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Demonstrating GIS spatial analysis techniques in a prehistoric mortuary analysis: a case study in the Napa Valley, California

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Demonstrating GIS spatial analysis techniques in a prehistoric mortuary analysis: a case study in the Napa Valley, California

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Content DEMONSTRATING GIS SPATIAL ANALYSIS TECHNIQUES IN A PREHISTORIC MORTUARY ANALYSIS:
A CASE STUDY IN THE NAPA VALLEY, CALIFORNIA
by
Lucian Norman Schrader III
A Thesis Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMATION SCIENCE AND TECHNOLOGY)
M a y 2013
Copyright 2013 Lucian Norman Schrader III

Acknowledgements
I would like to thank my advisor Dr. Karen Kemp for her guidance, expert advice, and
especially her patience. I would also like to thank the members of my thesis committee:
Professors Lynn Swartz Dodd and Thomas Garrison. Thanks also go to my academic advisor,
Katherine Kelsey, for helping me through the process of completing this thesis.
I would also like to thank my employer, Pacific Legacy Inc. for allowing me to conduct
this analysis. I would specifically thank John Holson for running the project and allowing me
access to the data. I also wish to thank Lori Hager for her expert advice when it came to the
actual physical mortuary analysis. Special thanks also go to Dr. Tsim Schnieder for his advice and
insight during this process.
I am especially indebted to my loving wife, Diana Winningham, for her patience and
understanding during this time. I would also thank my beautiful new daughter, Sonya Celeste
Schrader, for giving me further inspiration.

ii

Table of Contents
Acknowledgements........................................................................................................................ ii
List of Figures ................................................................................................................................. v
List of Tables ................................................................................................................................ viii
Abstract...........................................................................................................................................x
Disclaimer ..................................................................................................................................... xii
Chapter 1 - Introduction ................................................................................................................ 1
1.1 Motivation ............................................................................................................................ 2
1.2 Organization of the Thesis .................................................................................................... 3
Chapter 2 - Project Background ..................................................................................................... 6
2.1 Project Location ................................................................................................................... 6
2.2 Environmental Setting .......................................................................................................... 9
2.3 Cultural Chronologies of the Bay Area ................................................................................. 9
2.4 Wappo Ethnography .......................................................................................................... 15
2.5 Project History .................................................................................................................... 19
2.6 Native American Laws and Regulations.............................................................................. 27
Chapter 3 - Literature Review ...................................................................................................... 29
3.1 Mortuary Studies in Archaeology ....................................................................................... 29
3.2 Using GIS in Burial Analysis ................................................................................................ 39
3.2.1 World Studies .............................................................................................................. 39
3.2.2 Regional Studies .......................................................................................................... 42
3.3 GIS and Spatial Analysis for Burial Studies ......................................................................... 45
3.3.1 Spatial Autocorrelation ............................................................................................... 45
3.2.2 Cluster Analysis ........................................................................................................... 46
3.2.3 Grouping Analysis ....................................................................................................... 47
3.2.4 Interpolation using Cokriging Analysis ........................................................................ 48
Chapter 4 - Data and Data Management Methods ...................................................................... 50
4.1 Digitizing Field Data for use in GIS ...................................................................................... 50
4.2 Burial Attribute and Artifact Data ...................................................................................... 53
4.3 Exploring the Data .............................................................................................................. 60
Chapter 5 – Spatial Autocorrelation Analysis ............................................................................... 89
5.1 Spatial Autocorrelation Analysis Results ............................................................................ 89
5.2 Spatial Autocorrelation Analysis Discussion ....................................................................... 93
Chapter 6 – Cluster Analysis ....................................................................................................... 101
6.1 Cluster Analysis Results .................................................................................................... 101
6.2 Cluster Analysis Discussion ............................................................................................... 102
Chapter 7 - Grouping Analysis .................................................................................................... 119
7.1 Grouping Analysis Results ................................................................................................ 121
iii

7.2 Grouping Analysis Discussion ........................................................................................... 138
Chapter 8 – Radiocarbon Date Interpolation ............................................................................. 140
8.1 Burial Radiocarbon Date Interpolation ............................................................................. 140
8.2 Comparison by Date Range from Radiocarbon Date Interpolation .................................. 145
8.3 Changes in Attributes over Time ...................................................................................... 153
Chapter 9 – Conclusions ............................................................................................................. 163
9.1 Considerations for Future Burial Analysis......................................................................... 163
9.2 Site Summary ................................................................................................................... 167
Glossary ..................................................................................................................................... 171
References ................................................................................................................................. 174
Appendix A: Burial Shapefile Attributes Table .......................................................................... 181
Appendix B: Cokriging Prediction of Error Analysis Table for Interpolated Dates ..................... 186

iv

List of Figures
Figure 1: Study Area. ..................................................................................................................... 6
Figure 2: Project Overview. ........................................................................................................... 8
Figure 3: Tribal Territory. ............................................................................................................ 17
Figure 4: CA-NAP-399 Overview. ................................................................................................. 24
Figure 5: Labeled Burials ............................................................................................................. 25
Figure 6: Burial Flexure Examples and Codes .............................................................................. 55
Figure 7: AMS Radiocarbon Dates from Burials (Holson et al. 2013). ......................................... 63
Figure 8: Radiocarbon Dated Burials with Dates. ........................................................................ 64
Figure 9: Map of Age Attribute Distribution by Burial. ................................................................ 66
Figure 10: Map of Sex Attribute Distribution by Burial. .............................................................. 67
Figure 11: Map of Flexure/Position Attribute Distribution by Burial. .......................................... 68
Figure 12: Map of Orientation Attribute Distribution by Burial. ................................................. 69
Figure 13: Map of Side Attribute Distribution by Burial. ............................................................. 70
Figure 14: Map of Bone Preservation Attribute Distribution by Burial. ...................................... 71
Figure 15: Stratigraphic Profile of Burials from West to East. ..................................................... 72
Figure 16: Map of Artifact Association Attribute Distribution by Burial. ..................................... 73
Figure 17: Map of Total Wealth Item Distribution by Burial. ...................................................... 75
Figure 18: Map of Total Tool Distribution by Burial. ................................................................... 77
Figure 19: Map of Total Artifact Distribution by Burial. .............................................................. 78
Figure 20: Map of Total Artifact without Debitage and Faunal Distribution by Burial. ............... 79
Figure 21: Map of Tool Diversity Index Distribution by Burial. .................................................... 80
Figure 22: Map of Wealth Diversity Index Distribution by Burial. ............................................... 81
v

Figure 23: Map of Burials with Anemia. ...................................................................................... 82
Figure 24: Map of Burials with Auditory Exostoses. .................................................................... 83
Figure 25: Map of Burials with Dental Caries. ............................................................................. 84
Figure 26: Map of Burials with Healed Fractures. ....................................................................... 85
Figure 27: Map of Burials with Osteomyelitis. ............................................................................ 86
Figure 28: Map of Burials Demonstrating Femurs with Anterior to Posterior Flattening. ........... 87
Figure 29: Map of Inca Bone Distribution. .................................................................................. 88
Figure 30: Map of Cluster Analysis of the Burials Based on Depth in Meters............................ 104
Figure 31: Map of Cluster Analysis of the Burials Based on Total Wealth Items. ...................... 106
Figure 32: Map of Cluster Analysis of the Burials Based on Total Tools. ................................... 107
Figure 33: Map of Cluster Analysis of the Burials Based on Total Artifacts. .............................. 108
Figure 34: Map of Cluster Analysis of the Burials Based on Total Artifacts Minus Debitage and
Faunal. ....................................................................................................................................... 109
Figure 35: Map of Cluster Analysis of the Burials Based on Tool Diversity Index. ..................... 110
Figure 36: Map of Cluster Analysis of the Burials Based on Wealth Diversity Index. ................ 111
Figure 37: Map of Cluster Analysis of the Burials Based on Indirectly Associated Bifaces. ....... 112
Figure 38: Map of Cluster Analysis of the Burials Based on Indirectly Associated Edge-Modified
Flakes. ........................................................................................................................................ 113
Figure 39: Map of Cluster Analysis of the Burials Based on Indirectly Associated Unifaces. ..... 114
Figure 40: Map of Cluster Analysis of the Burials Based on Indirectly Associated Pestles. ....... 115
Figure 41: Map of Cluster Analysis of the Burials Based on Directly Associated Shell Beads. ... 116
Figure 42: Map of Cluster Analysis of the Burials Based on Indirectly Associated Natural Obsidian
Needles. ..................................................................................................................................... 117
vi

Figure 43: Map of Cluster Analysis of the Burials Based on Directly Associated Pendants. ...... 118
Figure 44: Map of Orientation Burial Group. ............................................................................ 122
Figure 45: Map of Preservation Burial Groups. ......................................................................... 124
Figure 46: Depth Grouping Analysis. ......................................................................................... 126
Figure 47: Depth Grouping and Radiocarbon Dates from Stratigraphic Profile from West (at left)
to East (at right). ........................................................................................................................ 127
Figure 48: Section 1 of Depth Grouping Analysis. ..................................................................... 128
Figure 49: Section 2 of Depth Grouping Analysis. ..................................................................... 129
Figure 50: Section 3 of Depth Grouping Analysis. ..................................................................... 130
Figure 51: Section 4 of Depth Grouping Analysis. ..................................................................... 131
Figure 52: Section 5 of Depth Grouping Analysis. ..................................................................... 132
Figure 53: Section 6 of Depth Grouping Analysis. ..................................................................... 133
Figure 54: Map of Artifact Association Burial Groups. .............................................................. 135
Figure 55: Map of Wealth Diversity Index Burial Group. ........................................................... 138
Figure 56: Correlation of Radiocarbon Dates and Depth........................................................... 141
Figure 57: Interpolated Dates from Radiocarbon Dates Only. .................................................. 143
Figure 58: Prediction Standard Error Map for Interpolated Burial Date Surface. ...................... 144

vii

List of Tables
Table 1: Cultural Chronologies of the Area. ................................................................................ 11
Table 2: Shapefiles and Data Layers. ........................................................................................... 53
Table 3: Age Codes. ..................................................................................................................... 54
Table 4: Sex Codes. ..................................................................................................................... 54
Table 5: Side Codes. .................................................................................................................... 57
Table 6: Bone Preservation Codes. .............................................................................................. 57
Table 7: Radiocarbon Dated Burials (Holson et al. 2013). ........................................................... 62
Table 8: Radiocarbon Date Range Comparisons.......................................................................... 65
Table 9: Number of Burials by Range of Wealth Items per Burial. .............................................. 74
Table 10: Summary of Wealth Items from Burials at CA-NAP-399. ............................................. 74
Table 11: Number of Burials by Range of Total Tools. ................................................................. 76
Table 12: Summary of Tools from Burials at CA-NAP-399. .......................................................... 76
Table 13: Number of Burials by Range of Total Artifacts. ............................................................ 78
Table 14: Number of Burials by Range of Total Artifacts Minus Debitage and Faunal Remains. .. 79
Table 15: Burial Attributes Spatial Autocorrelation Summary. ................................................... 90
Table 16: Tool Spatial Autocorrelation Summary. ....................................................................... 91
Table 17: Wealth Items Spatial Autocorrelation Summary. ........................................................ 92
Table 18: Pathologies and Anomalies Spatial Autocorrelation Summary. ................................... 93
Table 19: Individual Burial Cluster Analysis Summary. .............................................................. 102
Table 20: Cluster Analysis of Artifacts Summary. ...................................................................... 102
Table 21: Burial Attributes Grouping Analysis Summary. .......................................................... 120
Table 22: Results of Grouping Analysis Examining Depth in Centimeters. ................................ 125
viii

Table 23: Results of Grouping Analysis Examining Artifact Association. ................................... 134
Table 24: Results of Grouping Analysis Examining Wealth Diversity Index. .............................. 137
Table 25: Burials by Interpolated Date Range. .......................................................................... 145
Table 26: Comparison of Selected Attributes by Interpolated Date Range. ............................... 148
Table 27: Comparison of Pathologies and Anomalies by Interpolated Date Range. ................. 149
Table 28: Comparison of Directly Associated Tools by Interpolated Date Range. ..................... 150
Table 29: Comparison of Total Tools by Interpolated Date Range. ........................................... 151
Table 30: Comparison of Directly Associated Wealth Items by Interpolated Date Range. ........ 152
Table 31: Comparison of Total Wealth Items by Interpolated Date Range. .............................. 153
Table A-1: Burial Shapefile Attributes. ...................................................................................... 181
Table B-1: Cokriging Prediction of Error Analysis Table for Interpolated Dates. ....................... 190

ix

Abstract
This thesis uses a geographic information system (GIS) to demonstrate spatial analysis
techniques in order to examine changes to a prehistoric society of Native American Wappo
dating from 2450 to 1950 years before present (BP) from the Upper Archaic Period in the Napa
Valley of California. This cemetery was excavated by Pacific Legacy Inc., a private cultural
resources management firm, in compliance with the National Historic Preservation Act (NHPA)
and the California Environmental Quality Act (CEQA) for a flood control project. While Pacific
Legacy Inc. analyzed the burials on an individual basis, they did not conduct a spatial analysis.
They incorporated their data into a simple spreadsheet to look for patterns. This thesis serves
as a complimentary spatial examination of the burials based on spatial data.
The dataset is incomplete as it was not collected using a consistent, systematic
methodology. Additional burials related to the dataset had also been removed from the site
before excavation by erosion and other archaeological excavations. This paper demonstrates
select spatial analysis techniques using this dataset as an example.
This thesis examines the distribution of the burials within the cemetery to identify
spatial patterns based on burial attributes and artifact distribution. Spatial autocorrelation,
cluster analysis, and grouping analysis focus on identifying burial clusters and individual burial
outliers.
A form of interpolation known as kriging was used to estimate the dates for the burials
that were not subjected to Accelerator Mass Spectrometry (AMS) Radiocarbon dating. The
burials were then grouped into corresponding date ranges covering one hundred year time
spans. This experimental study allows for identification of changes to society by analyzing the
change in burial attributes and artifact types over the course of the Upper Archaic Period.
x

Due to the incomplete nature of the dataset, only two conclusions could be reached
with the remaining findings considered suggestive. There is clustering based on bone
preservation and the spatial analysis results tend to vary depending on different excavation
techniques. Possible clustering of depth, wealth diversity index, directly associated shell beads,
and directly associated pendants may reflect certain aspects of ancient society. The possible
clustering of artifact association, total tools, tool diversity index, indirectly associated bifaces,
indirectly associated edge-modified flakes, indirectly associated unifaces, and indirectly
associated pestles can likely be explained due to differing excavation techniques. Possible
clustering of natural obsidian needles may be explained as naturally occurring in the soil. Dental
caries were found to be possibly dispersed, which is likely just a random occurrence. The
experimental radiocarbon date interpolation allowed for an examination of changes to CA-NAP-
399 over a five hundred year period. Thus results from the analyses in this report should not be
seen as definitive nor should they be used as foundations for further archaeological analysis.
The main purpose here is to demonstrate how spatial analysis may be used with data of this
type.

xi

Disclaimer
Archaeological and other heritage resources can be damaged or destroyed through
uncontrolled public disclosure of information regarding their location. This document contains
sensitive information regarding the nature and location of archaeological sites which should not
be disclosed to unauthorized persons. The exact locations of these cultural resources are kept
vague in an attempt to avoid future relocation by those individuals who do not need to know
this information.
Information regarding the location, character, or ownership of a historic resource is
exempt from the Freedom of Information Act pursuant to 16 U.S.C 470w-3 (National Historic
Preservation Act) and 16 U.S.C. § 470hh (Archaeological Resources Protection Act) and California
State Government Code, Section 5254.10.
xii

Chapter 1 - Introduction
In 1986 and 1995 the Napa River flooded, causing millions of dollars worth of damage.
In order to alleviate future flooding events, a massive new floodwall and holding basin was
proposed along with altering the channel of the Napa River. In accordance with the NHPA and
CEQA, Pacific Legacy Inc. was contracted to research, study, survey, excavate, and monitor
cultural resources related to the construction of the flood control project. Several cultural
resources were located within the project’s area of potential effects (APE). One cultural
resource in particular, CA-NAP-399, proved to be an exceptionally rich and complex site that
required extensive investigation and monitoring. During the course of excavation and
monitoring for the flood control project, 162 prehistoric Native American Wappo burials were
recovered.
The purpose of this research is to demonstrate how spatial analysis might be used with
this kind of archaeological data. This large collection of individuals allows a unique examination
of a prehistoric society in one location over a period of 500 years. The findings of this research
will hopefully demonstrate to other archaeologists the potential usefulness of spatial analysis
techniques available in GIS. It may also help expand our understanding of prehistory in the area.
Spatial autocorrelation, cluster analysis, and grouping analysis allow for the analysis of
clusters or groups of burials based on certain attributes (such as age, sex, orientation, etc.),
artifact types (projectile points, bifaces, shell beads, etc.), and certain pathologies and health
anomalies (anemia, osteomyelitis, dental caries, etc.). Burials reflect a deceased individual’s
place in society (Binford 1971). The experimental use of GIS to gain a rough date of internment
for each individual allows for the comparison of different burial attributes and artifacts types
over time to see how they changed. This change is reflected in prehistoric society, and will
1
greatly enhance our understanding of prehistory in the area. It will also allow for greater inter-
site comparisons in the region.
1.1 Motivation
Several archaeologists have conducted spatial analysis of prehistoric burial populations
that spanned thousands of years, such as Bellifemine (1997), Byrd and Monahan (1995), Hudson
(1977), Huggett (1997), and Savage (1997) among others. They often looked for patterns or
clustering based on age, sex, and status. These studies will be addressed further in Chapter 3.
While this is acceptable if examining the entire burial population, it often ignores analysis of
burials by time periods or components. This thesis attempts a much finer examination of the
burials examining changes over centuries, not millennia.
By identifying the date the individuals were interred on the site, we can gain insight into
how site use and prehistoric society changed over time. We can also gain insight into the
increasing complexity of prehistoric society. Changes in artifact types over time can give insight
into procurement strategies and activities occurring onsite. This will contribute to our
understanding of the prehistory of the region.
The actual dating of Native American remains is incredibly rare. This is a rare instance
where the Wappo tribe granted permission to study and better understand their past. This is an
opportunity that few archaeologists have ever had. It would be folly to let an opportunity like
this pass, especially if some greater understanding could be reached.
As discussed later in Chapter 3, using GIS to try to determine the dates of the burials is
something that has not been examined in depth. Often GIS is used to produce maps of burials
located on sites. Using GIS to conduct spatial analysis is becoming increasingly popular;
2
however using GIS to conduct prehistoric mortuary analysis seems to have lagged behind other
advancements in archaeology. Most mortuary studies still tend to focus on cluster analysis,
looking for burials patterns based on age, sex, status, and/or grave goods. There are few
examples of studies that use GIS to conduct an intrasite mortuary analysis. This may be from
the lack of sites providing a large enough sample of burials to conduct a statistical analysis.
It is hoped this study will lead to a broader examination of burials within California using
GIS. This examination and paper is not meant to create an application for burial analysis, but
rather create a foundation upon which additional knowledge and insight regarding burial
analysis may be built. It should show archaeologists that GIS is a capable tool to compliment
burial and mortuary analyses. It is important for archaeologists to recognize incomplete
datasets, and to use complete datasets whenever possible. This can be difficult in archaeology
given that entire sites are no longer totally excavated. Sites are typically sampled, leaving
portions of the site untouched for future archaeologists to excavate and compare with past
excavation using newer and more advanced excavation techniques. This can present problems
regarding datasets that only sampling can solve.
1.2 Organization of the Thesis
The project and scope of the thesis are presented in the introduction. This report is
organized into nine chapters.
Chapter 2 gives information regarding the project background. There are six
subsections. The first two give a brief introduction and overview of the study area location and
environmental setting. Various cultural chronologies from the San Francisco Bay Area are then
presented to give a sense of where the site lies within regional prehistory. A very brief
3
accounting of Wappo ethnography is then given. This is followed by an account of the project
history. This chapter is then finished by a brief account of three Native American cultural
resource laws and regulations that concern the project.
Chapter 3 provides relevant information concerning archaeology and GIS. There are
three subsections. The first deals with mortuary studies in archaeology. The second details
several examples of GIS mortuary analysis. The third section gives background on several GIS
analysis techniques relevant to this study.
Chapter 4 details information regarding the data and methods used to analyze them.
This chapter is divided into three subsections. The first details the collection and digitization of
survey data into GIS shapefiles and details its limitations. This is followed by a section that
details the burial attribute data. The final section gives a brief overview of the data as a whole,
examining the spatial distribution of attributes, artifacts, and pathologies and anomalies.
Chapter 5 details the spatial autocorrelation study based on burial attributes, artifacts,
and pathologies and anomalies. The results and implications on prehistoric society are then
discussed.
Chapter 6 details the cluster analysis study based on burial attributes and select
artifacts. The results and implications on prehistoric society are then discussed.
Chapter 7 details the grouping analysis based on burial attributes. There are two
sections within this chapter. The first details the results and implications on prehistoric society
followed by the second section where the technique itself is then discussed.
Chapter 8 deals with an experimental study into the interpolation of radiocarbon dates
to date the remaining burials. This chapter is divided into three subsections. The first deals with
interpolation using cokriging on 21 radiocarbon dates coupled with depth data. The second
4
then compares the burials across five arbitrary one hundred year date ranges to see changes
over time for attributes, artifacts, and pathologies and anomalies. The third summarizes the
changes seen over time and their possible implications on prehistoric society.
Chapter 9 details the conclusion of this thesis and is comprised of two sections. The first
offers suggestions for future analytical work dealing with GIS and mortuary analysis. The second
summarizes the findings from CA-NAP-399.
Terms used for archaeology and GIS are found in the glossary. This is followed by a
reference section and the appendices. The first appendix presents a table of the burial shapefile
attributes. Appendix B discusses several methods of cokriging. It also contains a table that
details the prediction errors and accuracy of several possible cokriging methods for the date
interpolation model from Chapter 8.
5
Chapter 2 - Project Background
This chapter presents a general location of the study site, an environmental description
of the area, the history of the project, relevant background information concerning the
prehistory of the area, and a brief ethnography of the Native American Wappo.
2.1 Project Location
As shown in Figure 1, the project area is located in the Napa Valley in Northern
California, some thirty miles north of San Francisco Bay Area and thirty miles east of the Pacific
Ocean. The San Francisco Bay Area is a densely populated region surrounding the San Francisco
and San Pablo estuaries.

