 25 3.54E-05 1.67E-02 2.00E-01 5.53E-01 8.92E-01 9.995373056E-01 1.000000000E+00
700 35 1.07E-06 5.25E-03 1.50E-01 5.45E-01 9.22E-01 9.999450192E-01 1.000000000E+00
1000 50 6.00E-09 9.82E-04 1.01E-01 5.38E-01 9.51E-01 9.999976322E-01 1.000000000E+00
1500 75 1.16E-12 6.50E-05 5.45E-02 5.31E-01 9.76E-01 9.999999865E-01 1.000000000E+00
2000 100 2.37E-16 4.53E-06 3.06E-02 5.27E-01 9.88E-01 9.999999999E-01 1.000000000E+00
2500 125 5.01E-20 3.24E-07 1.76E-02 5.24E-01 9.94E-01 1.000000000E+00 1.000000000E+00
3000 150 1.08E-23 2.37E-08 1.02E-02 5.22E-01 9.97E-01 1.000000000E+00 1.000000000E+00
3500 175 2.36E-27 1.75E-09 5.99E-03 5.20E-01 9.98E-01 1.000000000E+00 1.000000000E+00
Figure 4. Plot of the probability of making a Type I error using an approximate
sampling distribution for a given number of iterations, when the P-value of the
actual distribution is at the 10% level of significance.

Figure 5. Plot of the probability of making a Type I error using an approximate
sampling distribution for a given number of iterations, when the P-value of the
actual distribution is at the 6% level of significance.

approximate sampling distribution for a given number of iterations, when the P-value
of the actual distribution is at the 4% level of significance.

approximate sampling distribution for a given number of iterations, when the P-value
of the actual distribution is at the 2.5% level of significance.
Calculating Probability p from an Approximate Sampling Distribution
The first step in calculating ! from the computed approximate sampling
distribution is to calculate the median. If !! equals the number of iterations of the Clark
and Evans nearest neighbor statistic and !!, !!,⋯, !!! are the !! values of the statistic
computed in the Monte Carlo simulation, then the median is calculated by:! = !
!! ∗
!!
!!! !!
. If ! < !, and there are !! !! ≤ ! then ! = !!
!! . If ! > ! and there are !! !! ≥ !
then ! = !!
!!.

Figures 8 and 9 present two examples of approximate sampling distributions computed
for the Clark and Evans nearest neighbor statistic using an Excel 4.0 macro I wrote,
Appendix A.
Discovering Features Within a Site
As was described in a previous section, the approximate sampling distribution of
the Clark and Evans nearest neighbor statistic for a specified number ! of pseudo-randomly
placed points in an !"! sampling window can be generated using a computer.
With this approximate sampling distribution (as was described in the last section) the
probability ! that the measured nearest neighbor statistic for ! = ! surface artifacts in a
!"! quadrat is the result of random chance can be calculated. This further gives the
probability that the ! artifacts are distributed at random over the surface of the site
enclosed by the quadrat. If the value of ! < 0.05 one of two things can also be said about
the artifacts in the recognition that their spatial pattern contains significant structure.
(1) If ! < 1 the artifacts show a tendency for clustering. This tendency increases as !
becomes smaller.
(2) If ! > 1 the artifacts tend to be repulsed or regularly spaced.
Here all artifact types are sampled together within each !"! quadrat. The purpose being
to identify features such as houses or house areas and outdoor activity areas. This is
possible because archaeological excavations as
well as ethnographic studies have provided sufficient evidence that supports the
expectation that certain artifact associations can be correlated with house areas and others
with outdoor activity areas.

Figure 8. Approximate sampling distribution for the Clark and Evans nearest
neighbor statistic in a !"#!" area containing 3 points. 10,000 iterations.
Figure 9. Approximate sampling distribution for the Clark and Evans nearest
neighbor statistic in a !"#!" area containing 8 points. 8,625 iterations.

Correlation Analysis
The type of correlation analysis that is used in this study is often referred to as a
Pearson correlation analysis. Counts of typed artifacts are the raw data in the current
analysis. The correlation coefficients computed from the raw data are arranged in a
symmetric !"!"!#$!" = !"!#!$%!" !"! matrix consisting of !! correlation coefficients,
!!", where
!!" = !"#(!,!)
!!∗!!
=
!
!!! !!"!!!
!!
!!
! !!
!!
!
!!! ! !!"!!! !)( !!"!!!
!!
!!
!!
= !!"!!! !!"!!!
!!
!!"!!! !)( !!"!!!
! !!
!!
!!
!!
In the present study ! = 4 (Area 1, Area 2, Area 3, and Area 5 in VEN-27).
In the preceding formula for!!", !"# !, ! is the covariance of ! and !, and !! ∗ !! is the
product of the standard deviations of ! and !, respectively. The resulting sample
correlation matrix is of the form.
! =
1 !!"
!!" 1
⋯ !!!
⋯ !!!
⋮ ⋮
!!! !!!
⋯ ⋮
⋯ 1
For the matrix in the present study !!" is a comparison between artifact type ! and artifact
type !. Each !!"! 0,1 ⊃ ℝ (the set of real numbers) and as applied in this study is a
statistical measure of how well the frequency of artifact type ! moves together with the
frequency of artifact type ! between four excavated areas in the Pitas Point site (VEN-
27). In the extreme case !!" = 1, the two artifact types are inferred to have a complete
association and in the other extreme case, !!" = 0 the inference is that the artifacts have
no association. This statistic has been in use for many years since the mathematician Karl

Pearson formulated it. For a more comprehensive discussion of correlation analysis the
reader is referred to Rencher (1995: 65-70). The Pearson correlation coefficients in the
VEN-27 analysis were computed using the correlation option that is part of the data
analysis tool in Microsoft Excel.
Exact Multi-response Permutation Procedures (EMRPP)
A brief description of permutation tests in the general sense is as follows:
Permutation tests generally come in three types: exact, resampling, and
moment approximation tests. In an exact test, a suitable test statistic is
computed on the observed data associated with a collection of objects, and
then the data are permuted over all possible arrangements of the objects
and the test statistic is computed for each arrangement. The null
hypothesis !! specified by randomization implies that each arrangement
of objects is equally likely to occur. The proportion of arrangements with
test statistic values as extreme or more extreme than the value of the test
statistic computed on the original arrangement of the data is the exact P-value
(Mielke and Berry 2001: 2).
For the purposes of the present study a specific type of permutation test known as
an exact multi-response permutation procedure (EMRPP) will be used to compare the
distributions of pairs of artifact types within the sampling area of SNI-25.

Description of the EMRPP used in this Study
Δ!,! = !!! − !!!
! !!! − !!!
! defines the Euclidean distance between two
distinct artifact locations ! and ! within the site surface area being sampled. It is desired
to compare the intrasite distributions of two artifact types A and B. It is therefore
necessary to separately measure the clustering of the surface artifacts belonging to each
of the two types. Let !! be the number of distinct locations of surface artifact type A in
the sampling area of the site and !! the number of distinct locations of surface artifact
type B within the same area. Let ! = !! + !!. The average of the Δ!,! distances across
the site surface within the sampling area among all Δ!,! values for each of artifact types A
or B are given by the equations !! = !!! Δ!,!
!!
2 and !! = !!! Δ!,!
!!
2 , where
!!! is the sum over all distinct site surface locations ! and ! for each of the two
artifact types such that1 ≤ ! < ! ≤ !!, where ! = ! or!, and
!!
2 is the number of
distances between distinct surface artifact locations within the sampling area for artifact
type ! (! = ! or !).
A summary measure of the spatial overlap of the surface artifacts belonging to
each of the two types is reasonably given by the equation ! = !!
! !! + !!
! !!. The P-value
associated with an observed value of ! (say !!) is the probability under the null
hypothesis !! of observing a value of ! as extreme or more extreme than !!. In the
present study a P-value ≤ 0.05 identifies a significant non-overlapping distribution of
two surface artifact types within the sampling area of SNI-25. An exact P-value for the
purpose of the present study may be expressed as:

! ! ≤ !! !! (number of !′! ≤ !!) /!, where ! = !!
!!!×!!! .
The original algorithm for computing EMRPP P-values is given in Berry (1982)
and Berry and Mielke (1984). However, even with the enormous computing power of a
current desktop PC, Mielke and Berry (2001: 21) state as a rule of thumb that ! = 10! is
a reasonable cut-off for the computation of EMRPP P-values in most cases.
It follows that approximation methods are needed for the practical computation of
MRPP P-values for very large values of !. Monte Carlo (resampling) and Pearson type
III moment approximations are the two recommended procedures for computing MRRP
P-values when ! is very large. All three of these options are available in the Blossom
Statistical Software available over the Internet from the USGS (Mid-continent Ecological
Science Center, Fort Collins, CO) and are also as an online supplement to Mielke and
Berry (2001) as FORTRAN 77 programs (text only). The online supplement is a folder
which contains several electronic files and is available at Professor Mielke’s website. The
Blossom Statistical Software was used to perform each EMRPP in this thesis.
For didactic purposes Figure 10 and Tables 3 and 4 are provided in order to
illustrate the concepts and many of the details of the computation by hand of an EMRPP
for the simple case of two artifact types with two distinct intrasite surface locations for
one type and four distinct intrasite surface locations for the other type.