Figure 1: Study Area.
6
The study site, CA-NAP-399, was originally recorded by Beard in 1976 when it was
noticed that human remains were eroding out of the side of the Napa river channel (Beard
1976). The original site record does not mention the size of the site. Shortly after that, the site
was leveled for a mobile home park and the site was assumed to have been destroyed. Large
boulders were brought in to slow erosion into the riverbank which eliminated visibility. In the
1990’s, work to install a storm drain immediately to the west of the cinder block floodwall
(which demarcated the western boundary of CA-NAP-399) uncovered human remains. These
remains were excavated by Origer (1994) who designated a new site number for the site, still
thinking CA-NAP-399 had been destroyed.
The Flood Protection Project focused on a section of the Napa River directly adjacent to
a mobile home park (Figure 2). The Flood Protection Project involved creating large holding
ponds and a floodwall as well as reengineering the drainage of the river to better accommodate
future flood events. The floodwall will protect a larger area from flooding in the future. It will
also allow for future development behind the safety of the floodwall. The Flood Protection
project was contentious, with many residents viewing it as a waste of money. Residents of the
mobile home park were not pleased as several mobile homes were removed and their scenic
walking path along the edge of the river was eliminated.

7
Figure 2: Project Overview. 8
2.2 Environmental Setting
Napa County is located in the North Coast Range, which is part of the California Coast
Ranges. This mountain range is characterized by relatively low mountains. There are several
volcanic intrusions in this mountain range. One of the most important is Napa Glass Mountain,
part of the Sonoma Volcanic formation, located two miles north of the project area. This was
one of the main sources of obsidian for creating stone tools for Native Americans in Northern
California.
The Napa River is the dominant drainage for the county. It flows from near Calistoga,
several miles north of the project area, south approximately 35 miles to where it forms a delta
and enters San Pablo Bay.
There are six distinct vegetation communities in the area consisting of Valley and
Foothill Grassland, Oak Woodland, Northern Mixed Chaparral, Coast Range Mixed Coniferous
Forest, Alluvial Redwood Forest, and Riparian Forest (Holson et al. 2013). These communities
support a wide variety of fish, shellfish, waterfowl, birds, amphibians, and large and small
mammals. These were important foodstuffs to the Native Wappo, as were acorns from Oak
trees and grass seeds.
2.3 Cultural Chronologies of the Bay Area
The cultural and temporal chronology for the Bay Area and the North Coast Ranges has
varied considerably over the years. There are at least three different systems for organizing the
archaeology of the Bay Area into coherent units of observation and comparison (Milliken et al.
2007). Which system an archaeologist uses often depends on their academic background.
Milliken et al. (2007) gives an overview of the history of the Bay Area. Bennyhoff (1977)
9
provides an examination of archaeological excavations in the Napa Valley up until the date of
publishing.
The Early-Middle-Late period nomenclature was created by Beardsley in 1948, and was
dubbed the Central California Taxonomic System by Gerow (1968). This is typically used by
South Bay archaeologists and some central Bay archaeologists (Milliken et al. 2007).
The Archaic Emergent temporal framework was put forth by Fredrickson (1973) based
on earlier work with Bennyhoff (Bennyhoff and Fredrickson 1967). This system relies on specific
cultural configurations identified by economic patterns, stylistic aspects, and temporally
constricted regional phases (Milliken et al. 2007). According to Milliken et al, this system is used
by North Bay archaeologists and some Central Bay archaeologists.
A hybrid system that marks large blocks of time with the Early-Middle-Late Period
structure coupled with Fredrickson’s system is used by some Central Bay archaeologists
including Milliken et al. (2007) who suggest that this has the advantage of allowing the
identification of regional aspects within larger cultural patterns.
Table 1 is taken from the Pacific Legacy Inc. templates for the Bay Area, and shows a
breakdown of the cultural chronologies of the area. In order to properly understand it, a variety
of terms must be defined and are presented in the Glossary. The project area falls into the Napa
Valley Cultural Pattern, seen in Table 1. Only aspects and patterns that occur in the project
study area are described here in detail.
10
Table 1: Cultural Chronologies of the Area.
Temporal
Periods
(Fredrickson
1973, 1974)
Temporal
Periods (Milliken
et al. 2007)
San Francisco
Bay Cultural
Pattern
North Coast Cultural
Pattern
Napa Valley
Cultural Pattern
Upper Emergent
(AD 1500-1800)
Terminal Late
Period (AD 1550-
1800)
Augustine
Pattern
Emeryville
Aspect
Augustine Pattern
Clear Lake Aspect
Augustine Pattern
St. Helena Aspect
Initial Late Period
(AD 1050-1550)
Lower Emergent
or Late Horizon
(AD 900-1500)
Middle/Late
Period Transition
(AD 1000-1050)
Upper Middle
Period (500 BC-
1050 AD)
Upper Archaic or
Middle Horizon
(500 BC–AD
900)
Upper Berkeley
Pattern
Ellis Landing
Aspect
Houx
Aspect
Mendocino
Aspect
Houx
Aspect
Hultman
Aspect
Lower Berkeley
Pattern
Stege Aspect
Middle Archaic
or Early Horizon
(3000-500 BC)
Early Period
(3500-500 BC)
Lower Archaic
(6000-3000 BC)
No Defined
Pattern
Borax Lake Pattern
Borax Lake Aspect
Borax Lake Pattern
Early Holocene
(8000-3500 BC)
No Defined Pattern Paleo-Indian
(10000-6000 BC)
Post Pattern No Defined
Pattern
Paleo-Indian Period
The Paleo-Indian period dates from 12,000 to 8,000 years BP. This period is poorly
understood with only one known site being discovered (Meighan and Haynes, 1968). This is
likely due to geological processes burying the sites. Isolated artifacts dating to this time period
have been discovered and consist of large fluted projectile points called Clovis points, crescent
shaped bifaces, and large shouldered projectile points. Fredrickson (1992) hypothesized that
the period was characterized by lacustrine sites with a probable emphasis on hunting. There is
11
no evidence of milling technology. Trade and exchange was probably on an individual basis. The
primary social unit was likely the extended family. Resources were likely acquired through
mobility rather than trade.
Lower Archaic Period
The Lower Archaic dates from 8,000 to 5,000 years BP. Very few sites dating to this
pattern have been discovered (again, likely due to geological processes). During this period, the
ancient lakes, which had been the subsistence base during the Paleo-Indian Period, began to dry
up as a result of climate change. An increased emphasis on plant foods can be inferred by the
abundant appearance of milling slabs and handstone/manos (Fredrickson, 1973). Projectile
points are typified by concave-base and stemless projectile points. Wide-stemmed points occur
in smaller numbers. Fredrickson (1992) stated that the family unit continued to be the main
primary social unit.
Borax Lake Pattern
The Borax Lake Pattern is difficult to categorize given the low number of sites excavated.
The material culture appears to be identical to the Lower Archaic Period description given
above. It is assumed this pattern occurs in the Napa Valley, only it is buried under more recent
geological deposits.
Middle Archaic Period
The Middle Archaic Period dates from 5,000 to 2,500 years BP. This time period is much
more widely known than the previous two time periods (Milliken et al. 2007). The mortar and
pestle appear during this pattern. Population growth increases. Projectile points are typified by
large leaf shaped dart points, shouldered projectile points, and bipoints. Deer ulna bone awls
12
and flakers are also common. Obsidian quarries create an industry for biface trade to
neighboring areas across the state. The Berkeley Pattern represents the expansion of Miwokian
speakers into the North Bay at approximately 500 BC (Bennyhoff 1968).
Houx Aspect
The Houx Aspect is believed to be indigenous to the Clear Lake area, while the
Mendocino Pattern was intrusive to the region (White and Fredrickson 1992; White 2002).
Mortars and pestles appear replacing the milling slab assemblage. This indicates a dependence
on acorns. Ulna awls and flakers appear, indicating possible basketry. Atlatl dart projectile
points consist of leaf shaped projectile points. Shouldered bifaces and bipoints are also present.
Hultman Aspect
The type site for the Hultman aspect is located one mile upstream from the project
area. This aspect is characterized by milling slabs and mullers with no beads. This southern
aspect of the Mendocino Pattern utilized obsidian for atlatl dart points that were leaf shaped.
This differs from the more northern portions of the pattern which relied more heavily on chert
material and corner-notched projectile points.
Upper Archaic Period
The Upper Archaic Period dates from 2,500 to 1,100 years BP. The expansion of
settlements coupled with population growth continued. Fredrickson (1974:48) suggested that
the Upper Archaic Period “seems to have been marked by ever increasing socio-political
complexity, a growth of status distinctions based on wealth, the emergence of group-oriented
religious activities, and greater complexity of the exchange systems.” Stone tools continue to be
dominated by large leaf shaped projectile points and shouldered projectile points. Deer ulna
13
bone awls, mortars, and pestles continue to be plentiful. There is also an increase in Olivella
beads, abalone ornaments, and incised bone. The large obsidian biface manufacturing industry
collapses throughout California around 1,800 years ago.
Emergent Period
The Emergent Period dates from 1,100 to 200 years ago. Prehistoric cultures
throughout California “reached levels of sociocultural complexity usually considered correlates
of agricultural societies” (Fredrickson 1973:38). The emergence of the bow and arrow
technology some 1,500 years ago meant a shift away from larger dart points to smaller
arrowheads. Early arrowheads called Stockton Serrated had numerous square barbs running up
each margin. These were replaced around 900 years ago by small, triangular, corner-notched
projectile points. Well shaped mortars and pestles are prevalent.
Augustine Pattern
The Augustine Pattern arose through stimulation from Patwin speakers newly arrived in
the lower Sacramento Valley from Oregon (Milliken et al. 2007). It is believed that they brought
with them the bow and arrow, the flanged pipe, preinternment grave-pit burning, and other
new cultural traits (Bennyhoff 1982). It is difficult for linguists to explain how so many varying
tribes in the Bay Area speaking different languages were able to share such a similar material
culture. Milliken et al. (2007) hypothesized that the Augustine Pattern, with its shared religious
and ceremonial organization, was developed as a means of overcoming insularity in an area
where many neighboring language groups were in contact.
14
St. Helena Aspect
The St. Helena Aspect is characterized by small serrated arrow points called Stockton
Serrated projectile points. These are replaced by small corner-notched points with few to no
serrations towards historic times. Well shaped mortars and pestles are present. Bone awls
likely indicate basketry, as does the presence of hopper mortars. Tubular tobacco pipes are
common. There in an increase in the number of ornamental objects and beads created from
shell, stone, and bone.
2.4 Wappo Ethnography
It is important to use ethnographic data as the starting point for any analysis (Larsen
1997). The project area lies in the ethnographic territory of the Native American Wappo.
Wappo is a name likely derived from the Spanish term guapo, which means “brave or good
looking“ (Kroeber 1925:217). This name was most likely given to the Wappo during the Mission
Period (the late 18
th
and early 19
th
centuries) since the group was well known for their strong
resistance to Spanish and Mexican incursions within their territory (Driver 1936; Kroeber 1925).
The Wappo call themselves ona-cáttis, “the people who speak plainly and truthfully, the
outspoken ones” (Sawyer 1978:263)
The ethnographic Wappo are composed of five linguistic subdivisions (the Southern
Wappo, Central Wappo, Northern Wappo, Western (or, Russian River) Wappo, and Clear Lake
Wappo) that are part of the Yukian language family (Kroeber 1925). Wappo linguistic
subdivisions are further subdivided by a mosaic of hunter-gatherer tribelets. A tribelet is the
largest autonomous or self-governing political unit for California hunter-gatherers and consists
of a single permanent village which serves as a sociopolitical center (Kroeber 1955). This
15
sociopolitical center is composed of several coalesced lineages, and was surrounded by a
network of smaller satellite villages (Kroeber 1955).
As recorded by early ethnographers such as Barrett (1908), Driver (1936), and Kroeber
(1925), the territory of the Wappo was unusual in that it was discontinuous and included
portions of several drainages (see Figure 3). The primary area of settlement was the Napa
Valley. Their territory stretched from near present-day Geyserville on the Russian River in the
northwest to the delta of the Napa River at San Pablo Bay in the southeast (Kroeber 1925).
Subsistence was based mainly on plant resources and was supplemented by animal
resources. Acorn was the primary plant resources. It was stored for use throughout the year,
and prepared as either a mush or bread. Several other plant resources supplemented the acorn
staple such as buckeye, various plant roots, and berries. Small game such as rabbits was the
most plentiful animal resource in the area, and was supplemented by larger game such as deer
whenever possible. Fish was an occasional resource as well, but did not reach the level of fishing
industries seen in the Pacific Northwest tribes with their Salmon fishing.
The Wappo settlement system was semi-sedentary with large permanent or semi-
permanent villages that were situated near fresh water sources and in environments with
diverse and abundant resources (Kroeber 1955). In the areas surrounding these villages, task
specific seasonal camps were distributed near specific resources. According to Driver
(1936:183), primary village sites were “occupied continually throughout the year and other sites
were visited in order to procure particular resources that were especially abundant or available
only during certain seasons.”
16

Figure 3: Tribal Territory.
The sweathouse seems to have been the primary factor in the organization of the
village. The sweathouse was located centrally within the village and its entrance always faced
south (Driver 1936). Dwellings were placed around the sweathouse but were not laid out in a
geometric form. Both the sweathouse and dwellings were semi-subterranean. The sweathouse
structure was constructed more substantially out of planks and posts, while the dwellings were
17
constructed of grass thatch and poles (Driver 1936). Summer houses were more temporary,
built on the ground surface with poles, thatch, and open ceilings.
The basic unit of Wappo social structure was the immediate bilateral kin group (Driver
1936). The largest unit of effective organization was the village community. Villages were led
by a chief who fulfilled “four offices or functions: 1) war chief; 2) home chief; 3) dance or
ceremonial chief; and 4) news-man or town crier” (Driver 1936:212). A different person could
fill each of these functions, but often a single chief fulfilled all four. The position was almost
always filled by a man even though there were women who also filled similar roles (Driver
1936). The Wappo had little specialization in terms of occupation and even chiefs were
expected to hunt and fish to feed their families. However, that little specialization included
occupations such as doctors, specialized ceremonial positions, and specialized craft artisans that
may have been learned through apprenticeships (Driver 1936).
The territory occupied by the Wappo was rich in desirable resources, particularly in raw
materials for stone tool manufacture such as obsidian. Obsidian sourced to the Napa Valley has
been found at archaeological sites throughout Central and Northern California. The Wappo
were obviously an important part of a regional trade network, however the exact nature and
operation of this network is not completely known. Trade was conducted through contact with
neighboring tribes or travel through their territory.
Kroeber (1925) stated there are no specific descriptions of Wappo habits regarding
burials, though he suggested they resemble those of the Pomo who practiced cremation of the
deceased. Driver (1936) elaborated, stating that there was no tribal mourning ceremony or
public tribute. He goes on to briefly recount what happened to the body after a death. The
individual was carried on three sticks by six men one to two miles outside of the village, and
18
then cremated with their possessions in a pit dug approximately two feet deep (Driver 1936).
The ethnohistoric account of cremations does not necessarily indicate that the same practice
was used in earlier prehistoric times. Burial practices can shift over time.
2.5 Project History
Pacific Legacy (2013) detailed the history of the project. An abbreviated account is
presented below.
Floods in 1986 and 1995 overtopped existing flood control structures along the Napa
River, resulting in over $50 million in damages. A collaborative effort by the City of St. Helena
with the Napa County Board of Supervisors and the Napa County Flood Control District
performed a joint study of the Napa River to better understand the hydraulics of flood flows.
The study’s conclusions resulted in the enlargement of the 100 year flood plain indicating a
more serious flood hazard than previously established by the Federal Emergency Management
Agency (FEMA).
FEMA awarded a grant to the city of St. Helena to study potential ways to reduce
damage from future flooding. A number of possible projects from this study were evaluated
over the next three years. A final project design and environmental impact report was finalized
in February 2004. Pacific Legacy Inc. was then hired by the City of St. Helena to assist in
managing any cultural resources within the project area. An addendum was adopted in
November 2005 addressing several design changes. In April 2006, a shortage of funds was
identified, necessitating further revisions to reduce costs.
A Historic Properties Treatment Plan (HPTP) was created by Pacific Legacy Inc. outlining
a program of archaeological excavation and analysis that fulfilled the research potential of
19
cultural resources within the Area of Potential Effects (APE). A Memorandum of Agreement
(MOA) between the State Historic Preservation Officer (SHPO), United States Army Corps of
Engineers (USACE), and State Water Resources Control Board (SWRCB) was signed in March of
2007. The City of St. Helena and the Mishewal-Wappo Tribe of Alexander Valley were
concurring parties to the MOA.
The entire APE was subjected to an extended archaeological survey (Bartoy and Holson
2005). As a result of the survey, eight archaeological resources were identified within the APE.
These eight resources were formally evaluated by Bartoy et al. (2005). Three resources
(including CA-NAP-399) were determined eligible for listing on the California Register of
Historical Resources (CRHR) and the National Register of Historic Places (NRHP). A fourth was
determined to not have enough information by the State Historic Preservation Officer (SHPO)
and further excavation was recommended.
The SWRCB and USACE applied the criteria of effect found within the Federal Register at
Title 36 of the Code of Federal Regulation (CFR), Chapter VIII, Part 800 (the protection of historic
properties) Section 5 (a) (1) and determined that the flood protection project would result in
significant adverse effects to the three eligible cultural resources. Requirements for these
environmental laws are found in Section 2.6. The SHPO agreed with the findings of adverse
effects, and recommended continued consultation regarding the fourth site. Under the
implementing regulations for Section 106 at 36 CFR § 800.6, the SWRCB and USACE consulted
on ways to avoid, minimize, or mitigate the potential adverse effects to the cultural resources.
This lead to the signing of the MOA with SHPO.
The SWRCB and USACE elected to conduct Phase III archaeological data recovery
excavations at the resources prior to their disturbance. Phase III data recovery excavation is the
20
controlled excavation of a sample of the site designed to recover a representative sample of
artifacts to allow for detailed analysis. The justification for implementing and funding
archaeological treatment measures at the cultural resources within the APE is founded on
SWRCB’s and USACE’s commitment to comply with Section 106 of the NHPA of 1996 (amended
2006). The HPTP was prepared to provide a cost effective and time-efficient approach for
completing archaeological data recovery excavations for the cultural resources within the APE.
The HPTP was based on a sampling strategy in which site areas with the greatest data potential
within the APE are targeted for archaeological data recovery. Less archaeological investigations
were expended on site areas that were already compromised by previous disturbances and/or
those that demonstrated a low archaeological data potential. However, the City of St. Helena
was committed to archaeological monitoring during all grading and data recovery if new and
relevant data was exposed during ground disturbing activities.
The Mishewal-Wappo Tribe provided a Native American monitor for the Phase III data
recovery effort, as well as all monitoring activities. Additionally, the Mishewal Wappo Tribe of
Alexander Valley and the client developed a burial agreement for the treatment of human
remains found as a result of construction or construction related activities associated with the
project.
Phase III data recovery excavations began in the summer of 2007 and proceeded for
three months. A Native American monitor was present at all times. The data recovery
excavation produced over 190,000 artifacts from roughly 60 cubic meters of soil. Three of the
excavation units from the Phase III data recovery in 2007 encountered human remains. In
accordance with protocol, the County coroner was called for the first incident. He accepted that
the burials were Native American as they were within a prehistoric context, and ceded control
21
over to Pacific Legacy Inc. The Native American Heritage Commission was contacted, who
appointed a most likely descendent from the Mishewal-Wappo to oversee how the burials were
handled.
Fieldwork for the archaeology monitoring operations began in September 2007 and
ended in December 2007. The site then sat idle for over a year as budget issues and public
protest prevented any further activity. Work resumed in June 2009 and continued
intermittently through November 2010.
Heavy machinery such as backhoes and excavators worked in small, irregular sections of
the site. These sections were called surface scrapes, and were typically irregularly shaped
polygons measuring several meters by several meters. The heavy machinery would
systematically remove soil in a controlled manner until sterile soils beneath the cultural deposit
were reached. One archaeologist and a Native American monitor closely monitored inside the
surface scrape watching the excavation activities, while another archaeologist raked through the
back dirt looking for human remains. A representative sample of artifacts located onsite were
collected in a controlled manner during Phase III data recovery. Monitoring operations focused
on the recovery of human remains.
This method of excavation resulted in the discovery of some burial pits, but also
destroyed the stratigraphic relationships that might have existed across the site. Because they
were not excavated in a controlled manner (in which the particular stratigraphic layer into which
they had been cut originally, if preserved, would have been carefully documented) recovery of
any extant archaeological data that might have informed archaeologists about the relative
dating of the burials was lost. Further, any artifacts directly over the burials would have been
removed as well and their association lost.
22
When a burial or feature was encountered, it was numbered and the machinery would
move to another surface scrape to continue working. Excavation of the feature might take place
a few days after discovery depending on the backlog of features to excavate and record.
Archaeologists would record the location and depth of the finds using a transit (theodolite) and
stadia rod. Data was recorded relative to the main site datum located in the eastern portion of
the site directly on top of the proposed floodwall. Several sub datums were also used across the
site to gain better visibility for shots.
A reading was taken on top of the skull (as this was generally the highest point for the
burial) and the bottom of the grave using the stadia rod viewed through the theodolite. A
trained osteologist would work to excavate and expose the burial and record as much
information as they needed in situ. This included the age, sex, burial flexure, orientation, and
any associated artifacts. The burial would be drawn and photographed, then carefully lifted and
taken to the laboratory at the Berkeley location of Pacific Legacy Inc. where a more thorough
examination would take place.
There were a total of 163 numbered burials. Burial 162 ended up not being human and
the number was discarded. This left 162 individuals that were recovered from the site. Burials
were labeled in order of discovery. Unfortunately five individuals were entirely removed and
deposited in the back dirt and sadly have only a rough provenience associated with them. A
rough placement in the stratigraphic profile was noted. All five fell within the range of the other
burials and were not considered outliers by depth.
These five were not considered for the analysis but they were located within the burial
area with the other burials. Figure 4 shows the locations of the burials within CA-NAP-399. The
23
burials are confined to the northwest corner of the site in an area roughly 120 meters east/west
by 30 meters north/south.