Figure 10. All Δ!,! (Euclidean distances) indicated as edges connecting nodes in the
graph where the nodes represent hypothetical surface artifact locations A through
F.

Table 3. Computation of delta for observed hypothetical distribution of scrapers and
choppers in Figure 10. The observed Euclidean distances used in this computation are
indicated as dashed edges in Figure 10.
AB; CDEF
Number Artifact Pair Euclidean Distance (meters)
1 A, B Scraper-Scraper 4
2 C, D Chopper-Chopper 6
3 C, E Chopper-Chopper 8.825531145
4 C, F Chopper-Chopper 11.43283867
5 D, E Chopper-Chopper 12.24295716
6 D, F Chopper-Chopper 13.60403617
7 E, F Chopper-Chopper 3.367758899
Sum of Chopper-Chopper Euclidean distances
55.47312204
55.47312204/6 = 9.245520339
(2/6)*4+(4/6)*(9.245520339)
delta = 7.49701355964778
Table 4. Delta values and corresponding P-values for each of the fifteen possible two-four
combinations of hypothetical surface artifact locations A through F in Figure 10.
Order Combination Observed delta Probability (Exact) of a smaller or
equal delta
1 CD; ABEF 4.625237699 1/15 = 0.0667
2 BF; ACDE 6.269733131 2/15 = 0.1333
3 EF; ABCD 6.960123351 3/15 = 0.2000
4* AB; CDEF 7.49701356 4/15 = 0.2667
5 BE; ACDF 7.673528201 5/15 = 0.3333
6 AF; BCDE 7.789464271 6/15 = 0.4000
7 AE; BCDF 7.858820482 7/15 = 0.4667
8 AD; BCEF 8.003970226 8/15 = 0.5333
9 CE; ABDF 8.145989313 9/15 = 0.6000
10 AC; BDEF 8.274176757 10/15 = 0.6667
11 BD; ACEF 8.764633501 11/15 = 0.7333
12 DE; ABCF 8.78498395 12/15 = 0.8000
13 CF; ABDE 9.159504623 13/15 = 0.8667
14 BC; ADEF 9.218536904 14/15 = 0.9333
15 DF; ABCE 9.244619921 15/15 = 1.0000
# of Permutations *Actual
M=6! /(2! *4!)=15

Graph Theory and Network Analysis
Graph Theoretic Definitions Required in the Present Study
The first three of the following definitions are taken directly from Gross and Yellen
(1999: 2,10, 48).
Definition 1. A graph ! = !!, !! is a mathematical structure consisting of two sets !!
and !!. The elements of !! are termed vertices (or nodes), and the elements of !! are
called edges. Each edge has a set of one or two vertices associated to it, which are known
as its endpoints.
Definition 2. A graph is simple if it has neither self-loops nor multi-edges.
Definition 3. A complete graph is a simple graph such that every pair of vertices is
joined by an edge.
Definition 4. A subgraph of a graph ! is a graph ! whose vertices and edges are all in
!.
Definition 5. A subgraph ! of !!, !! is called a clique or maximal complete subgraph
of !!, !! if every pair of vertices in ! is joined by at least one edge, and no proper
superset of ! has this property.
Definition 6. The adjacency matrix, !!, of a graph is a square matrix whose elements
!!" ! ≠ ! are 1 if nodes ! and ! are connected by an edge and 0 otherwise.
The Application of Graph Theory and Network Analysis in the Present Study
As was previously mentioned techniques from graph theory and network analysis
will be used to identify elements of tool kits in two contemporaneous maritime oriented
substantial habitation sites in southern California. The cultural chronology used presently
is that of King (1990: 28-44).

VEN-27 is a Middle to Late period or more specifically using King’s terminology
a Phase M5c- Phase L1c (A.D. 1050-1500) coastal Chumash village site, whereas SNI-25
is an exclusively Late period southern Channel Island village site whose time of
occupation (based on the previously mentioned calibrated radiocarbon dates) falls within
King’s Phase L1a- Phase L1c (A.D. 1225-1445). This means that VEN-27 and SNI-25
were occupied contemporaneously for a minimum of two hundred and twenty years.
Comparison of tool kits identified in the analysis of artifact types in VEN-27 and SNI-25
provide objective evidence in support of the proposition that there is a common regional
pattern in certain constituents of the material culture of Late period coastal hunter-gatherer
societies in southern California.
As mentioned previously, counts of typed artifacts from four excavated areas in
VEN-27 are used to construct a data matrix, which is used as raw data for a Pearson
correlation analysis. Next a cut off point for the correlation coefficient of the resulting
Pearson correlation matrix is determined. In this study it was determined that 0.78 is an
optimal cut off point for the correlation coefficient (c.c.). In this case all correlation
coefficients in the Pearson correlation matrix are coded 1 if they are in the interval
0.78 ≤ c.c. ≤ 1 and 0 if c.c. < 0.78. The coded correlation coefficients form an
adjacency matrix.
In the case of SNI-25 the Euclidean distances between all mapped surface
locations of a specific artifact type are used in the pair wise spatial analysis of selected
artifact types using exact multi-response permutation procedures (EMRPP) as described
in the previous section. The resulting P-values from these procedures are used to
construct an adjacency matrix. Here P-values ≤ 0.05 identify two artifact types whose

surface distribution within the sampling area of SNI-25 does not significantly overlap.
Therefore, in constructing this adjacency matrix P-values > 0.05 are coded 1 and P-values
≤ 0.05 are coded 0. In both matrices 1 in the adjacency matrix represents a
connection between two artifact types and 0 an absence of a relationship. The resulting
adjacency matrix for both VEN-27 and SNI-25 is raw data, and are entered directly in the
form of an ASCII file (e.g. Microsoft Windows “Notepad”) into the UCINET 6 for
Windows software package (Borgatti, Everett, and Freeman 2002). In this study the
UCINET 6 software is used to identify what in graph theory, are known as cliques as well
as to draw network graphs. A mathematical procedure using methods from linear algebra
for detecting cliques is given in Harary and Ross (1957). The algorithm implemented in
UCINET 6 is given in Bron and Kerbosch (1973). The Bron and Kerbosch (1973)
algorithm finds all Luce and Perry (1949) cliques greater than a specified size. In the
context of the present study cliques are interpreted as tool kits and in the network graphs
labeled solid circles (nodes) depict artifact types and lines (edges) connecting nodes
depict a significant relationship between two artifact types. Here the relationship is
spatial co-occurrence.
Advantages of Graph-theoretic Methods over Data Reduction Methods and
Clustering Procedures
The graph-theoretic methods used in this study are conservative in that no a priori
assumptions are made concerning the degree of homogeneity of the data being examined.
In fact, the internally cohesive groups (cliques) identified using graph theory result from
the structure of the data. Data reduction techniques such as principal components analysis
and factor analysis have been the preferred methods of archaeologists in the search for

‘tool kits’. A disadvantage in using such methods is that they are predicated upon
homogeneous data sets. This violates the very tenet of undertaking this kind of
exploratory analysis in the first place, which is the belief that the data are not
homogeneous (Read 1992). An advantage of using ordination techniques such as
principal components analysis on archaeological data is that they are effective at reducing
noise in data (Gauch 1982). Gauch (1982:1647) claims that eigenvector ordinations such
as those produced in principal components analysis are of three basic types:
(1) structure axes reflecting valid relationships, (2) spurious polynomial
axes, and (3) noise axes.
The magnitude of the correlation coefficients of the type used in the VEN-27 analysis is
influenced by noise as well as the extent of linearity in the structure of the intrasite spatial
distribution of two artifact types. This means that the magnitude of a correlation
coefficient is not a definitive measure of proximity in a spatial relationship between a pair
of artifact types. The spatial association of artifact types is more realistically represented
by the binary structure of an adjacency matrix. The adjacency matrix is analogous to the
correlation matrix with much of the noise removed.
Another analytical approach often employed by archaeologists, in their search for
uncovering structure in heterogeneous data, has been to use one of the varied assortment
of clustering algorithms, (Read 1992).
Read and Russell (1996:4) comment on the improper use of these procedures in
archaeology.
Generally no precise criteria have been used in applications by
archaeologists for deciding on the step that defines groups (Whallon
1990), and so groups determined are somewhat arbitrary.

A further disadvantage in using clustering procedures is that different algorithms produce
different results. Christenson and Read (1977) provide an archaeological example of this
dilemma.