Figure 4: CA-NAP-399 Overview.
Figure 5 shows the burial numbers, labeled in order of discovery. 153 out of the
remaining 157 burials have depth data. Two of the last burials were recorded quickly and the
depths were not properly recorded. Depth data for Burial 97 was illegible. Depth data for Burial
42 was inconsistent, and had to be ignored.
24

Figure 5: Labeled Burials
25
The 157 individuals studied in this analysis are part of a larger population. The burials
that were recovered from the site immediately to the west during the mid 1990’s by Origer are
likely cotemporaneous with the majority of those from CA-NAP-399. An untold of number of
individuals could also have been eroded out of the site by the Napa River. Other individuals
could have completely decomposed. There could also be more burials underneath the mobile
home park to the south. This means the dataset used for the spatial analysis later in the paper
is incomplete.
It is unlikely we will ever know the total number of individuals who were interred on this
site. When conducting a spatial analysis it is important that all the points be used.
Unfortunately, this is not possible due to a number of circumstances. This paper analyzes the
157 individuals who have solid spatial information associated with them. Please keep in mind
that if the other burials were also a part of this analysis, some of the spatial statistics results may
have been different.
Analysis of the burials and the related artifacts occurred in 2011 and 2012. The
laboratory examination provided a more in-depth and thorough analysis of burials (Holson et al.
2013). The age and sex of the individuals were verified. Any health pathologies and
abnormalities were examined. An extensive look into the life of the individual occurred. Their
past health issues could be determined as could some of their activities in life. Most of the
musculature attachments to the bone were very large indicating very strong muscles, likely from
having to continually hike over the North Coast Range. Many individuals also showed signs of
anemia.
Artifacts recovered from the burials were catalogued and examined. Those artifacts
found directly associated with the individuals were called directly associated artifacts. Due to
26
the richness of the site, other artifacts were also recovered from the soil matrix (midden)
surrounding the burial. These artifacts may or may not be related to the burials, and are termed
indirectly associated artifacts.
All the Native American remains and artifacts recovered from burial contexts were
repatriated and reburied near CA-NAP-399 in spring of 2012. This was done in accordance with
the Native American Graves and Repatriation Act. A small ceremony was held with members of
the Wappo tribe overseeing the reburial. The burial was on public land which allows the
modern Native Wappo access to their ancestors.
2.6 Native American Laws and Regulations
The North American Graves and Repatriation Act (NAGPRA) is a federal law that was
passed in 1990 (National Park Service, 2013). This provides a process for museums and federal
agencies to return certain Native American cultural items (human remains, funerary objects,
sacred objects, or objects of cultural patrimony) to lineal descendants and culturally affiliated
Indian tribes and Native Hawaiian organizations. NAGPRA laws and regulations are available
online at http://www.nps.gov/nagpra.
The National Historic Preservation Act (NHPA) was passed in 1966 and created the
National Register of Historic Places (NRHP), the list of National Historic Landmarks (NHL), and
the State Historic Preservation Offices (Advisory Council on Historic Preservation, 2013). NHPA
requires federal agencies to consider the effects of proposed federal projects on historic
properties. Federal agencies must initiate consultation with the State Historic Preservation
Officer (SHPO) as part of the Section 106 review process. CFR 800 is available online at
www.achp.gov/regs-rev04.pdf.
27
The California Environmental Quality Act (CEQA) was passed in 1970 (California Natural
Resources Agency, 2013). Part of CEQA requires state and local government agencies to
consider the environmental consequences of projects over which they retain discretionary
authority even after and environmental impact report have been certified. Cultural resources
management falls under the purview of the environmental impact reporting. Statutes and
guidelines concerning CEQA are located online at
www.ceres.ca.gov/ceqa/docs/CEQA_Handbook_2012_wo_covers.pdf.

28
Chapter 3 - Literature Review
In order to understand the analysis of the burials presented in this thesis, one must have
an understanding of the context in which this research exists. Relevant information regarding
archaeology and GIS are presented below, as are applicable studies incorporating the two fields
of study.
3.1 Mortuary Studies in Archaeology
Archaeology is the study of human culture through the physical remains it leaves
behind. While not an exact science, it often relies on subjective observations based on
ethnographic data to interpret the past. A variety of terms are used in this thesis that, while
familiar to archaeologists, may be unknown to other individuals reading this paper.
Archaeological terms and their definitions are presented in the Glossary section at the end this
paper.
The analysis of deceased individuals and their burials has been one of the most widely
studied aspects of archaeology. A burial is defined as the result of a series of ritualized practices
performed in relation to death (Fahlander and Oestigaard 2008).
The views regarding mortuary analysis have changed with the different paradigms of
archaeology over the last century. The cultural-historical paradigm championed by Kroeber in
the 1920s saw burials as “unstable, varying independently of biological social behaviors, and
that the level of similar or multiple practices among independent sociocultural units was the
result of cultural mixing, hybridization, or of generic or affiliational cultural relationships”
(Bellifemine 1997:10-11 summarizing Kroeber 1925). Binford (1971) refuted this view based on
29
his comparative ethnographic study of 40 societies where he found that burials reflected
patterns found in society.
A traditional perspective that remains in use today still regarding mortuary analysis is
based on the premise that disposal of the dead by ancient societies reflected patterns inherent
in society which reflected the social position of the individual (Binford 1971, Saxe 1970, Brown
1971, Chapman et al. 1981, Fahlander and Oestigaard 2008). This is highlighted in the Binford-
Saxe Model, named for two researchers whose work has guided mortuary analysis for the past
fifty years.
In the last decade, a new way of conducting mortuary analysis has gained popularity
called bioarchaeology. Bioarchaeology focuses more on the individual as it creates a narrative
of an individual’s life, and interprets their place in society. It has also been termed
osteobiography. For a more comprehensive overview of mortuary analysis refer to Brown
(1971), Chapman and Randsborg (1981), Chapman et al (1981), Goldstein (1981), and O’Shea
(1984).
The Binford-Saxe Model
Saxe (1970) formulated a theoretical framework that argued mortuary practices could
be analyzed in the context of social systems. Saxe created three terms that form the foundation
of his study: social identity, identity relationship, and social persona. Social identity is “a
category of persons or what has been called a social position or status” (Saxe 1970:4). Identity
relationship is “when two or more social identities are engaged in a social relationship” (Saxe
1970:4). Social personae are “a composite of several social identities selected as appropriate to
a given interaction” (Saxe 1970:7). Saxe proposed that different types of social organizations
30
with different sets of social relationships would evidence different sets of disposal treatments.
Saxe created his theoretical framework in the form of eight hypotheses.
Hypothesis 1 states “that the components of a given disposal domain cooperate in a
partitioning of the universe, the resultant combinations representing different social personae”
(Saxe 1970:65). Components are any unidimensionally scaled value of a variable in mortuary
practices that reflect social personae differently (Saxe 1970). This means that social personae
are symbolized by differences in mortuary practices (Bellifemine 1997).
Hypothesis 2 states “in a given domain, the principles organizing the set of social
personae (produced by cooperative partitioning of the universe of disposal components) are
congruent with those organizing social relations in the society at large” (Saxe 1970:66). For
egalitarian societies, differences are based on age, sex, and/or personal achievements while in
non-egalitarian societies differences are based on ascription (Bellifemine 1997).
Hypothesis 3 states “within a given domain personae of lesser social significance tend to
manifest fewer positive components in their significance relative to others, and conversely”
(Saxe 1970:69). This means the higher the social status, the higher the number of burial
components (i.e. grave goods).
Hypothesis 4 states “the greater the social significance of the deceased the greater will
be the tendency for the social personae represented at death to contain social identities
congruent with that higher position at the expense of other (and less socially significant
identities) the deceased may have had in life, and conversely” (Saxe 1970:71). This hypothesis
focuses on the content of the social personae rather than the number of components (Saxe
1970). Positions of greater social significance will involve more groups and exhibit greater
31
privilege according to Saxe. Aspects of being a kin group member or great hunter for example,
might be suppressed as compared to attributes relevant to being chief of the tribe.
Hypothesis 5 states “the more paradigmatic the attributes evidenced in the key
structure of the domain, the less complex and more egalitarian the social organization.
Conversely, the more tree-like the attributes, the more complex and the less egalitarian the
social organization” (Saxe 1970:75). Bellifemine (1997) summarized this by stating the greater
the independence of the burial attributes, the more egalitarian the society; while the greater
the number of correlations found among burial attributes, the more hierarchal the social
organization.
Hypothesis 6 states “the simpler a sociocultural system the greater will be the tendency
for there to be a linear relationship between number of components in significata, number of
contrast sets necessary to define them and the social significance of the significata; and
conversely” (Saxe 1970:112). In egalitarian societies there are opportunities for many to attain
high status, while in a highly stratified society the individual with the highest rank may be
unique (Bellifemine 1997).
Hypothesis 7 states “the simpler the sociocultural system the less divergence will be
evident in the treatment of different kinds of deviant social personae, and conversely” (Saxe
1970:118). This states the relationship between the complexity of a society and the degree of
differentiation in the treatment of different kinds of individuals such as the disabled, deviants,
and the sickly (Bellifemine 1997).
Hypothesis 8 states “to the degree that corporate group rights to use and/or control
crucial but restricted resources are attained and/or legitimized by means of lineal descent from
the dead (i.e. lineal ties to ancestors). Such groups will maintain formal disposal areas for the
32
exclusive disposal of their dead” (Saxe 1970:119). This hypothesis moved into causality in
describing economic and/or ecological reasoning for burial areas (Bellifemine 1997).
Goldstein (1981) examined the negative connotations behind Hypothesis 8 through
empirical testing, and demonstrated that the hypothesis does not work in both directions. She
found that not all corporate groups that control crucial and restricted resources through lineal
descent will maintain formal and bounded disposal areas for their deceased. Goldstein restated
Hypothesis 8 in three separate but related sub-hypotheses. The first is that if a corporate group
controls or uses a restricted resource through lineal descent from the dead, they will regularly
reaffirm the lineal corporate group by the popular religion and its ritualization. The second is if
a permanent or specialized bounded area for the exclusive disposal of the groups dead exists,
then it is likely that this represents a corporate group that has rights over restricted resources by
means of lineal descent linking the corporate group to the dead. The third is that the more
structured and formal the disposal area, the fewer the alternative explanations of social
organization that may apply, and conversely.
Binford (1971) conducted an ethnographic study of 40 societies regarding their burial
practices and refuted the cultural-historical paradigm. He suggested that two components
should be evaluated. The first is social persona (a composite of the social identities maintained
in life and recognized as appropriate for considerations at death) and the second is the
composition and size of the social unit recognizing status responsibilities to the deceased.
Binford (1971) set forth three characteristics for the funerary treatment of the
individual. The first characteristic is body treatment, which involves the preparation of the
body, form of disposal, and disposition of the body. The second is grave preparation, which
33
includes the form, orientation, and location of the grave. The third is grave furniture, which
consists of the furniture (different types of grave goods), the quantity, or a combination of both.
Binford (1971) put forth three propositions regarding burials, as follows:
Proposition 1 states that “there should be a high degree of isomorphism between (a)
the complexity or the status structure in a sociocultural system and (b) the complexity of
mortuary ceremonialism as regards differential treatment of persons occupying different status
positions” (Binford 1971:18). This states that the more complex a society, the more complex the
mortuary treatments.
Proposition 2 states that “there should be a strong correspondence between the nature
of the dimensional characteristics serving as the basis for differential mortuary treatment and
the expected criteria employed for status differentiation among societies arranged on a scale
from simple to complex” (Binford 1971:19). This proposition states that simpler societies should
have mortuary treatments based on physical characteristics such as age, sex, and personal
achievement while more complex societies base their mortuary treatments on more abstract
thoughts (Bellifemine 1997).
Proposition 3 states that “the locus of mortuary ritual and the degree that the actual
performance of the ritual will interfere with the normal activities of the community should vary
directly with the number of duty status relationships obtaining between the deceased and other
members of the community (scale of identity)” (Binford 1971:21). This proposition states that
older adults who have a high number of intra community relationships will be buried in a more
central location than those with less relationships (such as children and infants).
Binford (1971:25) concluded that “variation among cultural units in frequencies of
various forms of mortuary treatment vary in response to (a) the frequency of the character
34
symbolized by the mortuary form in the relevant population and (b) the number and distribution
of different characteristics symbolized in mortuary treatment as a function of the complexity
and degree of differentiation characteristic of the relevant society.”
The unified Binford-Saxe research approach has become the framework in modern
mortuary analysis. It has also created a basic foundation for the construction of larger
arguments concerning mortuary analysis (see O’Shea 1984, Brown 1995).
Bioarchaeology
Bioarchaeology shifts away from the Binford-Saxe Model by focusing on an individual
and their place in society. This is because the Binford-Saxe Model seldom focuses on
individuals. Populations are comprised of individuals, and those individuals provide a rich
source for developing an informed understanding of the lives, lifeways, and lifestyles of
ancestors (Stodder and Palkovich 2012). Bioarchaeology looks at diet and nutrition, health and
disease, demography, physical behavior, and lifestyles in the past (Larsen 1997). This essentially
writes a narrative of an individual.
Skeletal remains represent the majority of burials recovered across the globe. The
skeleton can provide a wealth of knowledge to the eye of a trained osteologist. Chemical
analysis of tooth enamel can reveal where an individual was born and where they moved to.
Repetitive motions can build muscle mass, increasing the size of the musculature attachment to
the skeleton. Previous injuries can give an insight into levels of interpersonal violence in society.
Those individuals who suffered from severe bone infections and even amputations can be
shown to have relied on the compassion of their family and community to stay alive as long as
they did.
35
This author is not a trained osteologist so there will be no bioarchaeology analysis in this
study. It is mentioned here for the sake of completeness. Larsen (1997) offers a comprehensive
overview regarding bioarchaeology. Stodder and Palkovich (2012) collect a series of
osteobiographies telling the narratives of deceased individuals from across the globe. Goldstein
(2008) provides an overview of modern trends in bioarchaeology.
Other Studies
While the Binford-Saxe model forms the foundation of modern mortuary analysis, and
bioarchaeology is increasing in popularity, there are many other approaches that give specific
insights into mortuary analysis. A brief discussion of a few such relevant works follows.
Brown (1981) focused on the issue of rank in prehistoric burials which is one of the most
widely studied aspects of mortuary analysis. He distinguished between social rank, power, and
authority as they seem to operate independently in small scale societies. These are related to
the material world using the Binford-Saxe Model. Brown stated that three arguments can be
employed to translate the archaeological record into forceful statements about the organization
of prehistoric groups. The Effort-Expenditure argument is that the greater the social rank of the
deceased, the greater the expenditure of energy (and wealth) in the internment. The Symbol of
Authority argument is that the disposition of symbols of authority among the deceased will
indicate the composition of the group within which authority is normally vested. The Age/Sex
Distribution argument is that normal populations should exhibit an equal ratio between sexes;
any deviation from this ratio can be seen as an indication of differential internment.
Brown also laid out three pitfalls that may await an archaeologist conducting a mortuary
analysis. The apical social order may be missed if there are not enough levels in society to form
36
distinctions between levels. Symbols of authority may not be identified by the archaeologist,
their meaning lost to time. Complex burial processing may also create false impressions of
disposal programs. Implications for this study from Brown’s three pitfalls involve there not
being a great deal of understanding about the levels of social order during the Upper Archaic.
Only broad generalizations regarding increasing complexity are mentioned with no obvious
examples given. Symbols of authority may only be recognized as utilitarian artifacts or wealth
goods, their meanings as symbols of authority lost over time. Most of the burials at the site
were buried very similarly, so there really was no false impression of disposal systems.
O’Shea’s (1984) seminal work covers a large portion of burial analysis, but it is his
insights into variations among burials that are relevant to this paper. Variation in the
organization and content of a society’s funerary treatment program can be summarized in terms
of two basic types of change: the manner in which a particular distinction is expressed through
the funerary ritual or the social positions that are marked or emphasized in the ritual. Variation
is the symbolic expression of social distinctions which may arise as a result of three basic forms:
the markers may change as a result of a conscious design by the living; they may vary due to
alteration in the overall inventory of material culture; or the markers may effectively change as
a result of variation in the consistency with which the proscriptive and prescriptive conventions
of funerary treatment are applied by the living.
Larsen (1997) reiterated that mortuary behavior is highly variable. She also emphasized
the use of ethnological and historical data in the analysis of mortuary behavior as especially
important in that the evidence gives solid grounding for phenomena that are simply not
available based on archaeological data alone.
37
Chapman (2000) argued that burial analyses are generally heavily under-theorized,
especially concerning agency structure relations. He also argued for conducting a detailed
analysis of smaller groups of graves within a cemetery rather than analyzing cemeteries as
closed entities.
Milliken et al. (2007) succinctly described the mortuary patterns and symbolic
expressions in the San Francisco Bay Area. The authors discussed various grave goods in terms
of energy expenditures. Shaped stone mortars are the costliest, and appear after 1200 AD.
Shell beads are also very costly to manufacture, as thousands of beads went into the ground
each year as mortuary offerings. The authors described the four main modes of mortuary
locations and organization in the Bay Area: the first is the most common and consists of the
non-cemetery pattern where people were buried in a dispersed informal way in and around
villages; the second is cemeteries in rich midden adjacent to villages; the third is cemeteries
located away from villages in sterile soils; and lastly possibly dedicated cemetery mounds with
formal burials and some dietary residue from feasting.
Fahlander and Oestigaard (2008) stated that objects follow the dead as either personal
objects or gifts that may relate to different social relations, various persons and groups the
deceased had with the living. They also reiterated that perishable materials may not be
recoverable. It is also not clear whether the deceased’s profession or status was most
important.
To summarize, burials reflect the society which interred them (Saxe 1970, Binford 1971).
Differences in mortuary practices can reflect status, social ties, and the rank of the individual, as
well as the structure of society (Saxe 1970). The more complex a society, the more complex the
mortuary treatments should be (Binford 1971). A simpler society should have mortuary
38
treatments based on physical characteristics while a more complex society would be based on
more abstract principles (Binford 1971). The locations of discrete disposal areas can also
indicate control over resources (Saxe 1970, Goldstein 1981). It is important to consider the
ethnographic data concerning mortuary practices when conducting a mortuary analysis on a
prehistoric population. It is also important to realize that preservation may mean that certain
burial goods are underrepresented, and that a true representation of burial goods may not be
possible. Finally, the deceased individual did not bury themselves. They were interred by
members of their own society, who cared enough about them to bury them in the first place.
3.2 Using GIS in Burial Analysis
The use of GIS in burial analysis has increased recently, but it is still mostly used to
merely display spatial distribution of the burials. This ignores an important aspect regarding GIS,
the ability to analyze the spatial relationships between attributes of the burials.
3.2.1 World Studies
There have been several studies that have merged GIS with mortuary analysis from
across the globe. While few intra-site studies have been completed (most focus on landscape
analysis) there are a few intra-site mortuary studies that are relevant to this paper.
Goldstein (1981) produced a very influential article on spatial analysis of burials. She
reiterated that mortuary practices are reflections of interpersonal, intergroup, and intra-group
relationships as well as society itself. Examining the spatial component of burials can yield
information on at least two levels. The first is the degree of structure, spatial separation, and
ordering of the disposal area which may reflect organizational principles of society as a whole.
The second is the spatial relationship between individuals within a disposal area can represent
39
status differentiation, family groups, descent groups, or spatial classes. She observed that
archaeologists analyze mortuary sites almost exclusively in terms of substance languages, but
they should also use space-time language as well.
Goldstein closed her article with five conclusions: mortuary systems are a
multidimensional system which includes a spatial component; the spatial component is also
multidimensional, and may reflect different levels of relationships and interactions; simple visual
techniques are the best way to begin a spatial analysis; when the spatial component is used as
the framework for examining the results of substance language approaches it can yield an
understanding of the meaning of the groups or statuses represented; it is the interplay between
the substance and spatial components which provides the maximum information about the
cultural elements represented in a mortuary site.
Aldenderfer (1982) analyzed methods of cluster validation for archaeology. He stated
that archaeologists who use cluster analysis often fail to validate it. Aldenderfer (1982:70)
defined validation methods as “methods which assess the compatibility of a clustering solution
with a particular theoretical perspective on what constitutes good classification.” He recognized
three families of cluster validation, but stops short of saying which one is the best. Aldenderfer
noted it is up to the archaeologists themselves to determine which validation to use and that
choice reflects their own biases of which they should be aware.
Voorrips and O’Shea (1984) created a method for the analysis of spatial patterning
based on aspects of spatial autocorrelation. By using a computer simulation they show that the
join count statistic has a wider validity than was originally presumed. The join count statistic is
the simplest measure of spatial autocorrelation used for binary variables (in this case, present or
absent). They apply this analysis to a late Mesolithic cemetery in Russia by studying three
40
pendant types in graves created from elk, bear, and beaver teeth. Voorrips and O’Shea (1984)
notice several interesting patterns such as all three pendant types exhibiting significant
clustering at a relatively small number of neighbors, but only elk and bear are still clustered at
10 to 13 neighbors. At 30 neighbors however, beaver pendants are clustered, bear pendants
are uniformly distributed, and elk are randomly distributed. The authors interpret these as
wealth tokens accumulated and gifted through close kin relations which form small corporate
units, probably extended families, which were buried in close proximity to one another.
Voorrips and O’Shea (1984) stated that the absolute Euclidean distance between
neighbors plays no part in their analysis, only the rank order distance expressed in the neighbor
order matrix matters. Euclidean distance is the shortest distance between two points. They
maintain that their analysis methods add a valuable dimension to spatial analysis in
archaeology.
Savage (1997) used GIS to conduct a cluster analysis of an Egyptian Predynastic
cemetery in order to determine clusters of burials. He found that there were six clusters of
burials. He went on to examine the distribution of grave goods, architectural elements, animal
offerings, and the temporal date range for burials within the clusters. Savage found that
descent based on kinship, power, and competition appear as powerful organizing principles. He
suggested that the clusters seem to be descent groups likely representing clan-type
organizations or different factions that made up the socio-spatial structure of Predynastic
society. Savage found that economic power is not shared equally among the different groups,
and evidence of competition occurs as intensification in plundering, elaboration of grave
architecture and mortuary ritual, and an increase in the number of grave goods occur through
time. He believed these findings imply why Upper Egypt began to expand.
41
Smith and Lee (2008) analyze two burial areas from a sedentary Neolithic village in
Jiahu, China, dating back to 9,000 BP. There were several discrete burial areas uncovered during
excavations, but they chose to focus on two where the boundaries had been completely
defined. Graves were partitioned into discrete formal disposal areas with graves being more
densely distributed towards the center of the disposal areas. They used spatial analysis
techniques to come to a number of conclusions regarding the burial area. There were smaller
partitions within the graveyards, with some graves sharing unique mortuary features.
Sometimes the graves cut into other graves. The authors argue this dense distribution projects
a collective group identity. The authors also found that there was differential treatment of the
single and collective burials during the second phase of the site.
3.2.2 Regional Studies
There have been relatively few GIS studies in the Bay Area and California that focus on
mortuary analysis. Only four could be located for this paper. This may be due to the relative
low number of sites producing a large number of burials. It could also be that private cultural
resource management firms excavate the sites to fulfill the environmental laws, but do not
publish scholarly papers on their findings. These companies may also not have access to GIS
technology.
Cartier et al. (1993) developed a statistical model to quantify social inequality based on
the range of wealth within a given mortuary component. This methodology scored the value of
each type of item found with the burials. Those items that were uncommon, exotic, and took a
large amount of production time were given higher scores. The authors then totaled the value
and gave each grave a grave association score. By using the score distributions, they found a
42
distinction along a cline from the poorest mortuary located at CA-SCL-128 to the richest
mortuary at CA-SCL-690. Wealth was more evenly distributed at CA-SCL-690, a predominately
middle to late period transition site. The inequality was highest at the poorest site, CA-SCL-128,
which was a mixture of middle and late period components.
Bellifemine (1997) examined the burials from the Yukisma Site (CA-SCL-38) in Santa
Clara County. She examined 244 individuals (and their burial goods) that span a period of 2,000
years. Bellifemine utilized a multivariate analysis to demonstrate a high degree of spatial
organization at the site. Multivariate analysis involves observation and analysis of more than
one statistical outcome variable at a time. She found the site is a highly structured cemetery
where individuals were allocated to specific areas based on their age and gender. She also
found a solid dependency between the age of the individuals and the spatial cluster in which the
grave was located. A similar correlation is found between sex and the spatial cluster. Artifact
diversity shows differences among the spatial clusters, suggesting wealth inequality existed in
prehistoric society. Bellifemine hypothesized that some spatial clusters could represent lineal
groups or moieties based on sex. She also found a high correlation between spatial cluster
distribution and the mode of internment of its individuals. The results indicate that the age, and
to a lesser degree the gender, of the individual are strong determinants of the location of burials
in the cemetery.
Luby (2004) examined the mortuary behavior of hunter gatherers associated with a San
Francisco Bay Area shell mound. He used cluster analysis to examine the burials from CA-ALA-
328 where approximately 571 burials were recovered during various excavations from the
1930s-1960s. He focused on the concept of inequality and its link to surplus. Luby summarized
Price and Feinman (1995) noting that inequality possesses political, economic, and ideological
43
dimensions, is present in all societies, and varies by degree along a continuum. He found that
the degree of inequality lessened as the site transitioned from a cemetery to a shell mound.
Luby also suggested that the concept of corporate group membership implied by the submound
cemetery was later transferred to the shell mound itself.
Luby reached several conclusions regarding mortuary analysis in the San Francisco Bay
Area. The first is this case illustrated the limitations of relying solely upon rank as a way to
understand mortuary behavior in hunter-gatherers. Second, it is important to expand the frame
of reference for mortuary analysis, both on theoretical and analytical grounds. Third, the
continued presence of some form of corporate group structure suggests that once a cemetery is
established, its function may be transferred to another site structure if significant mortuary
functions are retained. Fourth, although inequality has been observed in the mortuary practices
of CA-ALA-328 in this study, the cause remains unknown. Fifth, analysis of the mortuary
behavior of coastal central California hunter-gather-collectors can contribute to a wider
understanding of issues concerning inequality, exchange, and settlement patterns. Finally,
introducing concepts from more recent mortuary analyses into studies of coastal central
California populations can contribute to mortuary theory itself.
Wiberg (2005) excavated a late prehistoric site on Cache Creek in Yolo County,
California, approximately 30 miles west of Sacramento. A total of 122 formal burials were
recovered from an area measuring 75 meters north/south by 55 meters east/west. There were
88 inhumations and 34 cremations. Also discovered were 94 loci of bone which may have
represented burials. Wiberg does not expressly state that he is conducting a GIS analysis, but it
is evident that GIS was used in the production of the maps.
44
Wiberg (2005) discovered that most of the site features such as earthen ovens and
hearths were located outside the burial area. There were not a lot of post deposit disturbances
to the burials for additional internments. He found that there were three distinct clusters of
burials, distinguished by differences in the frequency of particular mortuary variables and grave
goods. They also discovered that most of the sub-adults were located along the perimeter of
the burial area.
3.3 GIS and Spatial Analysis for Burial Studies
GIS is a technology that is used to visualize, analyze, interpret, and understand spatial
data by analyzing trends, relationships, and patterns (Esri 2012). A variety of terms are used in
this paper that while familiar to GIS professionals, may not be understood by other readers. GIS
terms and definitions are presented in the Glossary section. The key concepts that are relevant
to this study are summarized below.
3.3.1 Spatial Autocorrelation
Spatial autocorrelation is the similarity between observations as a function of the
distance between them. This means that objects that are closer in space tend to be more
similar than objects further away. ArcGIS 10 Desktop help center (Esri 2012) gives the Global
Moran’s I statistic for Spatial Autocorrelation as:
𝐼 =
𝑛 𝑆 0
∑ ∑ 𝑤 𝑖 , 𝑗 𝑧 𝑖 𝑧 𝑗 𝑛 𝑗 = 1
𝑛 𝑖 = 1
∑ 𝑧 𝑖 2 𝑛 𝑖 = 1

where 𝑧 𝑖 is the deviation of an attribute for feature 𝑗 from its mean ( 𝑥 𝑖 - 𝑋 �
). 𝑤 𝑖 , 𝑗 is the spatial
weight between features 𝑖 and 𝑗 . 𝑛 is equal to the total number of features. 𝑆 0
is the aggregate
of all the spatial weights, and is represented by the following equation:
45
𝑆 0
= � � 𝑤 𝑖 , 𝑗 𝑛 𝑗 = 1
𝑛 𝑖 = 1

This evaluates whether the data points are dispersed, random, or clustered.
3.2.2 Cluster Analysis
Cluster analysis identifies the locations of statistically significant hot spots, cool spots,
and spatial outliers by subjecting a set of weighted features to the Anselin Local Moran’s I
Statistic (Anselin 1995). Cool spots are a statistically significant cluster of low values, and a hot
spot is a statistically significant cluster of high values. In addition to Local Moran’s I statistic, the
tool also calculates the z-score and p-value of the feature (Mitchell 2005). The z-score is the
standard deviations from the expected result. A number greater or more negative than two
standard deviations (z) would indicate that the feature is outside the normal distribution and
likely did not occur naturally. A positive z-score indicates the feature has neighbors with similar
high or low attribute values making it part of a cluster. A negative value indicates dissimilar
neighbor values making them outliers. The p-value measures the probability that the spatial
pattern was created by a random process. A very low p-value score (< 0.05) indicates that the
observed process is very unlikely to be the result of a random process. The Anselin Local
Moran’s I Statistic is given as
𝐼 𝑖 =
𝑥 𝑖 − 𝑋 �
𝑆 𝑖 2
� 𝑤 𝑖 , 𝑗 ( 𝑥𝑗 − 𝑋 �
)
𝑛 𝑗 = 1 , 𝑗 ≠ 1

where 𝑥 𝑖 is an attribute for feature 𝑖 , 𝑋 �
is the mean of the attribute, and 𝑤 𝑖 , 𝑗 is the spatial
weight between features 𝑖 and 𝑗 .
𝑆 𝑖 2
=
∑ ( 𝑥𝑗 − 𝑋 �
)
2 𝑛 𝑗 = 1 , 𝑗 ≠ 1
𝑛 − 1
− 𝑋 �
2

46
where n is the total number of features.
3.2.3 Grouping Analysis
Grouping analysis performs a classification procedure that tries to find natural clusters
within the data (Esri 2012). Given a number of groups to create, this analysis will look for a
solution where all the features within each group are as similar as possible, and all the groups
are as dissimilar as possible. It is unrealistic to try to identify a grouping algorithm that will
perform best for all the possible data scenarios. Groups can have different shapes, sizes, and
densities while their attribute data can reflect a wide variety of values and measurements. It is
best to think of the Grouping Analysis as an exploratory tool that can help the user learn more
about the underlying structure of their data (Esri 2012).
In order to better gauge the optimal number of groups to create for the analysis, the
Calinski-Harbasz pseudo F-Statistic is used. The Calinski-Harbasz psuedo F-Statistic is a ratio
reflecting within group similarity and between group differences and is given as:
𝑅 2
( 𝑛 𝑐 − 1)
1 − 𝑅 2
𝑛 − 𝑛 𝑐
where:
𝑅 2
=
𝑆𝑆𝑇 − 𝑆𝑆𝐸
𝑆𝑆𝑇

SST is the reflection of between group differences and is given as:
𝑆𝑆𝑇 = � � � � 𝑉 𝑖𝑗
𝑘 − 𝑉 𝑘 � � � �
�
2
𝑛 𝑣 𝑘 = 1
𝑛 𝑖 𝑗 = 1
𝑛 𝑐 𝑖 = 1

SSE is the reflection within group similarity and is given as:
47
𝑆𝑆𝐸 = � � � � 𝑉 𝑖𝑗
𝑘 − 𝑉 𝑡 𝑘 � � � �
�
2
𝑛 𝑣 𝑘 = 1
𝑛 𝑖 𝑗 = 1
𝑛 𝑐 𝑖 = 1

where 𝑛 is the number of features; 𝑛 𝑖 is the number of features in group 𝑖 ; 𝑛 𝑐 is the number of
classes; 𝑛 𝑣 is the number of variables used to group features; 𝑉 𝑖𝑗
𝑘 is the value of the 𝑘 𝑡 ℎ
variable
of the 𝑗 𝑡 ℎ
feature in the 𝑖 𝑡 ℎ
group; 𝑉 𝑘 � � � �
is the mean value of the 𝑘 𝑡 ℎ
variable; and 𝑉 𝑡 𝑘 � � � �
is the mean
value of the 𝑘 𝑡 ℎ
variable in group 𝑖 .
The largest F-Statistic indicates how many groups will be most effective at distinguishing
the features and variables specified. The Esri ArcGIS model only provides F-Statistics for the first
15 groups. There may be a higher number of groups past this number that are optimal.
3.2.4 Interpolation using Cokriging Analysis
Interpolation is an analysis method which uses spatially distributed data points to
produce a continuous field of values between all the known points. This continuous field is
generally represented as a raster, with individual values stored in each pixel.
There are two different general methods of interpolation, deterministic and
geostatistical. The deterministic approach assigns values based on the surrounding measured
values and uses non-statistical mathematical formulas to determine the resulting surface (Esri
2012). Inverse distance weighting interpolation (IDW) is one example of a deterministic method
in which the influence of a single data point on nearby interpolated values diminishes with
distance.
Geostatistical methods are based on statistical models that use measures of spatial
autocorrelation to predict the interpolated surface as well as to produce a spatial estimate of
the accuracy of the prediction. Kriging, one form of geostatistical analysis, is divided into two
48
distinct tasks: quantifying the spatial structure (variography) and producing a prediction. The
semivariogram measures the strength of spatial correlation as a function of distance. It is used
to estimate a curve that best describes the spatial structure of the data. This in turn can be used
to produce the predicted surface as well as the estimation of accuracy of the prediction.
There are several different types of kriging. Each of these methods may also produce a
specific output display. These are discussed more in detail in Appendix B. Cokriging, the
method used in this paper, uses the main variable of interest, its spatial autocorrelation, and
cross correlations between the variable of interest and other variables to make better
predictions.
The actual statistical models which underpin these methods are beyond the scope of
this paper. Tools for kriging, such as those provided by ArcGIS, use multistep wizards to assist
the well-informed novice to make the right choices and to produce valid results. The use of this
tool and choices made in this study are described in Chapter 8.
49
Chapter 4 - Data and Data Management Methods
This chapter describes the data used in the study and explains the processes used to
transform field survey data into GIS data. An overview of the data as a whole, including a visual
examination of the spatial distribution of attributes, artifacts, and pathologies and anomalies is
then discussed.
4.1 Digitizing Field Data for use in GIS
The burials, features, units from data recovery excavations, and modern infrastructure
located onsite were recorded using a theodolite and stadia rod. The theodolite was set to true
north on the datum or subdatum, and then the object was shot in. The theodolite had a digital
display that provided the degrees, minutes, and seconds. The distance and elevation were
calculated using the stadia rod. This data was transferred to a log.
The surveyed locations of the burials, features, excavation units, and other modern day
objects such as the existing floodwall, street, sidewalk, and trees were plotted manually on
blank paper using a compass and ruler from the readings from the theodolite. A scanned image
of the resulting map was georeferenced in ArcGIS using the WGS 84 datum as the geographic
coordinate system. A GPS point was taken on the datum and floodwall corner in the field using
a hand held Garmin GPS unit and used as the initial georeference points. Using aerial imagery
already georeferenced from Esri, the other modern day features were georeferenced as well.
Points representing the location of each burial were then manually digitized onscreen to create
the Burials GIS shapefile. Additional attributes (described in the next section) were then added
to the Burials attribute table. While it is possible to join a large spreadsheet to a shapefile, that
did not occur for this project. The attributes were continually expanded as examination
50
techniques evolved requiring additional attribute entries. This created a much more time
consuming approach to data entry. Table A-1 in Appendix A lists all of the attributes added to
each burial record.
It is important to recognize that a small amount of positional error may have entered at
any step in this process. A stadia rod may not have been perfectly upright which may have
shifted the depth or distance by a fraction of a centimeter. North may have been off slightly
when it was initially sighted using a compass and the theodolite. When the hand drawn map
was scanned it might have been slightly distorted and the georeferencing process may also have
a small positional error in the adjustment.
Fortunately, while there are many potential sources of positional error, the total errors
should be negligible. Given the small size of the site, these errors should be no more than a few
centimeters. Relative positions are preserved and the precision of point locations at the scale of
analysis is sufficient. We also must remember that the points represent only the crania of the
individuals and at the scale of any map in this report each dot is actually larger than the real life
crania of the individuals.
Several additional shapefiles and data layers were used during the course of the
analysis. All of the shapefiles were created by the author. All subsequent shapefiles and raster
datasets from analysis were derived from these shapefiles. The satellite imagery comes
courtesy of ArcGIS Online from Esri. The names and type of layer are presented in Table 2, along
with the source.
This dataset is limited in several ways, however, it still serves its purpose in
demonstrating the spatial analysis techniques discussed in the upcoming chapters. The first
thing to note is that this dataset does not encompass all the burials from CA-NAP-399. Two
51
more individuals were excavated by Origer (1994) in the early 1990’s immediately west of CA-
NAP-399; those were likely related to that site. These should have been included, but there was
no way to positively ascertain their correct position in space from the site drawings. During the
original recording of the site in the late 1970’s, Beard (1976) mentions that several burials have
eroded out of the banks during floods, but does not give a concrete number or position of those
burials. It should be pointed out that this was secondhand knowledge from local inhabitants.
These burials too should have been included in the dataset. It is also possible that a burial was
missed during monitoring activities. The excavator and backhoe did remove soil in small
increments; however there is always the chance something got missed. A more methodical
method of excavation would have allowed for more controlled manner of monitoring, however
this would have increased costs for the floodwall project exponentially, and delayed its
construction for years. Several burials also lost data between field excavation and the office due
to illegible writing or mistakes. These issues narrow down the usefulness of the dataset because
some of the spatial analytical techniques require all the points to have a value. An unknown
value for a burial can affect the analysis and skew results. As well, these issues of data loss
indicate that the dataset is incomplete in its spatial extent. Thus results from the analyses in
this report should not be seen as definitive nor should they be used for as foundations for
further archaeological analysis. As stated earlier, the main purpose here is to demonstrate how
spatial analysis may be used with data of this type.
52
Table 2: Shapefiles and Data Layers.
Name Type Creator/Credit
Burials Point Lucian N Schrader III
Floodwall Project Area Polygon Lucian N Schrader III
CA-NAP-399 Site Datum Point Lucian N Schrader III
Napa River Line Lucian N Schrader III
Site Boundary Polygon Lucian N Schrader III
Tribal Territory (Based on Kroeber 1925) Polygon Lucian N Schrader III
Political Boundaries of the United States of America Raster Esri
World Imagery Raster Esri
4.2 Burial Attribute and Artifact Data
Trained osteologists at Pacific legacy Inc., Dr. Lori Hager and Samantha Schnell, analyzed
the burials and coded the information using their own internal system. This coding was carried
over to the burial attributes in the Burials shapefile. Information about artifacts discovered with
each burial was also entered into the attribute table.
Age
Table 3 shows the codes used for the age of the individuals. Age is determined from the
maturation of the human bone. As the individual grows older, different bones fuse together in
known age ranges. By identifying which bones have and have not fused allows for the
estimation of the individual’s age. Due to preservation issues or missing skeletal elements, the
age may not have been able to be determined.
53
Table 3: Age Codes.
Code Age
9 Fetus (pre-term)
0 Neonate (at birth)
1 Infant (0-3 years)
2 Child (3-12 years)
3 Adolescent (12-20 years)
4 Young Adult (20-30 years)
5 Middle Adult (30-49 years)
6 Old Adult (50+ years)
7 Adult (20+ years)
8 Unable to determine
Sex
Table 4 refers to the sex of the individuals. Note that the sex is not the same thing as
gender, which is assigned by cultural mores and individual decisions. Until individuals undergo
puberty, it is very difficult to determine their sex. The shape of the pelvis is the main
determining factor in determining the sex.
Table 4: Sex Codes.
Code Sex
0 Too Young to Determine
1 Female
2 Possible Female
3 Unable to Determine
4 Possible Male
5 Male
Flexure
Figure 6 refers to the flexure of the burial, and provides four examples from Pacific
Legacy (2012). Flexure refers to how tightly the burial is flexed during its internment. This can
change slightly over time as decomposition occurs. This category could also be considered
burial position or burial type. Given that almost all the burials were flexed, it was decided to
focus more on the flexure.
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The burial flexure is defined as the range of flexing incurred by the burial. Tightly flexed
individuals have their knees drawn up to nearly touch their heads, where the leg bones run
parallel to the spine. Semi-flexed is the flexure where the legs are brought up to around 20
degrees from parallel with the spine. Flexed is where the legs are at a 45 degree angle from the
spine. Loosely flexed is where the legs are around 70 to 90 degrees with the spine. Examples of
each type of flexure are shown in Figure 6. These are examples from CA-NAP-399.