CHAPTER 3
INTRASITE SPATIAL ANALYSIS
Definition of Activity Area
A definition of an archaeological activity area is:
A spatially restricted area where a specific task or set of related tasks have
been carried on, which is generally characterized by a scatter of tools,
waste products, and/or raw materials; a feature, or set of features, may also
be present (Flannery 1976:34).
Within the remnants of a specific type of activity area in an archaeological site it is
therefore expected that a characteristic sub-assemblage of the total assemblage of
artifacts contained within the site will repeatedly occur. Of the characteristic artifacts
comprising this sub-assemblage it is assumed that one or more (tool kits) used in specific
kinds of organized activities will be present. It is further assumed that the remaining set
of artifacts within an activity area will have a measurable non-random spatial
distribution. All of these assumptions rest on the tacit assumption that pre and post
depositional disturbances have not been sufficient to confound a meaningful intrasite
spatial analysis.
Dichotomy of House and Outdoor Activity Areas
In considering the distribution of utilitarian artifacts within a site, there are
apparent and consistent regional similarities in the way certain types of artifacts occur in
household activity areas and not in activity areas disjunctive from the locations of houses.
For example groundstone tools (e.g. manos and metates) primarily used in the processing

of plant materials appear to be universally linked to activity areas within or in close
proximity to houses. Groundstone tools are also strongly associated with women. As far
from Southern California as Mesoamerica, there is a strong connection between women
and the use of groundstone tools in household activity areas (Flannery and Winter 1976:
37). The linkage in the use of groundstone tools, women and household areas therefore
appears to be multi-regional and possibly universal. However, groundstone is but one
example of an apparent widespread pattern whereby certain utilitarian artifacts or sets of
such artifacts are associated with either male or female activities.
Criteria for Identifying House and Outdoor Activity Areas
House Locations and Activity Areas
A considerable amount of archaeological data pertaining to artifact types
associated with household activity areas in a maritime oriented southern California
prehistoric and historic substantial habitation site was gathered by members of the Van
Bergen-Los Angeles Museum Expedition of 1932. One of the goals of this expedition
was to collect more data on Chumash houses. In the pursuit of this goal the remains of
three houses (Houses A, B, and C) were completely excavated in the village of Muwu
(CA-VEN-11). The three houses were occupied into historic time by Chumash and were
in an excellent state of preservation at the time of their excavation. The village of Muwu
is located a few meters north east of Highway 1 (formerly Roosevelt Highway) on the
edge of a lagoon in the vicinity of Point Mugu in Ventura County (Woodward 1938:141).
Woodward (1932) gives the original description of the artifacts discovered in
Houses A, B, and C in the Muwu site. Based on these data I conjecture that the co-occurrence
and clustering of three of the artifact types found in House B in the 1932

excavation, as part of a non random surface artifact cluster, provides a non trivial
inference of a household location in a Middle to Historic period coastal village site in
Southern California. The presence of a surface cluster of fire affected rock (FAR) as part
of the total surface cluster strengthens this inference, and also indicates the location of a
hearth. The three artifact types are: (1) whole or fragmentary mortars, (2) whole or
fragmentary pestles, and (3) hammerstones. A detailed description of House B, which
includes mention of some of the cultural materials recovered from the floor of this
structure are given in (Gamble 1991:107). As will be discussed later, mortars, pestles,
and hammerstones are part of a household tool kit used in the processing and cooking of
food.
I propose that a second set of spatially co-occurring artifact types, as part of a
non-random surface artifact cluster, infer the location of a house. These artifact types are:
(1) hammerstones, (2) choppers, and (3) tarring pebbles. Archaeological evidence that
supports this proposal comes from House 3 in the Pitas Point site (VEN-27). Specifically,
a cluster consisting of four heavy hammerstones, four cobble choppers, and four tarring
pebbles was found in House 3 (Gamble 1983). As will be discussed later ethnography
suggests that choppers and hammerstones were used together in household areas in the
manufacture of groundstone tools, such as mortars and pestles. Ethnography also
suggests that tarring pebbles were used exclusively for sealing a specialized type of
watertight basket known as a “water bottle”. Additional ethnographic evidence strongly
links most types of basket making to household areas.