1 – Flexed (F)

2 – Loosely Flexed (LF)

3 - Semi Flexed (SF)

4 – Tightly Flexed (TF)
Figure 6: Burial Flexure Examples and Codes
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Orientation
Orientation refers to what cardinal direction the burial is facing. These were recorded in
degrees in the field. It is believed that whatever direction the burials were facing when buried
represents something sacred. That direction could be towards a sacred mountain or the setting
sun in the west.
Unfortunately when conducting the spatial analysis, it was realized that there was a
difference of 359 degrees between a bearing of 359 degrees and 0 degrees north when there
should have only been a one degree difference. This necessitated the bearings to be grouped
into eight categories of orientation covering 45 degrees each. These categories include: North
(337.5° to 22.5°), Northeast (22.5° to 67.5°), East (67.5° to 112.5°), Southeast (112.5° to 157.5°),
South (157.5° to 202.5°), Southwest (202.5° to 247.5°), West (247.5° to 292.5°) and Northwest
(292.5° to 337.5°). Each category was then coded 1-8, starting with north coded at 1 and
proceeding clockwise through northwest with a code of 8. This did not solve the problems as
Bellifemine (1997) noted similar problems and no solution. The implications for this included
biased and incorrect results within this study.
Side
Table 5 refers to what side the body was interred on. This has not been widely studied,
and the implications are unknown. Portions of the body may have also shifted post deposition,
affecting identification. The upper torsos of some burials were interred on the ventral (back) or
dorsal (front) side, but the legs were to the left or right. These are identified first with the
position of the torso, a backslash symbol, then the position of the legs. A cremation does not
technically have a side to be interred on, and are listed here for the sake of completeness.
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Table 5: Side Codes.
Code Sides
1 dorsal
2 dorsal/left
3 dorsal/right
4 ventral
5 ventral/left
6 ventral/right
7 left
8 right
9 sitting
10 cremation
Preservation
Table 6 refers to the preservation of the bone. The age and overall health of the
individual can influence this. Preservation is mainly influenced by the type of soil. Some soils
are very good for preservation while others are very poor.
Table 6: Bone Preservation Codes.
Code Bone Preservation Description
1 Poor Little to no integrity, sometimes the consistency of oatmeal
2 Poor/Fair In-between poor and fair preservation
3 Fair Some preservation issues but most of the bone is intact
4 Fair/Good In-between fair and good preservation
5 Good Solid, well preserved
Depth
The depth was compiled in two ways for this study. The first depth attribute, depth,
was originally intended to represent the depth to the bottom of the burial and was recorded in
centimeters below the site datum. It was not until several days later that it was realized that
ArcGIS interpreted these numbers in meters. So instead of the range of depth for the burials
being between 60 and 250 centimeters, the program thought it was 60 and 250 meters.
57
Depth in meters was created to remedy this mistake. This was entered in as a negative
numbers with two decimal places. The range for the depth was now -0.60 to -2.20 meters.
Artifact Association
Artifact association refers to burial goods that are associated with the burials. In any
given archaeological context there can be displacement of artifacts over time. These possibly
displaced artifacts are discovered near the burial, but cannot be positively linked to the burial,
and are termed here indirectly associated artifacts. Artifacts that are positively linked to the
burial are termed here directly associated artifact. Burials with no artifacts were coded with a
zero, burials with indirectly associated artifacts were coded with a one, and burials with directly
associated artifacts were coded with a two.
Artifacts
Most of the remaining attributes refer to the artifacts discovered with the individual.
The artifact type is given with either a “DIR” or “IND” before its name. “DIR” stands for direct
association, which means that these artifacts were found directly associated with the burial.
“IND” stands for indirect association, which means that the artifacts may or may not be related
to the individual.
These artifacts were examined in a variety of ways. A spatial autocorrelation analysis
was conducted for each type of artifact, examining both directly and indirectly artifacts
associated with the burials in an attempt to discern areas of possible professions or guild burials.
Total tools refer to the total number of direct and indirectly associated tools found with
each burial. Total artifacts refer to the total number of all artifacts found with each burial. Total
58
artifacts minus debitage and faunal remains are an attempt to eliminate background noise from
the midden by eliminating those artifacts commonly found in midden soils.
The tool diversity index is a way of measuring tool diversity from the burials. Each type
of tool was given a value of one. If that tool was present with the burial, it received a value of
one; otherwise the value would be zero. These values were added together, then divided by the
total number of tool categories, in this case 13 (bifaces, bone awls, bone pins, bowl mortars,
cores, core tools, drills, edge-modified flakes, handstones/manos, millingslabs/metates, pestles,
projectile points, and unifaces). A burial with a tool diversity index value of one can be said to
contain all the manner of tools available onsite, while a burial with zero has no tools.
The wealth diversity index was created in a similar way to the tool diversity index. It is
important to point out here that the word wealth brings with it certain connotations of status.
It is also possible that some tools could be considered wealth items. For the purpose of this
paper, wealth items are considered non-utilitarian items that are not required to live life day to
day. These items can also be considered personal adornment items and include bird bone
beads, charmstones, obsidian needles, pendants, quartz crystals, shell beads, stone beads, and
whistles. A burial with a wealth diversity index of one can be said to contain all the manner of
wealth items available onsite, while a burial with zero has no wealth items. Inferences regarding
status and hierarchy may be made from this value.
Health Issues
Several issues regarding the health of the individuals were analyzed as well. These
values were often represented as a presence (value of one) or absent (value of zero). These
59
pathologies and anomalies were not initially entered to see if there was any spatial clustering,
but rather to see their change through time.
Anemia is a condition characterized by low levels of iron in the blood, and presents itself
on the bones of the individual, usually in the form of small pin holes, although recent research
has shown there are other possible causes (Walker et al. 2009). Auditory exostoses is also
known as swimmers ear, and is characterized by the heavy ossification of the inner bones of the
ear in response to repeated exposure to very cold water. Dental caries are essentially cavities.
Healed fractures can give an indication into interpersonal violence or heavy workload. It should
be noted that there were only two burials with evidence of interpersonal violence (Holson et al.
2013). Osteomyelitis is a bone infection, and presents itself in the bones as accretions with
holes to drain pus. Femurs with anterior to posterior flattening are indications of heavy
workload and travel. The Inca bone is an extra plate in the skull, and is considered a non metric
trait that may be indicative of familial or genetic traits, and can give evidence into whether
certain parts of the cemetery were used by families.
4.3 Exploring the Data
The distribution of a variety of burial attributes and heath pathologies and anomalies
are shown in the following pages. This will allow for a preliminary visual analysis to determine if
there are any identifiable patterns to the naked eye. This can be considered one of the first
steps in spatially exploring a dataset.
A preliminary analysis of the data provides several insights and relevant details. The
first thing to notice from the distribution of burial points is they trend along an east/west axis.
This can be seen in Figure 8, or any of the other figures showing all the burial points. The Napa
60
River also flows in this same direction immediately north of the site. Any burials to the north of
the burial points likely would have been eroded out of the banks, as evidenced by Beard’s
original site recording in 1976. It also appears as if the burials are curving to the south along the
eastern edge of the burial deposit.
The burials are found in an area measuring approximately 125 meters east/west by 30
meters north south. This calculates to a burial area that has a burials density of roughly one
burial for every 23 square meters. There is roughly 7,500 cubic meters of soil in this area for the
two meters of site deposit with the burials. There is on average one burial per approximately 46
cubic meters.
It is also worth noting the spacing between the burials. Only two burials showed signs of
disturbance from post depositional burial activities. Given the relatively small area, and the
number of burials present, this would seem to imply two possibilities. The first is that there
were burial markers of some kind to keep track of where burials were located. There were no
obvious indicators above the burials that were observed during data recovery excavations or
monitoring. This could be the result of post depositional cultural activities on the site erasing
the markers. It could also be that the grave pits were visible in the landscape until they
naturally filled in over a course of years. The second is that cultural memory of burial events
lived on within the society. Each generation would inform the next of the location of the burials
so they would not be disturbed.
The burials selected for radiocarbon dating were chosen by Pacific Legacy for a variety
of reasons. The first was to try to obtain a more accurate obsidian hydration rind correlation, so
those burials with obsidian artifacts directly associated with them were given preference. In
addition, a sample of burials from across the site near the top and bottom of the deposit was
61
selected to provide a cross section and range of dates. Finally, those burials that were
particularly interesting were selected as well to answer select questions regarding the
individual. The results of the 22 radiocarbon dated burials are presented in Table 7.
Table 7: Radiocarbon Dated Burials (Holson et al. 2013).
Burial Age Sex Depth (mbd) Conventional Age (BP) Conventional Age Range (BP)
10 5 2 0.76 2130±30 2160-2100
15 4 3 1.85 2380±30 2410-2350
28 2 0 1.45 2090±30 2120-2060
29 2 0 1.45 2090±30 2120-2060
32 5 1 0.66 150±30 180-120
55 6 3 1.29 2030±30 2060-2000
56 4 5 1.52 2290±30 2320-2260
67 6 2 1.56 2450±30 2480-2420
70 5 1 1.57 1990±30 2020-1960
73 4 5 1.21 2200±30 2230-2170
79 1 0 1.29 2200±30 2230-2170
86 5 4 1.54 2430±30 2460-2400
94 5 5 1.42 2230±30 2260-2200
115 3 5 1.00 2200±30 2230-2170
116 5 1 0.68 2200±30 2230-2170
117 6 1 1.29 2240±30 2270-2210
125 7 3 1.24 2380±30 2410-2350
130 5 5 1.12 2150±30 2180-2120
139 6 5 1.34 2030±30 2060-2000
148 1 0 1.04 2140±30 2170-2110
149 5 5 1.34 2200±30 2230-2170
156 6 1 1.23 2380±30 2410-2350
Examination of the range of radiocarbon dates is shown in Figure 7. This shows the
burial dates are tightly clustered in time with one outlier. Radiocarbon dates have a low level of
uncertainty. This is usually plus or minus thirty years, a relatively small window for
archaeologists. Upon the initial examination of the artifacts from the data recovery excavation
units at CA-NAP-399, particularly the projectile point types, it was initially thought that the
62
burials would represent individuals from over the course of several thousand years. Instead, all
save one appear to date to a tight 500 year time span centering on 2200 BP.
Four distinct projectile point types were recognized in the assemblage form the
excavation units at CA-NAP-399. The types and relative date ranges come from Justice (2002).
A small, triangular, corner-notched arrowhead termed the Rattlesnake series dates from
approximately 800 BP to contact times. The Stockton series are arrowheads with distinctive
square denticulated edges in a variety of shapes dating from approximately 1,500 BP up to 500
BP. The Excelsior series are larger leaf shaped dart points that date from approximately 5,000
BP up to 1,300 BP. Mendocino Concave Base projectile points a dart points with a concave base
that date from approximately 5,000 BP to 3,000 BP. Most of the burials found from CA-NAP-399
are contemporaneous with the Excelsior series, while Burial 32, the protohistoric outlier, was
contemporaneous with the Rattlesnake series.

Figure 7: AMS Radiocarbon Dates from Burials (Holson et al. 2013).
Figure 8 shows the distribution of the radiocarbon dates across the site. There does
appear to be patterning based merely on visual inspection. The western portion of the site
seems to contain more individuals from approximately 2200 BP, while the older burials seem to
0 500 1000 1500 2000 2500 3000
Years Before Present
63
be located in the eastern half of the burial area, particularly towards the southeast. This could
imply that as the site was utilized, the burials were buried further and further to the west.

Figure 8: Radiocarbon Dated Burials with Dates.
Table 8 compares the radiocarbon dated burials by date across five one hundred year
date ranges. This format is created to better understand changes over time. While the sample
size is small, it does offer potential insights into how society changed over time.
The second and third date ranges from 2350-2150 BP seem to be high points for wealth
items and tools. There also seems to be more variability in flexure during these date ranges as
well. The orientation later in time seems to be more variable as well. There appears to be
fewer artifacts from the first date range from 2450-2350 BP.

64
Table 8: Radiocarbon Date Range Comparisons.
Burial Attribute
Date Range (BP)
2450-2350 2350-2250 2250-2150 2150-2050 2050-1950
n= 5 1 7 5 3
Sex
Male 1 1 4 1 1
Female 2 0 2 1 1
Unknown 2 0 1 3 1
Age
0-3 0 0 1 1 0
3-12 0 0 0 2 0
12-20 0 0 1 0 0
20-30 1 1 1 0 0
30-50 1 0 3 2 1
50+ 2 0 1 0 2
Unknown 1 0 0 0 0
Flexure
Loose 0 0 1 0 0
Flexed 0 0 1 0 0
Semi 0 1 2 0 0
Tight 3 0 3 1 3
Cremation 1 0 0 0 0
Orientation
North 0 0 0 0 0
Northeast 0 0 0 0 0
East 0 0 0 0 1
Southeast 0 0 0 0 0
South 0 0 0 0 1
Southwest 1 0 0 0 1
West 2 1 5 1 0
Northwest 0 0 2 1 0
Artifact
Association
None 1 0 0 0 0
Indirect 3 0 1 1 1
Direct 1 1 6 5 2
Total Tools 7 9 63 48 12
Average Tools 1.4 9 0 9.6 3
Highest Tool Index 0.15 0.23 0.23 0.31 0.23
Median Tool Index 0.08 0.23 0.15 0.15 0.15
Mean Tool Index 0.08 0.23 0.153 0.154 0.15
Total Wealth 0 115 249 12 2
Average Wealth 0 115 35.571 2.4 0.67
Highest Wealth Index 0 0.25 0.75 0.125 0.125
Median Wealth Index 0 0.25 0.375 0.125 0.125
Mean Wealth Index 0 0.25 0.286 0.1 0.083
65
Age
The distribution of age for the burials is unusual (see Figure 9). There were two
neonates, 11 individuals from 0-3 years, 11 from 3-12 years, six individuals from 12-20 years, 23
individuals from 20-30 years, 61 individuals from 30-49 years, 28 over 50 years of age, and 15
adults 20 years or older. There does not appear to be any clustering visible to the naked eye.
The distribution appears to be fairly random. There are slightly more individuals coded 7 along
the eastern edge, however this may be a reflection of preservation affecting the accurate aging
of the individuals.
There are very few children represented in the burial population. There are also a high
number of adults present. This would appear to form a normal bell shaped distribution related
to age, however that rarely happens in nature. In nature, there should be a more bimodal
distribution with children and elderly dying. This population appears to show that mostly adults
passing away. Possible implications and causes are examined further in Chapter 8.

Figure 9: Map of Age Attribute Distribution by Burial.
66
Sex
The distribution of sex is shown in Figure 10. There are 28 individuals that are too
young to determine sex, 47 females, 19 possible females, 19 that were unable to determine, five
possible males, and 39 males.
This attribute appears to be randomly distributed spatially around the burial area.
There are more individuals along the eastern edge of the site that are unable to have their sex
determined due to preservation issues. It is odd that there are more females present. In a
normal population the ratio between sexes should be closer to 50:50 while here it is closer to
60:40. Brown (1981) might consider this evidence of differential interment based on sex,
however there are a few possible explanations. The first is that there is a normal distribution
between the sexes but that evidence did not preserve. Some males may have suffered from
poorer preservation where their sex could not be positively determined. Some males may have
also died away from the village, and were unable to be transported back for burial.

Figure 10: Map of Sex Attribute Distribution by Burial.
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Flexure
The flexure of the burials is shown in Figure 11. There were 29 individuals where the
flexure could not be determined, four that were flexed, eight that were loosely flexed, ten that
were slightly flexed, and 106 that were tightly flexed.
Again, there does not appear to be any spatial patterning present to the naked eye. The
majority of the burials appear to be tightly flexed. There does appear to be a few more loosely
flexed burials in the western half of the burial area. The overwhelming type of internment could
be a reflection of religious beliefs or cultural preferences. The Windmiller culture, located in
Central California in the California Delta region, dates to approximately 3500 BP. Their
preferred flexure was fully extended. Another possible explanation could be found in energy
expenditure theory. This could mean that the grave was excavated to a point where the body
would just fit inside. Excavating a grave larger than was needed would require additional effort
or energy expenditure.

Figure 11: Map of Flexure/Position Attribute Distribution by Burial.
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Orientation
The orientation of the burials is shown in Figure 12. There were 28 individuals where
the orientation could not be determined, eight individuals oriented to the north, six to the
northeast, six to the east, two to the southeast, four to the south, 15 to the southwest, eight to
the west, and 20 to the northwest. Of the 28 individuals where no orientation could be
determined, four were cremations while the remainder had poor preservation that prevented
positive identification of their orientation.
There is no discernible pattern or clustering observed by the naked eye. The majority of
burials appear to favor orientation to the west. Most of the burials that are oriented more
towards the north appear to be located in the western half of the burial area.
The majority of burials are oriented to the west which is a common orientation in
prehistoric societies where the individual is orientated facing the setting sun. A cluster of burials
that deviates from the normal could indicate a difference in belief systems.

Figure 12: Map of Orientation Attribute Distribution by Burial.
69
Side
The side the burials were interred on is shown in Figure 13. There were 25 burials
where the side of internment could not be determined, 13 individuals were interred on the
dorsal side, six on the dorsal/left side, three on the dorsal/right side, 46 on the left side, 40 on
the right side, one sitting, 12 on the ventral side, four on the ventral/left side, three on the
ventral/right side, and four cremations.
This shows that there was a wide variety of sides for the individuals at CA-NAP-399 to be
buried on. There appears to be possible clustering based on the naked eye. The cremations
appear to be clustered near the southern portion of the burial area towards the center. There
are also slightly more individuals interred on the dorsal side in the western half of the burial
area. There also appears to be slightly more individuals interred on the ventral side in the
eastern half of the burial area. Burials interred on the left or right sides are the most common.
The individual interred in a sitting position is located near the northern edge of the burial area.

Figure 13: Map of Side Attribute Distribution by Burial.
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Preservation
The preservation of the burials is shown in Figure 14. Thirty burials had poor
preservation, 25 had poor to fair preservation, 41 had fair preservation, 21 had fair to good
preservation, and 40 had good preservation. Actually, the distribution of preservation is fairly
even along a spectrum of preservation, with the number of poor and poor to fair burials almost
equal to that of fair to good and good.
There does appear to be a cluster of poorly preserved burials along the eastern edge of
the burial area. All the burials along the eastern edge of the burial area have poor preservation.
The remainder of the site shows a fairly random distribution of preservation. There are a few
isolated instances of individuals demonstrating poor preservation in the western half of the
burial area. This could represent poorer preservation along the northern edge of the burial area
adjacent to the Napa River. The age and health of the individual may also influence the bone
preservation.

Figure 14: Map of Bone Preservation Attribute Distribution by Burial.
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Depth
The west/east profile of burial depth across the site is shown in Figure 15 below. Burial
80 is the burial furthest to the west. Burial 83 is the burial furthest to the east. There appears
to be one outlier. Burial 32 is the protohistoric outlier to the upper right at 62 centimeters
below the site datum.
There is a general trend of the burials being located higher in the soil column further to
the west. The burials are typically deeper along the eastern edge of the burial area. This
corresponds with the radiocarbon dates shown in Figure 8 above, where the older radiocarbon
dates are towards the east, and the more recent towards the west. This would seem to indicate
that the older burials are located deeper. This is further examined in Figure 51, from Chapter 7,
where grouping by depth (coupled with radiocarbon dates) is examined. This allows for a more
detailed analysis of depth and radiocarbon dates.

Figure 15: Stratigraphic Profile of Burials from West to East.
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Artifact Association
The association of artifacts is shown in Figure 16. Twenty-one burials had no artifacts
whatsoever, 100 burials had only indirectly associated artifacts, and 36 had directly associated
artifacts. This is a normal bell shaped distribution curve. This would seem to indicate a roughly
egalitarian society. If there was high number of individuals with no individuals, and a few
individuals with a high number of artifacts, it would be indicative of a hierarchal society.
There are some possible patterns or clustering discernible to the naked eye. It appears
that most of the burials in the western half of the burial area contain artifacts, while those in the
southeastern corner of the burial area have mostly indirectly associated artifacts. There is no
cluster of burials without artifacts which could indicate an area designated for “poor”
individuals. There appears to be small possible cluster of individuals with directly associated
artifacts. It is located roughly 30 meters west of the eastern edge of the burial area where six
individuals with directly associated burials are located within a five meter radius.

Figure 16: Map of Artifact Association Attribute Distribution by Burial.
73
Wealth Items
The distribution of the total number of wealth items is shown in Figure 17. The number
of individuals by the range of wealth items per burial is shown in Table 9. Table 10 summarized
the wealth items from the Burials from CA-NAP-399 by direct and indirect association.
Table 9: Number of Burials by Range of Wealth Items per Burial.
Total Wealth Items Per Burial Number of Individuals
0 100
1-3 47
4-11 2
12-23 1
24-35 2
36-41 1
42-116 3
117-251 1
Table 10: Summary of Wealth Items from Burials at CA-NAP-399.
Wealth Item Directly Associated Indirectly Associated Total
Bird Bone Beads 0 30 30
Charmstones 5 13 18
Glass Beads 233 0 233
Obsidian Needles 29 23 52
Pendants 4 3 7
Quartz Crystals 173 20 193
Shell Beads 182 13 195
Stone Beads 29 11 40
Whistles 0 3 3
Total 655 116 771
The shell beads were all manufactured from olivella shell. There were multiple olivella
bead types recovered from the burials (Holson et al. 2013). Types A, C, and G were recovered,
which typically date to the Middle Archaic period (Milliken and Schwitalla 2009). The stone
beads were manufactured from steatite. The quartz crystals could be Lake County diamonds, a
small quartz crystal found near Clear Lake. The pendants were manufactured from haliotis shell.
74
There appears to be more burials with wealth items in the western half of the burial
area. The eastern half has few wealth goods. This could perhaps be related to the poor bone
preservation. If the bone was poorly preserved in this area, then perhaps shell, which serves as
the material type for beads and pendants, decomposed completely. There is a small cluster of
individuals with more than 30 wealth items approximately 12 meters east of the western edge
of the burials ground, in a dense cluster of burials. The few burials with between 12 and 35
wealth items appear to occur in the middle of the burial area. The rest of the wealth items
appear to be randomly distributed across the burial area. The outlier in the northeast corner of
the burial area is Burial 32.
There are very few burials with a high number of wealth items. There are only five
individuals with more than 35 items. This suggests a certain degree of wealth inequality was
present at the site.