Outdoor Activity Area Locations
I propose that the co-occurrence of hammerstones (especially flaking hammers),
scrapers (carinate, domed, flake, etc.), and flakes as part of a non random surface artifact
cluster that has a low relative frequency of whole or fragmentary mortars, is an objective
and sufficient criterion for identifying the location of an area of outdoor activities in a
Middle to Historic period coastal village site in southern California. It is expected that
discrete and more homogeneous surface artifact clusters are present within such a cluster,
and identify specific activity areas. For example, Area 1 in the Pitas Point site (VEN-27)
has been interpreted as an outdoor activity area adjacent to a house (Gamble 1983).
Within this area flake clusters that co-occur with concentrations of bone are seen as
probable butchering areas (Gamble 1983). In addition, a large number of flake and
domed scrapers, as compared to Areas 2, 3, and 5 in VEN-27, were recovered in Area 1.
The high frequency of domed scrapers in Area 1 may indicate the manufacture of wood
plank canoes (Gamble 1983).
Locations of House and Outdoor Activity Areas in the Sample Area of SNI-25
Methods of Analysis
Nearest Neighbor Analysis
The statistically significant results using the previously described nearest
neighbor analysis for the mapped surface artifacts in the SNI-25 sampling area are given
in Table 5 and visually depicted in Figure 11. As is apparent from Table 5 the significant
values of the Clark and Evans nearest neighbor statistic for the sampling area in SNI-25
are all less than one, which indicates clustering in these groups of surface artifacts. This
agrees with

the intuitive expectation that surface artifacts should have a close spatial association in
activity areas within an archaeological site. Also, as can be seen in Table 5, the Clark and
Evans nearest neighbor statistic is smaller in every case for mapped surface artifacts
within what are interpreted as house activity areas than for what are interpreted as
outdoor activity areas in the sampling area in SNI-25. This means that within the SNI-25
sampling area the surface artifacts within what are interpreted, as house activity areas are
more tightly clustered than the surface artifacts within what are interpreted as outdoor
activity areas. This result makes sense when one considers that many of the outdoor
activities that probably occurred at SNI-25 including the repair and maintenance of open
ocean watercraft such as wood plank canoes or the construction of near shore fishing
platforms such as driftwood rafts required more room than typical household activities
such as cooking.

Table 5. Results of the nearest neighbor analysis of the surface artifact sample at CA-SNI-
25.
Easting
(interval)
Northing
(interval)
Clark &
Evans
Nearest
Neighbor
Statistic
355-360 910-915 0.48 0.003 7 1000 House
355-360 915-920 0.11 0 10 1000 House
360-365 915-920 0.52 0.002 12 1000 House
365-375 900-910 0.52 0.002 12 1000 House
365-370 910-915 0.33 0.0001 6 1000 House
370-375 910-915 0.46 0.002 4 1000 House
360-365 875-880 0.29 0.001 3 1000 House
360-365 880-885 0.33 0.007 6 1000 House
365-370 880-885 0.84
365-367.5 877.5-880 0.78
360-375 885-900 0.56 0.001 14 1000 Outdoor
Interpretation of Results
SNI-25 Activity Areas
P-value
No. of
Surface
Artifact
Locations
No. of
iterations
used to
compute the
approximate
sampling
distribution
Interpreted
Type of
Activity
Area
0.055
14 1500 Outdoor
0.067
7 1500 Outdoor
Figures 12, 13, 14, and 15 provide a visual depiction of statistically significant
clusters of mapped and typed surface artifacts in what are interpreted as house activity
areas within the sampling area of SNI-25. Refer to Appendix E for representative SNI-25
artifacts. Two identifiable types of activity loci within what may be the areal extent of
individual houses are apparent in these figures. As will be discussed in detail later, the
results of the network analyses of artifact types from VEN-27 and SNI-25 support the
proposition that mortars and pestles are tools used in the preparation of food in house

Figure 11. SNI-25 sampling area with plotted positions of surface artifacts
designated as belonging to one of the six interpreted activity areas.

activity areas and not outdoor activity areas. Ethnohistoric and ethnographic data as will
be given later also supports this conclusion. The first identifiable type of activity locus
consists of a cluster of surface artifacts that includes mortar fragments. This suggests that
food processing occurred in this type of activity locus. Also, in the case of House
Activity Area #1 the association of fire-affected rock (FAR) with the mortar fragments in
one locus suggests the presence of a hearth, which in combination with the mortar
fragments implies both the preparation and cooking of food (Figure 12).
Figure 12. House Activity Area #1.
The second type of identifiable activity locus that is present in what are
interpreted as house activity areas in SNI-25 consists of a cluster of surface artifacts that
are mostly metavolcanic and/or quartzite flakes. Note that in my analysis the separate
morphology-based artifact types of debitage and flake in the official San Nicolas Island