Figure 17: Map of Total Wealth Item Distribution by Burial.
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Total Tools
The distribution of the total number of tools is shown in Figure 18. A total of 639 tools
were recovered from the burial excavations. A total of 54 were directly associated with burials
while the remaining 585 were indirectly associated. Table 11 shows the number of burials by
range of total tools per burial. Table 12 summarized the tools by direct and indirect association
from the burials at CA-NAP-399.
Table 11: Number of Burials by Range of Total Tools.
Total Tools Per Burial Number of Individuals
0 35
1-3 63
4-6 28
7-10 16
11-13 6
14-17 4
18-22 4
23-29 1
Table 12: Summary of Tools from Burials at CA-NAP-399.
Tool Directly Associated Indirectly Associated Total
Bifaces 40 501 541
Bone Awls 5 8 13
Bone Pins 1 3 4
Bowl Mortars 0 6 6
Cores 0 2 2
Core Tools 0 3 3
Drills 0 1 1
Edge-Modified Flakes 2 41 43
Handstones 2 5 7
Millingslabs 0 1 1
Pestles 2 6 8
Projectile Points 2 5 7
Unifaces 0 3 3
Total 54 585 639

76
There does not appear to be any spatial patterning present based on the total number
of tools. Most of the burials have a few tools while very few have none. These seem to be
interspersed across the site.
Those burials with the highest number of tools are found towards the western half of
the burial area. This could be explained by a higher artifact density present in the midden soils.
This area also had the higher number of burials with directly associated tools. The burial with
the highest number of tools recovered was one of the first excavated, suggesting differing
excavation techniques may be at work. The other initial burials also had higher number of
burials. These differing excavation techniques are discussed later in Section 5.2.

Figure 18: Map of Total Tool Distribution by Burial.
77
Total Artifacts
The distribution of the total number of artifacts is shown in Figure 19. Table 13 shows
the number of burials by the range of total artifacts recovered per burial.
Table 13: Number of Burials by Range of Total Artifacts.
Total Artifacts Per Burial Number of Individuals
0 24
1-11 87
12-21 18
22-32 10
33-45 10
46-63 4
64-174 5
175-625 3
Seven burials (24, 32, 56, 73, 79, 80, and 141) had over 100 artifacts. The burials with
over 175 artifacts seem to be located along the periphery of the burial area. The center of the
burial area shows burials with few artifacts. There is no discernible spatial patterning visible.

Figure 19: Map of Total Artifact Distribution by Burial.
78
Total Artifacts minus Debitage and Faunal Remains
The distribution of the total number of artifacts without debitage or faunal remains is
presented in Figure 20. Table 14 shows the number of burials by range of total artifacts minus
debitage and faunal remains.
Table 14: Number of Burials by Range of Total Artifacts Minus Debitage and Faunal Remains.
Total Artifacts Minus Debitage and Faunal Remains Per Burial Number of Individuals
0 24
1-5 74
6-10 26
11-15 11
16-20 7
21-30 5
31-55 6
56-140 2
141-261 2
There is a small cluster of individuals with more than 30 artifacts near the western edge
of the burials ground, in a dense cluster of burials, however the remainder are low in number.

Figure 20: Map of Total Artifact without Debitage and Faunal Distribution by Burial.
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Tool Diversity Index
The distribution of the tool diversity index values for the burials is shown in Figure 21.
Thirty five individuals had a tool diversity index score of zero, 79 had a score of 0.08, 32 had a
score of 0.15, 15 had a score of 0.23, two had a score of 0.31, and one individuals had a score of
0.38.
There are very few burials with no tools. Most burials have one type of tool. The overall
distribution appears fairly random to the naked eye. There is a small cluster of individuals with a
score greater than 0.08 approximately 12 meters east of the western edge of the burials ground,
in a dense cluster of burials. Not surprisingly this distribution and map mimic that seen under
the Total Artifacts and Total Artifacts minus Debitage and Faunal Remains previously described.
Most of the burials with a higher tool diversity index are seen in the western half of the burial
area which may be a reflection of the early excavation techniques used. Cultural implications of
spatially clustered tool diversity index values could indicate areas designated for craftspeople.

Figure 21: Map of Tool Diversity Index Distribution by Burial.
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Wealth Diversity Index
The distribution of the wealth diversity index values for the burials is shown in Figure 22.
One hundred individuals had zero wealth items, 39 had a wealth diversity score of 0.125, 12 had
a score of 0.25, five had a score of 0.375, and one had a score of 0.75. The 0.75 is located in a
dense cluster of burials approximately 12 meters east of the western edge of the burial area.
Most of the burials show a very low diversity of wealth items. Most of the burials with
wealth items appear to be located in the western half of the burial area. The amount of
individuals with no wealth items is striking in this view. This could indicate that there is perhaps
some wealth inequality at work on the site. The very low diversity of wealth items among the
burials could also be evidence of this.
Cultural implications of clustered wealth diversity index can be applied to rank and
status studies, and also help denote specific areas for rank and status.

Figure 22: Map of Wealth Diversity Index Distribution by Burial.
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Anemia
The distribution of burials with anemia is shown in Figure 23. A total of 112 out of 162
burials demonstrated anemia. This is a very high number and percentage in a population. This
could indicate a variety of issues were at work on the site including malnourishment, a genetic
abnormality causing this, improperly leeched tannins from acorns, or another disease like scurvy
that presents itself in a similar manner to anemia on the bones. There is no clustering
discernible to the naked eye.
Recent work by Walker et al. (2009) has shown that anemia is not necessarily tied with
iron deficiency. They concluded that iron anemia does not provide a reasonable physiological
explanation for the lesions. The authors argue the small lesions are the result of megaloblastic
anemia acquired by nursing as an infant from depleted maternal vitamin B12 reserves and
unsanitary living conditions. A lack of vitamin C (scurvy) and a vitamin B12 deficient diet can
also cause the small pin hole sized lesions.

Figure 23: Map of Burials with Anemia.
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Auditory Exostoses
The distribution of burials with auditory exostoses is shown in Figure 24. A total of 15
burials were found with this condition. The burials exhibiting this condition appear to be
randomly dispersed across the burial area. There are none near the southeastern corner of the
burial area however. The preservation in this area was poor, so perhaps the evidence of
auditory exostoses did not survive.

Figure 24: Map of Burials with Auditory Exostoses.
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Dental Caries
The distribution of burials with dental caries is shown in Figure 25. Forty-one out of 162
burials had dental caries. There does not appear to be any clustering discernible to the naked
eye for this condition.

Figure 25: Map of Burials with Dental Caries.
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Healed Fractures
The distribution of burials demonstrating healed fractures is shown in Figure 26. A total
of 33 burials demonstrated healed fractures. There appears to be possible clusters based on
this condition.

Figure 26: Map of Burials with Healed Fractures.
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Osteomyelitis
The distribution of burials with osteomyelitis is shown in Figure 27. Only ten burials
show evidence of suffering from osteomyelitis. There does not appear to be any clustering to
the naked eye based on this condition. There are no cases towards the center of the burial area.

Figure 27: Map of Burials with Osteomyelitis.
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Femurs with Anterior to Posterior Flattening
The distribution of burials demonstrating femurs with anterior to posterior flattening is
shown in Figure 28. A total of 82 out of 162 burials suffered from this condition. There does not
appear to be any discernible clustering to the naked eye.

Figure 28: Map of Burials Demonstrating Femurs with Anterior to Posterior Flattening.
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Inca Bone
The distribution of Burials with the non metric Inca Bone trait is shown in Figure 29.
Only four individuals exhibited this non metric trait. It does appear that there could be possible
clustering based on visual observations.

Figure 29: Map of Inca Bone Distribution.
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Chapter 5 – Spatial Autocorrelation Analysis
This chapter examines the spatial autocorrelation of the burial points. The results are
presented in the first section. This is followed by a brief discussion regarding select attributes,
artifacts, and pathologies and anomalies. Numerous tables in this chapter refer to burial
attributes that are abbreviated; please refer to Table A-1 in Appendix A for their meanings.
5.1 Spatial Autocorrelation Analysis Results
The distribution of burial points was tested for spatial autocorrelation. The spatial
autocorrelation tool from ArcGIS 10 uses the Global Moran’s I statistic to determine feature
similarity based on both feature location and attribute value simultaneously. In this project the
tool was run using the following specifications: inverse distance was chosen so there is less of a
steep drop off in influence; Euclidian distance was chosen as it represents the closest distance
between any two points; and ROW was chosen for standardization as some point data may be
potentially biased due to sampling design or aggregation. The results of the demographic
attributes of the burials are summarized in Table 15 below. The Z-Score refers to the number of
standard deviations. The P-Score is the probability value, the closer to zero, the more likely that
the object is not randomly placed in space.
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Table 15: Burial Attributes Spatial Autocorrelation Summary.
Attribute
Moran’s
Index
Z-
Score
P-
Value
Distribution
Age 0.061 1.304 0.192 Random
Sex 0.026 0.627 0.531 Random
Flexure -0.046 -0.660 0.510 Random
Orientation 0.068 1.308 0.191 Random
Side -0.064 -0.965 0.334 Random
Bone Preservation 0.204 4.040 0.000
Clustered, <1% chance
random
Depth 0.400 7.688 0.000
Clustered, <1% chance
random
Depth in Meters 0.422 13.549 0.000
Clustered, <1% chance
random
Direct Association 0.104 2.121 0.034
Clustered, <5% chance
random
Total Wealth Items 0.015 0.521 0.602 Random
Total Tools 0.152 3.131 0.002
Clustered, <1% chance
random
Total Artifacts -0.004 0.059 0.9523 Random
Total Artifacts minus Debitage and
Faunal Remains
0.010 0.385 0.700 Random
Tool Diversity Index 0.102 3.529 0.000
Clustered, <1% chance
random
Wealth Diversity Index 0.128 4.486 0.000
Clustered, <1% chance
random
Clustering was found for bone preservation, depth, depth in meters, direct association,
total tools, tool diversity index, and wealth diversity index. The remainders of the burial
attributes were randomly distributed.
Spatial autocorrelation was examined for the tools. Clustering of these items could
denote possible profession, clan, or moiety areas within the cemetery area. The summary of
spatial autocorrelation scores for tools is presented in Table 16. Those items with only a zero
listed did not have any items recovered.
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Table 16: Tool Spatial Autocorrelation Summary.
Attribute Moran’s Index Z-Score P-Value Distribution
Direct Projectile Points 0.021 1.200 0.230 Random
Indirect Projectile Points -0.015 -0.316 0.752 Random
Direct Bifaces 0.015 0.826 0.409 Random
Indirect Bifaces 0.075 2.712 0.007 Clustered, <1% chance random
Direct Edge-Modified Flakes -0.013 -0.868 0.385 Random
Indirect Edge-Modified Flakes 0.130 4.687 0.000 Clustered, <1% chance random
Direct Drills 0 0 0 0
Indirect Drills -0.001 0.741 0.459 Random
Direct Cores 0 0 0 0
Indirect Cores -0.008 -0.047 0.963 Random
Direct Core Tools 0 0 0 0
Indirect Core Tools 0.015 0.843 0.399 Random
Direct Unifaces 0 0 0 0
Indirect Unifaces 0.133 7.420 0.000 Clustered, <1% chance random
Direct Millingslabs 0 0 0 0
Indirect Millingslabs -0.005 0.207 0.836 Random
Direct Handstones -0.013 -0.306 0.760 Random
Indirect Handstones -0.018 -0.436 0.663 Random
Direct Bowl Mortars 0 0 0 0
Indirect Bowl Mortars -0.020 -0.460 0.645 Random
Direct Pestles -0.012 -0.259 0.795 Random
Indirect Pestles 0.068 2.821 0.005 Clustered, <1% chance random
Direct Bone Awls -0.032 -1.350 0.177 Random
Indirect Bone Awls -0.012 -0.186 0.853 Random
Direct Bone Pins -0.007 -0.045 0.964 Random
Indirect Bone Pins -0.022 -0.851 0.395 Random
Spatial autocorrelation was then examined for the wealth items. Clustering of these
items could denote possible high status, high rank, or privileged areas within the cemetery area.
The summary of spatial autocorrelation scores for the wealth items is presented in Table 17.
Those items with only a zero listed did not have any items recovered.

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Table 17: Wealth Items Spatial Autocorrelation Summary.
Attribute Moran’s Index Z-Score P-Value Distribution
Direct Bird Bone Beads 0 0 0 0
Indirect Bird Bone Beads -0.012 -0.614 0.539 Random
Direct Shell Beads 0.100 5.242 0.000 Clustered, <1% chance random
Indirect Shell Beads 0.007 0.445 0.656 Random
Direct Steatite Beads -0.001 0.577 0.564 Random
Indirect Steatite Beads 0.003 1.192 0.233 Random
Direct Quartz Crystals -0.017 -0.608 0.543 Random
Indirect Quartz Crystals -0.009 -0.128 0.898 Random
Direct Natural Obsidian Needles -0.001 0.721 0.471 Random
Indirect Natural Obsidian Needles 0.081 2.910 0.004 Clustered, <1% chance random
Direct Pendants 0.113 5.225 0.000 Clustered, <1% chance random
Indirect Pendants -0.037 -1.638 0.101 Random
Direct Charmstones -0.034 -0.993 0.321 Random
Indirect Charmstones -0.036 1.552 0.121 Random
Direct Whistles 0 0 0 0
Indirect histles -0.037 -1.641 0.101 Random
Indirect bifaces, indirect edge-modified flakes, indirect unifaces, and indirect pestles
were found to be clustered for tools. Direct shell beads, indirect natural obsidian needles, and
direct pendants were found to be clustered for wealth items. The remainders of the artifacts
were randomly distributed or had no artifacts recovered.
Several pathologies and anomalies regarding the burials were examined for evidence of
spatial autocorrelation. These are summarized in Table 18. This could give insight into whether
particular parts of the cemetery were utilized as an area for the diseased or sickly. Auditory
exostoses and the femurs with anterior posterior flattening can possibly mark occupation or
profession areas in the burial area. The Inca bones are non-metric traits that can be inherited,
and could give an indication into whether familial plots existed within the cemetery. Inca bone
may also indicate occasional marrying in or capture of outsiders.
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Table 18: Pathologies and Anomalies Spatial Autocorrelation Summary.
Attribute Moran’s Index Z-Score P-Value Distribution
Anemia 0.003 0.290 0.771 Random
Auditory Exostoses -0.035 -0.955 0.340 Random
Dental Caries -0.071 -2.0875 0.037 Dispersed, <5% chance random
Healed Fractures -0.003 0.125 0.900 Random
Osteomyelitis 0.000 0.225 0.822 Random
Femurs with Anterior-
Posterior Flattening
0.002 0.259 0.796 Random
Inca Bone 0.013 0.722 0.471 Random
None of the pathologies and anomalies studied for spatial autocorrelation displayed
clustering. Dental Caries were found to be dispersed. Dispersed is the opposite of clustering,
and can be better visualized as a checkerboard pattern.
5.2 Spatial Autocorrelation Analysis Discussion
Only six of the burial attributes showed evidence of clustering while the remainders are
randomly distributed. It was expected that there would be clustering based on depth given that
all the burials were within a tight range. The bone preservation, directly associated artifacts,
and total tools were unexpected.
Age
There is no spatial clustering based on age, as the spatial distribution is random. If there
was to be preferential burial treatment based on age, it should reflect the age of the individual
as they had a longer life in which to establish social ties that would be reflected in the mortuary
treatment.
Brown (1981) argues that those individuals with greater social ties should be present
near the center of the village or cemetery, while those individuals with less social ties (the
younger individuals who did not have the time to develop many social ties) would be buried on
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the periphery of the burial area. We see no evidence for this occurring at CA-NAP-399,
indicating that preferential treatment based on age did not occur. If there was clustering based
on age, it would be an indication that greater social interactions were at work.
Sex
There is no spatial clustering based on sex, as the spatial distribution is random. Brown
(1981) argues that there should be an equal distribution between the sexes, and that any
deviation from this ratio can be seen as an indication of differential internment. The ratio
between men and women is not equal, as there are more women present. There are several
explanations for this, the most likely of which is that the men from the site simply died further
away, and could not be transported back and buried. This suggests that women, children, and
the elderly stayed in more central locations while the adult males ventured out further from the
village for either hunting or trading expeditions. Other reasons include one man with multiple
females or with females who live longer or with more than one female over life of male (if they
die earlier in childbearing for example).
Flexure
There is no spatial clustering based on the burial flexure as the spatial distribution is
random. This could be because the vast majority of the burials were tightly flexed, meaning all
the tightly flexed burials could be considered “normal”. Any deviations from this could be
considered outliers.
In terms of energy expenditures, the tightly flexed method is one of the easiest methods
of burials since it is placed in the smallest grave that requires the least amount of energy
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expenditure to dig depending on the depth. This could merely be a reflection of an expedient
burial method.
The depth of the burials can vary, and this can present various problems analyzing data.
This is especially evident from the western cluster of burials according to depth. There were five
radiocarbon dated burials dating to 2200 BP from this cluster. The center third of the cluster
showed a half meter difference between three of the radiocarbon dated burials (see Figure 53).
There are a variety of reasons this may have occurred.
There could have been more soil accumulation along this cluster, or an uneven
topography to begin with. Given that there were a high number of graves dating to the same
time period from this area, it could be that the surface level rose from all the burials taking
place. The depth of the burial could also have something to do with the individual themselves.
A person could be buried deeper based on their wealth, whether they were seen as
good or evil, or even based on the smell of the body. It is a highly variable attribute that can be
very difficult to discern if the grave outlines are not noticed during controlled excavation. Those
proponents of the greater energy expenditure that allowed for wealthier individuals to be
buried deeper fail to answer why being buried deeper would be preferred. One could suppose it
would allow for a smaller chance of the burial being robbed, but that rarely occurred in
prehistoric society. One could also propose that the deceased wished to be closer to their
ancestors, but this supposes earlier burials being present onsite.
Orientation
There is no spatial clustering based on burial orientation as the spatial distribution is
random. It was originally thought that most of the burials would be facing Napa Glass
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Mountain, an assumed place of sacredness. Most of the burials were facing west however,
possibly facing the setting sun or possibly even the ocean. Whether this means the inhabitants
did not consider the obsidian source sacred cannot be discerned from the evidence at hand.
Side
There is no spatial clustering based on the side the individual was buried on as the
spatial distribution is random. It was hoped that some sort of pattern would emerge from this
attribute, but it is simply too variable. The body may shift post internment which can also
complicate matters.
It is likely that the real meaning to which side the individual was buried on, if any, has
been lost to time. It could simply be an unintentional side effect of how the individual was laid
to rest in the excavated grave.
Preservation
There was spatial clustering based on bone preservation. This is unlikely the result of
cultural actions however. Bone preservation likely has more to do with where the burials were
placed in site, coupled with the age of the burials and the overall health of the individual at the
time of death. Different soils types and stratigraphic layers onsite may have affected
preservation, while those individuals who are older at the time of their death generally have less
robust bones.
In the case of CA-NAP-399, most of the poorly preserved burials are all located along the
eastern edge of the site. This area also appears to have been slightly lower in prehistoric times
judging by the differing burial depths and the radiocarbon dated burials across the site.
Geomorphological studies of the site reveals that this area suffered repeated breaches by the
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Napa River as it encounter Sulpher Creek slightly to the southeast (Holson et al. 2013). This
periodic inundation over the years likely accounts for the overall poor preservation of the
burials along the eastern edge of the site.
Depth
There is clustering based on depth. It is interesting to note that even with the large
discrepancy in the depth between the attributes “depth” and “depth in meters”, there was still
spatial clustering. This makes sense as the burials date within a five hundred year period and
suggests an almost continuous use of the site for burials over that time. There is very little
variation in the depth across the site, as all the burials were discovered within a 1.75 meter thick
swath.
Direct Association
There is spatial clustering based on direct association. The outliers are more likely to
produce more information when depth is factored in and their place in the site chronology is
established.
The direct association of artifacts could be skewed by a few individuals in an area who
were deliberately buried with grave goods. There does not appear to be a single area where
individuals without grave goods were buried, nor does there seem to be an area where those
with grave goods were buried. A likely explanation for this clustering can be found below in the
artifacts subsection.
Artifacts
There is clustering based on a variety of attributes related to tools. Spatial clustering
was discovered for Total Tools, Tool Diversity Index, Wealth Diversity Index, indirect bifaces,
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indirect edge-modified flakes, indirect unifaces, indirect pestles, direct shell beads, indirect
natural obsidian needles, and direct pendants.
Total tools could be the result of excavation techniques as there were many tools
present in the soil matrix that may or may not have been associated with the individual. Some
of the burials with the highest amount of tools recovered were some of the very first ones
excavated. Differing excavation techniques could account for this. The first 12 burials were
excavated with a “moat” around the burial. This moat was generally a few inches wide and
encircled the burial. Excavation of the burial proceeded inward from this moat as dirt was
pulled into the moat for removal. Excavation of this moat likely accounted for a higher number
of these tools. Later burials were dug stratigraphically, in which the visible outlines of the
graves were followed and the soil inside the excavated grave was screened. It is important to
keep in mind that varying excavation techniques can produce differing data, and must be
addressed.
Given that the tool diversity index is dependent on the total tools attribute, it is not
surprising that there is clustering for this attribute. Again, the differing excavation techniques
likely account for this clustering, as artifact density was very high in this area.
The wealth diversity index does have the potential to identify areas where individuals
with a greater potential amount of wealth were located. This could suggest that these areas
were designated areas devoted to individuals containing a higher number of variable burials
goods, possibly indicating rank or high status areas.
A disadvantage to this attribute is that it does not quantify the amount of wealth, or
even what sort of wealth is more important. Is an individual who only has five different wealth
items wealthier or considered higher status than one individual with over a hundred of the same
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item? These are questions that archaeologists may never know. As a measure of diversity it can
be helpful, but it does have its shortcomings.
Indirect bifaces were found to be clustered. Again, this is likely the result of the
excavation techniques. The same can be said for edge-modified flakes and pestles.
Indirect unifaces were found to be clustered as well. This is very odd as there were only
three unifaces recovered with the burials, and very few encountered during the data recovery
excavation. Analysis of the clustering revealed that all three unifaces were located with two
burials in close proximity to one another. This too can likely be explained as differing excavation
techniques for the earlier burials.
Another explanation regarding the clustering of indirect tools is that they could also
possibly represent activity areas onsite. Certain areas of the site, in prehistoric times, could
have been devoted to different activities such as habitation, tool production, food preparation,
cooking, basket production, tanning of hides, etc. Tools specifically related to project specific
tasks could be located within the soil matrix within specific areas of the site, and were exposed
during excavation of the burials in these areas.
Spatial clustering based on wealth items holds a great potential into the examination of
status and rank within the site. Both shell beads and pendants could be seen as an indication of
rank or even social clans, and being buried in a cluster could denote a specified area reserved
for high ranking members of society. Examination of both attributes found that they were
clustered based on two individuals with a large number and assortment of burial goods in close
proximity to one another. Both burials date to 2200 BP. However, there are a large number of
contemporaneous burials nearby. It is also worth noting that these two individuals seem to be
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at the center of the westernmost cluster based on depth. This suggests that these two
individuals were of higher rank than those burials surrounding them.
Indirectly associated natural obsidian needles were also found to be clustered. A
possible explanation for this is that naturally occurring needles are found in the soil matrix. The
site is located on a floodplain located downriver from Napa Glass Mountain. Erosion of obsidian
deposits could have resulted in deposition of these objects into the soil, which was then
revealed during excavation of the burials. This could happen to any of the burials with one or
two of the natural obsidian needles.
Pathologies and Anomalies
There was no clustering with any of the pathologies and anomalies entered into the
attribute data. The dental caries were found to be evenly dispersed across the site, with less
than 5% chance of this being random. This result is still likely random, as it seems highly unlikely
for the prehistoric inhabitants to space out all the burials based on dental caries.

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Chapter 6 – Cluster Analysis
This chapter presents the results of the cluster analysis. It also discusses the results and
the implications on prehistoric society.
6.1 Cluster Analysis Results
The Anselin Local Moran’s I clustering was run in ArcGIS using inverse distance squared,
with the Euclidian distance, and a ROW for standardization. The results of running the Anselin
Local Moran’s I clustering statistic for the demographic attributes are presented in Table 19.
This identifies burials that are part of a cluster of higher values (HH), or lower values (HL). It also
indicates which burials were outliers. Burials could be considered outliers with either high
values surrounded by low values (LH) or low values surrounded by high values (LL).
The use of nominal values to represent specific data presents a few problems for the
cluster analysis. These data sets are not applicable to this type of study as they represent
discrete values and are not numeric values. For this reason, those attributes that consisted of
nominal data were removed from this particular analysis.
It is also worth noting that this particular analysis technique does not identify clusters of
middle range values. The clusters reported within this chapter are different than those from the
previous chapter. The clusters within this chapter focus on high and low values for the data. A
more comprehensive grouping analysis that incorporates all values is used in the next chapter.
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Table 19: Individual Burial Cluster Analysis Summary.
Cluster Analysis HH Burials HL Burials LH Burials LL Burials
Depth in meters
13, 14, 15, 25, 26, 30,
31, 39, 60, 64, 66, 69,
83, 144, 145, 155
131 32
5, 6, 9, 18, 21, 100,
101, 102, 113, 114,
115, 116, 124, 126,
127, 132, 133, 134,
135, 138, 154,
Total Wealth
Items
73, 79 32, 56 None None
Total Tools
6, 7, 8, 9, 10, 17, 94,
136, 158
16 5,12 None
Total Artifacts None 24,32 None None
Total Artifacts
Minus Debitage
and Faunal
Remains
73, 79 32, 56 151 None
Tool Diversity
Index
3, 6, 7, 8, 9, 10, 11,
17, 158
22, 32, 40, 143
5, 12, 23, 33, 73,
138
91, 124
Wealth Diversity
Index
7, 8, 20, 33, 51, 73,
79, 94, 100, 121, 129
84, 141 18, 76, 101 None
For the sake of brevity, only the seven artifact types that had evidence for clustering in
the autocorrelation analysis were subjected to cluster analysis. These are presented in Table 20.
Table 20: Cluster Analysis of Artifacts Summary.
Cluster Analysis HH Burials HL Burials LH Burials LL Burials
Indirect Bifaces
6, 7, 8, 9, 10, 17,
94, 133, 158
16 5, 11, 12 None
Indirect Edge-
Modified Flakes
3, 6, 7, 8, 9, 10, 17,
19, 35
16, 32, 68 None None
Indirect Unifaces 7, 8 None None None
Indirect Pestles 10, 17, 158 104 None None
Direct Shell Beads 73, 79 None None None
Indirect Natural
Obsidian Needles
5, 7, 8, 12, 33, 73,
79, 100
1, 68, 105 None None
Direct Pendants 73, 79 143 None None
6.2 Cluster Analysis Discussion
The results of the cluster and outlier analysis based on certain attributes allowed for the
advancement of studies in other areas regarding the burials. Determining which individuals
were parts of clusters, and which were outliers, allows for archaeologists to ask more pertinent
questions regarding these burials.
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It is also worth noting that even though the spatial autocorrelation results showed that
there was no clustering in some cases, running the cluster and outlier analysis can result in
clustered burials. This is because that while a few burials may show signs of clustering; there
are not enough clustered burials to be considered statistically significant. Further, there could
be a cluster consisting of one burial as the surrounding burials had higher values, but not
enough to be considered statistically significant.
Another aspect to consider is that while there is clustering in two dimensions, the
clustering seen may not be present among cotemporaneous burials in the same date range.
This could be accounted for by random happenstance, or it could be an indication that an oral
narrative detailing the burials was maintained by the inhabitants of the site which dictated the
placement of certain individuals with certain attributes.

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Depth in meters
The cluster and outlier analysis based on depth in meters is presented in Figure 30. It
showed that there were 16 burials (13, 14, 15, 25, 26, 30, 31, 39, 60, 64, 66, 69, 83, 144, 145,
155) that had high scores surrounded by high scores, one burial (131) that had a high value
surrounded by burials with small values, one burial (32) that had a low value surrounded by high
values, and 21 burials (5, 6, 9, 18, 21, 100, 101, 102, 113, 114, 115, 116, 124, 126, 127, 132, 133,
134, 135, 138, 154) that had low values surrounded by low values.

Figure 30: Map of Cluster Analysis of the Burials Based on Depth in Meters.
Depth is one of the more useful attributes to study for clustering. Clustering most likely
indicates areas that had numerous individuals interred at the depth. In this case, high values
surrounded by high values indicate burials with a deeper depth. Low values surrounded by low
values indicate burials with a higher depth and later internment date. It is possible that burials
that cluster at the same depth may be from the same date range. This is examined further in
Chapter 7 where the burials are examined in groups by depth.
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Outliers based on depth could indicate burials that are from different date ranges or
time periods onsite. A high value surrounded by low values indicates a deeper burial with
numerous other burials above it, suggesting greater antiquity. A low value surrounded by high
values indicates a burial higher in the soil profile than the deeper burials beneath it.
At CA-NAP-399 there are three discrete, discernible to the naked eye, clusters of burials
based on depth. Burials 39, 50, 60, 64, 66, 67, and 69 appear to be a cluster of burials at the
eastern edge of the burial area from around 2450 BP based on the radiocarbon date from Burial
67. Burials 13, 15, 25, 26, 30, 31, 144, 145, and 155 appear to be a cluster of burials near the
center of the burial area from around 2380 BP based on the radiocarbon date from Burial 15.
Burials 3, 5, 6, 7, 8, 9, 10, 18, 19, 20, 21, 33, 93, 100, 101, 102, 113, 114, 115, 116, 118, 123, 124,
126, 127, 132, 133, 134, 135, 138, 153, 154 appear to be a cluster of burials from the western
edge of the burial area dating from between 2200 BP and 2130 BP. This is based on the four
radiocarbon dates of 2200 BP from Burials 73, 79, 115, and 116 along with the date of 2130 BP
from Burial 10.
For further discussion regarding depth, please refer to Chapter 7 where another analysis
technique similar to clustering, grouping, is used. This technique avoids the high/low clustering
seen within this chapter. This chapter also gives a greater insight into the vertical distribution of
the burials across the site, coupled with the radiocarbon dates.

105
Total Wealth Items
The cluster and outlier analysis based on the total number of wealth items is presented
in Figure 31. It showed that there were two burials (73, 79) with high values surrounded by high
values and two burials (32, 56) with high values surrounded by low values.

Figure 31: Map of Cluster Analysis of the Burials Based on Total Wealth Items.
A cluster of high values surrounded by high values would indicate an area where the
individuals were buried with a large number of wealth items. This could indicate an area
dedicated to higher status individuals. A high value surrounded by low values indicates an
individual with a large number of wealth items surrounded by individuals with less. A low value
surrounded by high values would indicate a person with few wealth items being buried near
individuals with large numbers of wealth items. A cluster of low values surrounded by low
values would indicate an area where individuals had very few wealth items. This could indicate
an area where people of a lower status or rank were buried. Results would suggest a high status
area; however one of these burials was a sub-adult who did not have a chance to achieve status.
106
Total Tools
The cluster and outlier analysis based on the total number of tools is presented in Figure
32. It showed that there were nine burials (6, 7, 8, 9, 10, 17, 94, 136, 158) with high values
surrounded by high values, one burial (16) with a high value surrounded by low values, and two
burials (5, 12) with low values surrounded by high values.

Figure 32: Map of Cluster Analysis of the Burials Based on Total Tools.
A cluster of high values surrounded by high values could indicate an area where
individuals were interred with large number of tools. It could also indicate a possible activity
area onsite where a large number of tools were left behind, then mixed into the burial matrix
during the burial.
It is likely the clustering seen here is the result of excavation techniques, where excess
midden soils containing a high number of tools were excavated around a number of the initial
burials.
107
Total Artifacts
The cluster and outlier analysis based on total artifacts is presented in Figure 33. It
showed two burials (32, 56) where there were high values surrounded by low values.

Figure 33: Map of Cluster Analysis of the Burials Based on Total Artifacts.
Clustering based on similar artifact totals would indicate that there was a controlled
effort to bury the individuals with similar numbers of artifacts. The high values surrounded by
low values show there are outlier burials with a high number of artifacts surrounded by
individuals with fewer total artifacts.
Excavation techniques may also explain the higher artifact totals. Burial 32 in the
northeast corner of the burial area required excavation through 1/16” wire mesh in order to
recover the small glass trade beads. This likely recovered a number of smaller flakes as well
from the burial matrix that may not have necessarily been directly associated.
108
Total Artifacts minus Debitage and Faunal Remains
The cluster and outlier analysis based on total artifacts minus debitage and faunal
remains is presented in Figure 34. It showed two burials (73, 79) that had high values
surrounded by high values, two burials (32, 56) with high values surrounded by low values, and
one burial (151) with a low value surrounded by high values.

Figure 34: Map of Cluster Analysis of the Burials Based on Total Artifacts Minus Debitage and Faunal.
Clustering based on similar artifact totals would indicate that there was a controlled
effort to bury the individuals with similar numbers of artifacts. It would be difficult for this to
occur naturally. This attribute singled out four out of the six burials with the highest amount of
wealth items. This attribute strongly mimics the total wealth items attribute.
By eliminating the debitage and faunal remains, the total number of artifacts is greatly
reduced. This results in eliminating some of the background noise from the midden, and allows
for a more even examination of artifacts that might have more significance.
109
Tool Diversity Index
The cluster and outlier analysis based on tool diversity index is presented in Figure 35. It
showed nine burials (3, 6, 7, 8, 9,10, 11, 17, 158) that had high values surrounded by high
values, four burials (22, 32, 40, 143) with high values surrounded by low values, six burials (5, 12,
23, 33, 73, 138) with low values surrounded by high values, and two burials (91, 124) with low
values surrounded by low values.

Figure 35: Map of Cluster Analysis of the Burials Based on Tool Diversity Index.
Clustering based on the tool diversity index mimics the total tools attribute. This is not
surprising as the tool diversity is based on that attribute to a certain extent. Clustering can likely
explained by excavation techniques. Another possible explanation is the presence of artifact
specific activity areas onsite that may have mixed a different number of tools into the soil matrix
which was then incorporated into the burial fill unintentionally.
110
Wealth Diversity Index
The cluster and outlier analysis based on wealth diversity index is presented in Figure
36. It showed 11 burials (7, 8, 20, 33, 51, 73, 79, 94, 100, 121, 129) with high values surrounded
by high values, two burials (84, 141) with high values surrounded by low values, and three
burials (18, 76, 101) with low values surrounded by high values. This would seem to suggest
that there are possibly two areas where individuals with a diverse array of wealth items that
may reflect rank were interred.

Figure 36: Map of Cluster Analysis of the Burials Based on Wealth Diversity Index.
Clustering based on the wealth diversity index shows individuals with similarly diverse
wealth assemblages were clustered together. Burials 73 and 79 had two of the highest wealth
diversity index readings, and are located near to one another. This suggests the possibility that
portions of the burial area were reserved for individuals with a more diverse assemblage of
wealth items.
111
Indirect Bifaces
The cluster and outlier analysis based on indirectly associated bifaces is presented in
Figure 37. It showed nine burials (6, 7, 8, 9, 10, 17, 94, 133, 158) with high values surrounded by
high values, one burial (16) with a high value surrounded by low values, and three burials (5, 11,
12) with low values surrounded by high values.

Figure 37: Map of Cluster Analysis of the Burials Based on Indirectly Associated Bifaces.
Clustering based on indirect bifaces can be explained in a variety of ways. Given the
massive biface production that was occurring onsite for trade, it seems likely that bifaces from
the midden became intermixed with burial fill. Another possible explanation is the differing
excavation techniques, however this would not account for four of the burials from the high
values surrounded by high values cluster.

112
Indirect Edge-Modified Flakes
The cluster and outlier analysis based on indirectly associated edge-modified flakes is
presented in Figure 38. It showed nine burials (3, 6, 7, 8, 9, 10, 17, 19, 35) with high values
surrounded by high values and three burials (16, 32, 68) with high values surrounded by low
values.

Figure 38: Map of Cluster Analysis of the Burials Based on Indirectly Associated Edge-Modified Flakes.
Much like indirectly associated bifaces, the indirectly associated edge-modified flakes
can likely be explained by excavation techniques. It is also possible that given the large amount
of debitage onsite, that flakes demonstrating trampling could have been interpreted as
intentionally edge-modified. Another possibility is that the cluster of high values could have
been in an activity area where edge-modified flakes were in use, which then became intermixed
with the burial matrix.
113
Indirect Unifaces
The cluster and outlier analysis based on indirectly associated unifaces is presented in
Figure 39. It showed two burials (7, 8) with high values surrounded by high values.

Figure 39: Map of Cluster Analysis of the Burials Based on Indirectly Associated Unifaces.
This clustering is likely the result of excavation techniques and the very low number of
unifaces being recovered. There were a low number of unifaces recovered from the excavation
units. It seems that two or three unifaces popping up in the burial matrix would be random in
this case, and again, a direct result of the differing excavation techniques for the burials
employed early on in the excavation. An activity area that utilized unifaces may be another
explanation.

114
Indirect Pestles
The cluster and outlier analysis based on indirectly associated pestles is presented in
Figure 40. It showed three burials (10, 17, 158) with high values surrounded by high values and
one burial (104) with a high value surrounded by low values.

Figure 40: Map of Cluster Analysis of the Burials Based on Indirectly Associated Pestles.
Clustering based on indirectly associated pestles may also be based on excavation
techniques. However, most of these burials were found after the change to stratigraphic
excavation so this seems unlikely. Pestles were relatively rare from the excavation units. This
could denote a possible activity area onsite, with the pestles becoming intermixed with the
burial matrix. Yet the pestles are fairly large so this too seems unlikely. It could also mean that
these indirectly associated pestles were directly associated with these burials, and their
proximity shifted post deposition.
115
Direct Shell Beads
The cluster and outlier analysis based on directly associated shell beads is presented in
Figure 41. It showed two burials (73, 79) with high values surrounded by high values.

Figure 41: Map of Cluster Analysis of the Burials Based on Directly Associated Shell Beads.
Clustering based on directly associated shell beads can be a good indicator of areas
devoted to wealth or possibly status. This suggests the presence of a small wealth area. This
same area appears in the wealth diversity index analysis from earlier. It is odd how the other
burials in the area which only had one or two beads did not seem to form a cluster of low
values.

116
Indirect Natural Obsidian Needles
The cluster and outlier analysis based on indirectly associated natural obsidian needles
is presented in Figure 42. It showed eight burials (5, 7, 8, 12, 33, 73, 79, 100) with high values
surrounded by high values and three burials (1, 68, 105) with high values surrounded by low
values.

Figure 42: Map of Cluster Analysis of the Burials Based on Indirectly Associated Natural Obsidian Needles.
Clustering based on indirectly associated natural obsidian needles can likely be
explained as a natural phenomena occurring in the soil. If there was clustering of obsidian
needles, it may denote a specific area indicative of ceremonial dance regalia. Only one burial
had a large number of needles. The remaining burials likely obtained their needles as a result of
the natural obsidian needles that occurred naturally in the soil matrix next to the Napa River. It
could also be partially explained by excavation techniques as well.
117
Direct Pendants
The cluster and outlier analysis based on directly associated pendants is presented in
Figure 43. It showed two burials (73, 79) with high values surrounded by high values with one
burial (143) with a high value surrounded by low values.

Figure 43: Map of Cluster Analysis of the Burials Based on Directly Associated Pendants.
Clustering based on directly associated pendants can be an indication of areas devoted
to the internment of individuals based on wealth, status, or even clan moieties. In this case, it is
the same two burials that keep appearing for many other various wealth variables. The
pendants are not shaped like some other examples from the San Jose area which are attributed
to certain religious movements later in time.

118
Chapter 7 - Grouping Analysis
This chapter uses a spatial analysis technique similar to that used by Bellifemine (1997).
For a brief description as to how grouping analysis works, please refer to Section 3.2.3. Depth
was singled out for a more in depth examination in the analysis. This is to examine the possible
dates of the undated burials and determine how the burial area was formed.
The grouping analysis was run using the K nearest neighbors with a value of eight for
determining spatial constraint. A preliminary analysis, using 15 groups that generated a report
was run on all the attributes in order to determine the best number of groups to use. The
analysis report also gives the value for 𝑟 2
(the coefficient of determination) where the closer to
the value of one, the more explanatory the variable. Ideally, the number of groups would be
lower. Burials with attributes that were unknown were eliminated from that particular analysis,
as the burials with unknown or null values was found to be disruptive for the grouping analysis.
The tables in this section summarize the results by listed grouping class number (GC)
and:
• the number of data points within the group;
• the mean value for the group;
• the standard deviation within the group;
• the minimum value for the group;
• the maximum value of the group
• the share value (the ratio of the group and global range);
The radiocarbon dates of any burials within the group are included to see if there is
temporal clustering.
119
Maps showing the distribution of burials in each group analysis (provided substantial
clustering was observed) are provided. This technique examines groups with similar values in
spatial proximity. The grouping analysis also allows for using multiple attributes to determine if
there are distinct groups based on combinations of these attributes. A preliminary examination
of common combinations involving age, sex, orientation, and flexure produced no distinct
groups. Therefore, this chapter focuses on groups within each possible attribute.
The results of the grouping analysis are summarized below in Table 21. Depth was run
twice, once to see the optimum number of groups, and the second to determine if smaller
groupings of burials represented a tight, cohesive date range.
Table 21: Burial Attributes Grouping Analysis Summary.
Attribute Number of Groups F-Statistic 𝒓 𝟐 Grouping?
Age 15 34.279 0.772 No
Sex 7 124.769 0.871 No
Flexure 15 113.663 0.932 No
Orientation 15 115.801 0.935 Possible
Side 7 110.063 0.841 No
Preservation 15 76.182 0.883 Yes
Depth in Meters 2 98.895 0.396 No
Depth in Meters 15 64.099 0.867 Yes
Artifact Association 15 73.236 0.878 Yes
Total Wealth Items 15 38454.602 0.999 No
Total Tools 3 67.989 0.476 No
Total Artifacts 15 1484.89 0.993 No
Total Artifacts
minus Debitage and
Faunal
15 502.074 0.980 No
Tool Diversity Index 2 46.629 0.227 No
Wealth Diversity
Index
15 129.377 0.927 Yes
120
7.1 Grouping Analysis Results
Age
When evaluating the age attribute, it was found that the best choice for number of
groups was 15 which had the highest F-Statistic score at 34.279. The coefficient of
determination was 0.772. It was found that too there were too many groups, with no
discernible grouping.
Sex
When evaluating the sex attribute, it was found that the best choice for number of
groups was 7 which had the highest F-Statistic score at 124.769. The coefficient of
determination was 0.871. It was found, that after eliminating the burials with an unknown sex,
the distribution of burials was not acceptable as there was numerous gaps in coverage for the
burials. The grouping analysis was conducted for the sake of completeness, but there was no
discernible grouping.
Flexure
When evaluating the flexure attribute, it was found that the best choice for number of
groups was 15 which had the highest F-Statistic score at 113.663. The coefficient of
determination was 0.932. Similar to sex, when the burials with an unknown flexure were
removed, the burial distribution was skewed and the whole population was not represented.
The grouping analysis was conducted for the sake of completeness, but did not observe any
grouping.
121
Orientation
When evaluating the orientation attribute, it was found that the best choice for number
of groups was 15 which had the highest F-Statistic score at 115.801. The coefficient of
determination was 0.935.
Examining the groups in Figure 44 we see one large group that runs across the entire
burial area (westerly orientation). There are a few small clusters of non-westerly oriented
individuals; however few are tight and discrete. Group 5 is a small clustering of three easterly
oriented individuals. Group 3 is slightly discrete, and consists of three easterly oriented
individuals. Group 11 would seem to be a group of three southerly oriented individuals;
however it is not very discrete. If the orientation of the burials with null values was known, it
could mean some slightly larger groups. It is possible there is grouping for orientation as there
were few non western oriented individuals buried on site, and a small number of them seem to
be in close proximity suggesting intentional internment, however it could also be random.