lithics typology are merged into a single “functional” type of artifact, which I call
“flake”. This is because the sharpness and shape of the edges of a “flake” relate directly
to its use as a scraping, sawing, cutting, boring or perforating implement and not the
presence or absence of a percussion bulb, which is the main criterion used to differentiate
flakes and debitage in the San Nicolas Island lithics typology. It is probable that some of
the longer quartzite and metavolcanic flakes in my sample are unifacially retouched and
therefore could be typed as flake scrapers. In a later section the information obtained
from the results of replicative experimentation and microwear analysis are used to
connect flake length with use in SNI-25. Based on these results it is inferred that meat
cutting/butchery and/or wood, small bone, and shell working were the principle activities
that took place in the second identifiable type of activity locus within house activity areas
in SNI-25.
Also, House Activity Area #4 (Figure 13) is more than fifteen meters south of the
other three interpreted house activity areas within the sampling area. Based on my
observations of surface artifacts outside my sampling universe, it is suggested that House
Activity Area #4 is part of a separate and more interior cluster of houses. Also, House
Activity Area #4 is situated immediately to the west of what has been interpreted as
Outdoor Activity Area #2.

Figure 13. House Activity Area #4 and two adjacent small outdoor activity areas.
Outdoor Activity Area #2 (Figure 14) has a much denser as well as noticeably different
and more heterogeneous composition of surface artifacts as compared to the other
interpreted outdoor activity area in the sampling area, Outdoor Activity Area #1 (Figure
15). For example, several of the surface artifacts in Outdoor Activity Area #2 include one
of a kind types in the sample, such as half of a donut-shaped steatite artifact with
asphaltum repair (Figure 18), a small chunk of sandstone with pitting on one surface, and
a sandstone pestle fragment that appears to have been used as an abrader (Figure 19).

Figure 14. Outdoor Activity Area #2 and two small outdoor activity areas.
Also, clusters of broken donut-shaped stones larger than the preceding SNI-25 artifact
have been observed only in interior sites of San Clemente Island, in areas where
Dichelostemma capitatum (blue dicks) are common (J. Cassidy 2004, personal
communication).

Figure 15. Outdoor Activity Area #1.
Because of the small size of the SNI-25 donut stone fragment it is not likely this artifact
in its complete form was used as a digging stick weight. It is possible that this artifact
was part of a sun stick or some other ritual object (C. King 2004, personal
communication). These observed differences in artifact composition of House Activity
Area #4 suggest this area may not be contemporaneous with House Activity Areas #1, 2
(Figure 16), and 3 (Figure 17). In the event House Activity Area #4 is contemporaneous
with the other three house activity areas maybe House Activity Area #4 is the remnant of
the household of an SNI-25 inhabitant of high social rank such as a chief.

Figure 16. House Activity Area #2.
Figure 17. House Activity Area #3 and adjacent small outdoor activity area.

The relatively higher density and diversity of the surface artifact types in Outdoor
Activity Area #2 compared to the other surface artifact clusters in the sampling area
points to this possibility. Considering social organization at SNI-25, if House Activity
Area #4 is contemporaneous with House Activity Areas #1,2, and 3 and is part of a
second and well-demarcated house cluster from that of the first three house activity areas,
this suggests the possibility that each house cluster belongs to a separate kinship group.
Dual organization or more specifically a moiety system such as existed in a number of
southern California Uto-Aztecan speaking groups (e.g. Serrano) is inferred in this case. In
the case that House Activity Area #4 is not contemporaneous with House Activity Areas
#1,2, and 3, it remains unequivocal that House Activity Areas #1,2, and 3 form a tight
cluster at the northern edge of the site. The close proximity of these three house activity
areas to one another suggests not only that they co-occur in time but also are part of a
single kinship group, possibly an extended family.
Figures 11 and 12 provide an illustration of what have been interpreted as outdoor
activity areas in the sampling area of SNI-25. Outdoor Activity Area #1 is interior to all
other interpreted activity areas in the sampling area of SNI-25. Because Outdoor Activity
Area #1 is enclosed by a rather large sampling quadrat (15 x 15 meters) compared to the
other sampling quadrats, it is less certain if all of the inferred activity loci within this
activity area overlap in time. Some of the surface artifacts in Outdoor Activity #1 are
much closer to House Activity Areas #1, 2, and 3 than they are to either House Activity
Area #4 or Outdoor Activity Area #2. The converse is also true. Specifically, clusters of
surface flakes are more numerous and on average are larger in total number in two
aggregates of surface artifacts close to House Activity Area #4 and Outdoor Activity

Area #2. It is also possible that House Activity Area #4, if contemporaneous with House
Activity Areas #1, 2, and 3, was the residence of craft specialists who made and repaired
fine utilitarian objects that included both sandstone mortars and pestles. This possibility
is manifested by the presence of the apparent sandstone abrader, whose utilized edge is
concave and of the right curvature to suggest use in the final shaping and smoothing of
mortar rims and/or pestles. Also, a chopper/hammer is a close neighbor to the sandstone
abrader, and as will be discussed later, choppers and hammer stones appear to have been
the primary pecking tools used in the manufacture of groundstone utilitarian objects at
the multi-regional level.
Tool Kits and Activity Areas in the Pitas Point Site (VEN-27)
In this section the counts of twenty-one artifact types from four of five areas
excavated by Chester King and others in the Pitas Point site, VEN-27 are re-examined in
the attempt to elucidate specific tool associations or tool kits. These areas are given as
Areas 1, 2, 3, and 5 in Gamble (1983). Complete provenience data of the excavated
VEN-27 artifacts exists (C. King 2004, personal communication) but was not available to
me at the time I did the analysis of the artifact data from this site. Therefore as analyzed
in this thesis the VEN-27 artifact data are taken from a three-dimensional archaeological
space but lack point provenience at the individual artifact level, as is the case with the
SNI-25 data.

These data from VEN-27 along with providing a much larger sample than the surface
sample taken at SNI-25 contain types of artifacts all of which are present at SNI-25. Also,
the period of occupation of VEN-27 has a considerable overlap with SNI-25, and both
sites were heavily dependent on a very similar assemblage of marine resources. It is
therefore reasonable to assume that the organizational structure of the artifact assemblage
at VEN-27 might be quite similar to that of SNI-25. Tool kits not present in the SNI-25
sample but present in the site might therefore be predicted in the analysis of the VEN-27
artifact counts. Analysis of the VEN-27 artifact data also provides for a comparison of
two roughly contemporaneous maritime-based hunter-gatherer substantial habitation sites
widely separated by open ocean. It is known that the people who occupied VEN-27 were
Chumash but the actual cultural affiliation of the people of San Nicolas Island is not
known.
Martz (2002:3) makes the following statement in support of the expected
similarities in many of the activities that took place in the lives of the San Nicolas
Islanders and coastal Chumash and Gabrieleno.
The lifestyle (of the people of San Nicolas Island) appears to have been
quite similar to that of the marine oriented Chumash and Gabrieleno who
occupied the Channel Islands and adjacent coastline of Southern
California at the time of European contact.
As has already been mentioned Pitas Point is a substantial Chumash habitation
site. The site is located approximately eight miles northwest of Ventura, California and is
adjacent to both Highway 101 and the ocean. The site area encompasses both sandy
beach and the top of a low-lying colluvial terrace immediately behind the beachfront.
Area 1 is located on the beach and contains dark, organically enriched sand. (Gamble
1983) interpreted this location as an area of outdoor activity. Areas 2, 3, and 5 are located

on the terrace. Area 2 is farthest from the beach. (Gamble 1983) interpreted Areas 3 and 5
as containing portions of houses. During the time the site was occupied Area 1 was
directly below Area 3. Gamble (1983) gives the counts of 21 artifact types from each of
the five excavated areas in Table 1 of her paper. She then analyzes these data to test the
hypothesis (using chi-square tests) that within the site there are statistically significant
differences in the types of artifacts occurring within houses as compared to those
occurring in areas of outdoor activity. The conclusion of her analysis is to accept the
hypothesis.
Methods of Analysis
Correlation Analysis
The frequency data in Table 6 were used to compute a Pearson correlation matrix,
Table 7. This method of analysis has already been discussed in a previous section.
To connect the correlation coefficient with tool kits it is necessary to make the
following assumption: Assume that pairs of artifact types that consistently move together
in terms of abundance between locations in a site are functionally associated. In other
words when two artifact types belong to at least one identical and consistent grouping or
aggregate of artifact types (tool kit) they can be directly linked to a specific range of
organized human activities that repeatedly occurred within a site over time. In the next
step in the analysis the correlation matrix will be transformed into an adjacency matrix.
As was mentioned earlier the adjacency matrix will form the basis for discovering tool
kits using the UCINET 6 network analysis software (Borgatti, Everett, and Freeman
2002) to identify cliques.

Merrill m ma_thesis_2004

Recommended

Recommended

More Related Content

Similar to Merrill m ma_thesis_2004

Similar to Merrill m ma_thesis_2004 (20)

Recently uploaded

Recently uploaded (20)

Merrill m ma_thesis_2004