Figure 44: Map of Orientation Burial Group.
122
Side
When evaluating the side attribute, it was found that the best choice for number of
groups was 7 which had the highest F-Statistic score at 110.063. The coefficient of
determination was 0.841. As with sex and flexure, there were a number of burials with null
values that had to be removed from the study, skewing the results and decreasing the validity of
the results. The grouping analysis was run for the sake of completeness, and did not discover
discreet grouping.
Preservation
When evaluating the preservation attribute, it was found that the best choice for
number of groups was 15 which had the highest F-Statistic score at 76.182. The coefficient of
determination was 0.883.
Examining the groups in Figure 45 we see several larger groups. Group 1 along the
eastern edge of the burial area appears to be a large group of poorly preserved individuals. This
is contrasted with Group 10, a small discrete cluster of well preserved burials immediately
adjacent Group 1 to the west. The standard deviation is also on the lower side, with none of the
groups having a very large range of values. This could be a reflection of the coded values
representing a spectrum of preservation. There are several smaller groups of better preserved
individuals that are not discrete. These are broken up by several smaller groups of lesser
preserved individuals that are also not discrete. There does appear to be grouping based on
preservation. As stated before this is likely not a reflection of cultural actions, but rather of
natural environmental ones.
123

Figure 45: Map of Preservation Burial Groups.
Depth
The four burials without depth were eliminated from this particular analysis. When
evaluating the depth in meters attribute, it was found that the best choice for number of groups
was 2 which had the highest F-Statistic score at 98.895. The coefficient of determination was
low at 0.396. The grouping analysis divided the burials into an upper and lower half of burials at
around 140 cmbd. This was not considered grouping.
In order to determine if smaller groups of burials represent discrete internment events
with a tight date range, a grouping of 15 was chosen. It allowed for more differentiation across
the site, and also had the next highest and acceptable F-Statistic value at 64.099. The coefficient
of determination was 0.867. The results are summarized in Table 22 below. The values in this
case refer to centimeters below the site datum.
124
Table 22: Results of Grouping Analysis Examining Depth in Centimeters.
GC# n= mean
St.
Dev.
Min Max Share Burials
Radiocarbon
Dates (BP)
1 6 74.3 4.57 68 79 0.063 10, 132, 133, 134, 135, 138 2130
2 8 111.4 11.5 97 134 0.211
22, 110, 119, 121, 122, 129, 130,
139
2030, 2150
3 5 198.6 11.5 179 215 0.206 60, 64, 66, 69, 82 None
4 6 116.3 15.5 89 137 0.247 4, 57, 58, 146, 148, 156 2140, 2380
5 1 66.0 0.0 66 66 0.000 32 150
6 9 159.p 14.0 142 192 0.286 70, 71, 89, 90, 94, 96, 99, 108, 131 1990, 2230
7 1 235.0 0.0 235 235 0.000 85 None
8 3 183.3 9.0 177 196 0.109 150, 151, 157 None
9 12 132.3 12.8 108 158 0.286
11, 12, 34, 35, 51, 73, 76, 79, 123,
128, 136, 137
2200, 2200
10 9 86.6 13.3 60 100 0.229
18, 113, 114, 115, 116, 118, 124,
126, 127
2200, 2200
11 25 107.6 11.9 87 145 0.331
3, 5,6, 7, 8, 9, 17, 19, 20, 21, 33,
74, 77, 80, 84, 93, 95, 100, 101,
102, 103, 104, 107, 153, 154
None
12 5 129.2 5.5 122 134 0.069 36, 37, 38, 43, 149 2200
13 14 179.6 11.7 158 206 0.274
13, 15, 16, 25, 26, 30, 31, 78, 81,
140, 142, 144, 145, 155
2380
14 22 139.8 12.1 124 176 0.297
2, 23, 24, 27, 28, 29, 72, 75, 91,
92, 98, 105, 106, 109, 111, 112,
117, 120, 125, 141, 143, 152
2090, 2090,
2240, 2380
15 27 153.9 14.9 112 181 0.394
1, 14, 39, 40, 41, 44, 45, 46, 47,
48, 49, 50, 52, 53, 54, 55, 56, 59,
62, 63, 65, 67, 68, 83, 86, 87, 147
2030, 2290,
2430, 2450
Examining the groups in Figure 46 we see several discrete groups. The burial area was
also divided into six roughly identical sized areas to better examine the grouping in the soil
profile along a north/south axis.
Group 12 is a very tight and discrete group with a very low standard deviation,
suggesting internment around the same time. Examining Table 16 however, we see that there is
a good deal of variation in the radiocarbon dates by group, suggesting mixing and uneven
vertical distribution of burials throughout time. A few groups (9 and 10) did have a tighter date
range it seems. To determine if this is indeed the case, a three dimensional view of the burials
through the soil profile is examined further in Figures 47-53.
125

Figure 46: Depth Grouping Analysis.
Figure 47 below shows the depth grouping and radiocarbon dates from stratigraphic
profile along a west/east axis. This shows that some groups that are dispersed in plan view are
slightly more discrete when viewed in profile. Another thing to note is that the radiocarbon
dates appear to become younger towards the upper right (west), though there is still mixing.
126

Figure 1: Depth Grouping and Radiocarbon Dates from Stratigraphic Profile from West (at left) to East (at right).

127
Figure 48 shows the depth grouping and radiocarbon dates for Section 1 from the
stratigraphic profile along a north/south axis. The grouping does not appear to be that bad.
One burial from Group 12 does appear to be mixed in with Group 6. Some of Group 12 appears
as if they could also be part of Group 13 near 100 centimeters below site datum. Judging by the
radiocarbon dates, it would appear that most of these burials would be from later in the site
formation process. Another thing to note is that the uppermost burials of Group 11 seem to
form a gentle hill surface, suggesting that this was the original site surface at the time of
interment. The gap in the center of the burials is also odd, perhaps suggesting something
present onsite prevented internment in this area. This could have been a tree, an activity area,
or even a structure of some sort. It is only a few meters wide, which is the size of some
dwellings. It could also be completely random.

Figure 48: Section 1 of Depth Grouping Analysis.
128
Figure 49 shows the depth grouping and radiocarbon dates for Section 2 from the
stratigraphic profile along a north/south axis. The grouping is better than that seen in Section 1.
One burial from Group 12 is also included in this group, and should not be considered an outlier
as the grouping by sections was random. Group 9 looks acceptable but there is a larger range in
Groups 3 and 6.
Another thing to note is with the radiocarbon dates. The date of 2380 BP from Group 3
seems to be rather high in the profile. Also the latest date is the deepest in this section. It is
along the southern edge of the burial area however. This again gives credence to the possibility
that the original site was on a gently sloping hill or natural levee adjacent to the Napa River. If it
was on a floodplain, then the ancient inhabitants went out of their way to bury this particular
individual very deep for some reason.

Figure 49: Section 2 of Depth Grouping Analysis.
129
Figure 50 shows the depth grouping and radiocarbon dates for Section 3 from the
stratigraphic profile along a north/south axis. The grouping is not bad. The overall distribution
of the burials again resembles a small hill or levee, as the older date is higher towards the center
with the more recent dates towards the perimeter.
Examining the distribution of radiocarbon dates we again see that there is an older date
above the younger dates. This is located in almost the same position as the other older burial
from Section 2, suggesting a higher, linear site surface running west to east across the two,
perhaps the top of the levee. The younger radiocarbon dates along the perimeter of the burial
area would again suggest that there was a natural slope or else those individuals buried along
the periphery of the burial area were interred progressively deeper. It appears that Groups 3
and 5 would date to later in the site formation process, while Group 8 would be earlier.

Figure 50: Section 3 of Depth Grouping Analysis.
130
Figure 51 shows the depth grouping and radiocarbon dates for Section 4 from the
stratigraphic profile along a north/south axis. The grouping does not appear to be that good
upon initial inspection. Group 2 would appear to be very widespread and unconnected,
however that is merely the result of the arbitrary placement of the sections as the group
continues further to the east where they all connect. The same is true of Group 8, only it
continues further west.
Examining the radiocarbon dates we see a later date sandwiched between two older
dates. This would seem to confirm that groups do not exactly correspond to dates. The upper
radiocarbon date of 2380 BP from Group 4 is again in line with the older dates from Sections 2
and 3 that are higher in the stratigraphic profile. The lower radiocarbon date of 2380 BP from
Group 9 occurs between them suggesting a possible break or dip in the original site surface.

Figure 51: Section 4 of Depth Grouping Analysis.
131
Figure 52 shows the depth grouping and radiocarbon dates for Section 5 from the
stratigraphic profile along a north/south axis. The grouping appears to be very good except for
Group 15. Group 11 is very tightly grouped in both profiles and plan views, suggesting
cotemporaneous internment with one another. This would appear to be around 2200 BP given
the lone radiocarbon date from the group. Another possible explanation is the intentional
internment of familial relations in a small area, with descendants being buried next to their
ancestors. This assumes the location and depth that the ancestor was buried was known.
Examining the radiocarbon dates we see that the younger date is near the top and the
older date near the bottom which is what an archaeologist would hope for. It would appear the
Group 11 is likely from slightly before the other burials given its placement in the stratigraphic
profile. Group 10 might possibly predate Group 11.

Figure 52: Section 5 of Depth Grouping Analysis.
132
Figure 53 shows the depth grouping and radiocarbon dates for Section 6 from the
stratigraphic profile along a north/south axis. It is also worthwhile to note the dramatic shift to
the south of the burials in this area. This mimics the nearby bend in the Napa River, giving
additional evidence that the original site was situated on a small levee. It also demonstrates just
how much of an outlier Burial 32 was. Group 10 is small, but they cluster together well. The
arbitrary placement of the sections makes it seem as if they are outliers.
Examining the radiocarbon dates one sees a very large discrepancy in Group 12. There
is a span of almost 400 years in 20 cm. This would seem to indicate that there was either not
much soil accumulation along this edge of the burial area or that the individual from 2030 was
buried very deep.

Figure 53: Section 6 of Depth Grouping Analysis.

133
Artifact Association
When evaluating the artifact association attribute, it was found that the best choice for
number of groups was 15 which had the highest F-Statistic score at 73.236. The coefficient of
determination was 0.878. The results are summarized in Table 23. Refer back to Section 4.2 for
an explanation regarding the artifact association. A value of 0 denoted no artifacts, a value of 1
denoted indirectly associated artifacts, and a value of 2 denoted directly associated artifacts.
Table 23: Results of Grouping Analysis Examining Artifact Association.
GC# n= mean
St.
Dev.
Min Max Share Radiocarbon Dates (BP)
1 4 0.0 0.0 0.0 0.0 0.0 None
2 3 0.0 0.0 0.0 0.0 0.0 None
3 11 2.0 0.0 2.0 2.0 0.0 1990, 2200, 2200, 2200, 2230
4 3 0.0 0.0 0.0 0.0 0.0 None
5 3 0.0 0.0 0.0 0.0 0.0 None
6 3 0.0 0.0 0.0 0.0 0.0 2380
7 4 2.0 0.0 2.0 2.0 0.0 None
8 3 1.0 0.0 1.0 1.0 0.0 2380
9 2 0.0 0.0 0.0 0.0 0.0 None
10 2 2.0 0.0 2.0 2.0 0.0 None
11 6 2.0 0.0 2.0 2.0 0.0 2140, 2290
12 4 2.0 0.0 2.0 2.0 0.0 None
13 3 2.0 0.0 2.0 2.0 0.0 2090, 2090
14 2 0.0 0.0 0.0 0.0 0.0 None
15 104 1.048 0.255 0.0 2.0 1.0
150, 2030, 2030, 2150, 2200, 2200, 2380, 2430,
2450

Examining the groups in Figure 54 we see that there are a few small discrete groups. It
is interesting to note the Group 3 has five radiocarbon dates from the 11 members of the group,
all within a 50 year period. Group 11 also has a cluster of individuals with directly associated
burial goods; however there is a 150 year range in the radiocarbon dates. Group 15 is the
largest and consists mostly of individuals with indirectly associated artifacts and can be
considered background. There are several small groups (2, 6, 14) of individuals with no artifacts.
134
It is unlikely that this is meaningful or that they represent discrete areas for “poor” individuals
given their small size. Organic grave goods may have also decomposed, influencing this
attribute. There is grouping based on artifact association.

Figure 54: Map of Artifact Association Burial Groups.
Total Wealth Items
When evaluating the total wealth items attribute, it was found that the best choice for
number of groups was 15 which had the highest F-Statistic score at 38454.602. The coefficient
of determination was 0.9997. Total wealth items refer to both indirectly and directly associated
items. Given the very high F-Statistic score and coefficient of determination value, grouping was
expected. However, when the results are analyzed, it points out the wealth outliers, and
combines most of the individuals into one group with little to no wealth items. There is no
discernible grouping observed for total wealth items.
135
Total Tools
When evaluating the total tools attribute, it was found that the best choice for number
of groups was 3 which had the highest F-Statistic score at 74.513. The coefficient of
determination was 0.476. Total tools includes both indirectly and directly associated tools
found with the burials. One discrete group of burials was observed, but it was the same one
that has appeared continually throughout this analysis as the difference in excavation
techniques recovered more tools from the midden. There was no discernible grouping for total
tools.
Total Artifacts
When evaluating the total artifacts attribute, it was found that the best choice for
number of groups was 15 which had the highest F-Statistic score at 1484.89. The coefficient of
determination was 0.993. Total artifacts refer to both indirectly and directly associated items.
There was no discernible grouping for total artifacts.
Total Artifacts minus Debitage and Faunal
When evaluating the total artifacts minus debitage and faunal attribute, it was found
that the best choice for number of groups was 15 which had the highest F-Statistic score at
502.074. The coefficient of determination was 0.980. Similar to Total Artifacts there was no
discernible grouping observed.
Tool Diversity Index
When evaluating the tool diversity attribute, it was found that the best choice for
number of groups was 2 which had the highest F-Statistic score at 46.629. The coefficient of
determination was 0.227. There was no grouping based on the tool diversity index.
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Wealth Diversity Index
When evaluating the wealth diversity index attribute, it was found that the best choice
for number of groups was 15 which had the highest F-Statistic score at 129.377. The coefficient
of determination was 0.927. The results are summarized in Table 24. The tool diversity index is
a measurement of the number of different tools interred with a burial.
Table 24: Results of Grouping Analysis Examining Wealth Diversity Index.
GC# n= mean
St.
Dev.
Min Max Share Radiocarbon Dates (BP)
1 1 0.75 0.0 0.75 0.75 0.0 2200
2 1 0.375 0.0 0.375 0.375 0.0 2240
3 4 0.125 0.0 0.125 0.125 0.0 2140
4 6 0.271 0.047 0.25 0.375 0.167 2230
5 1 0.25 0.0 0.25 0.25 0.0 None
6 1 0.375 0.0 0.375 0.375 0.0 None
7 2 0.125 0.0 0.125 0.125 0.0 2030
8 1 0.375 0.0 0.375 0.375 0.0 2200
9 21 0.131 0.027 0.125 0.25 0.167 2130, 2200
10 2 0.125 0.0 0.125 0.125 0.0 None
11 2 0.125 0.0 0.125 0.125 0.0 None
12 106 0.007 0.029 0.0 0.125 0.167
150, 1990, 2090, 2090, 2200, 2200, 2380, 2380,
2380, 2430, 2450
13 1 0.375 0.0 0.375 0.375 0.0 None
14 6 0.188 0.063 0.125 0.25 0.167 2030, 2150
15 2 0.25 0.0 0.25 0.25 0.0 2290
Examining the groups in Figure 55 we see that there are a few discrete groups. Group
12 is the largest group that covers almost the entire burial area. It consists almost entirely of
individuals with little to no wealth items and can be considered background noise. Groups 3 and
14 appear to be fairly discrete. Both have individuals with only one or two types of wealth
items. Group 4 is more widespread but it has a higher standard deviation and a higher mean
value. There does appear to be grouping present based on the wealth diversity index.
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Figure 55: Map of Wealth Diversity Index Burial Group.
7.2 Grouping Analysis Discussion
This analysis found that grouping may not be useful in populations that have a few
outliers. This analysis was useful as a tool for spatial exploration of the data. It was observed
that with the more groups selected for the grouping analysis, there tended to be more
individual outliers. However if there were fewer groups in number, there tended to be a larger
standard deviation seen in the group as the outliers were consolidated into larger and larger
groups. The identification of outliers as individual groups was not expected.
Just because the analysis says there was a group, does not necessarily mean that one
existed. Often there is a standard deviation which means that the group is not uniform. The
mixing of nominal values should not occur in a discrete, uniform group. Further, many of the
groups do not have a tight date range which would be expected if the group represented a
single, discrete, burial period in time and space.
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Another thing to consider is at what point could a group be considered legitimate?
Certainly there is a bit of subjective reasoning involved. Are there a minimum number of
individuals in a group? Would two individuals be too little? Does one individual constitute a
group? Where is the cut-off point at which there is a group and not just a random collection of
individuals? Also, do the groups have to be tight and discrete, or can they be a little more
widespread? These are valid questions that could not be addressed by this incomplete dataset
whose excavation process means that there are unknown relationships between the direct and
indirectly associated artifacts in and around the burial itself. It is up to other archaeologists and
the GIS user to decide these things as they explore their own dataset.
In summary, grouping was observed for preservation, depth, artifact association, and
wealth diversity. Possible grouping was observed for orientation.

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Chapter 8 – Radiocarbon Date Interpolation
Of the 162 burials recovered from CA-NAP-399, 157 have data regarding their exact
location. These 157 form the bases of the spatial autocorrelation and cluster and outlier
analysis. One burial, 32, was a protohistoric outlier that was removed from the radiocarbon
date interpolation study as it skewed the data too much. That leaves 156 burials for this
particular study.
This chapter examines the changes to society, the burials, and their artifacts over time
by interpolating the dates of the burials from the known radiocarbon dates and depths of 21
samples. This will allow the grouping of burials into five 100 year date ranges. What is being
predicted is a surface representing the age of the burials based on the 21 radiocarbon samples.
When the radiocarbon date surface values are assigned to each burial point, this assumes that
all burials in a specific location have the same date, that there is no superposition occurring.
This is a large assumption to make, however this chapter is more of an experimental analysis to
see if any insight can be gained by exploring an interpolated date surface. A discussion of
different kriging types can be found in Appendix B, as well as the summarized results of this
particular study run through several different types of kriging.
8.1 Burial Radiocarbon Date Interpolation
A simple cokriging with prediction model was used with the 21 radiocarbon dates as the
primary data coupled with depth in meters as the secondary data. The model was auto-
optimized for increased accuracy. Depth was chosen as the secondary dataset because the
lower in the stratigraphic deposit the burial was, the older it should be. There is a correlation
between the depth of the burial and the radiocarbon age, as seen in Figure 56. Burial 32 was
140
omitted from this study as it greatly skewed the results. A possible explanation for this
correlation not being greater is that the original surface of the site at the time of internment had
irregular topography or was sloped. Some cotemporaneous burials may have been buried
deeper than others of the same time period which could also affect the results as well. This may
sound like a great deal of uncertainty; however the point of this experiment is a test of method
rather than an assertion that the data for this particular case study is relevant for cultural
comparative purposes.

Figure 56: Correlation of Radiocarbon Dates and Depth.
The geostatistical wizard from ArcGIS provides a series of prediction errors for cokriging
models. These vary slightly based on which particular type of cokriging is used (see Appendix B).
These prediction errors allow the user to judge how valid the interpolation model was. The root
mean square error indicates how closely the model predicts the measured values.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
1900 2000 2100 2200 2300 2400 2500
Depth in Meters Below Datum
Year Before Present
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The results of only running the 21 radiocarbon dates coupled with depth follow. The
root mean squared error was 121.522. A full range of values from the kriging exercise can be
found in Table B-1 in Appendix B. With this level of error, there is essentially a 250 year window
in which each burial falls. The prediction error values for the model suggest an acceptable
model; however there is still a wide range of error. It does mark an improvement over stating
the burials occur in a 500 year window. With a better dataset one could remove a single
radiocarbon date to see if the methods predicted it. It is also worth exploring the variability of
radiocarbon dates and depth in specific areas of the site to see if perhaps certain areas work
better than others.
Figure 57 shows the interpolated surface from the cokriging model using radiocarbon
dates with depth in meters. The older dates seem to be located in the eastern half of the burial
area, with most confined to the southeastern corner. The interpolated surface appears to be
younger towards the western half of the burial area, with a few older outliers underneath. This
would suggest that the burials were interred progressively further to the west over time, with
periodic revisits to older burial areas. These revisits could possibly represent familial internment
areas, clan areas, or moiety areas.
Figure 58 shows the prediction error surface. Given the sparse distribution of dated
locations, it is important to acknowledge that the error surface shows a distinctive pattern of
low error values around each sample point. This does point to the limited reliability of the
analysis, but it was felt that the potential opportunity for further insight based on the date
surface made it worth proceeding with this analysis.

142

Figure 57: Interpolated Dates from Radiocarbon Dates Only.
155

Figure 58: Prediction Standard Error Map for Interpolated Burial Date Surface.

156
To assign dates from the interpolated surface to individual burials the interpolation
result was converted to a raster file with a default cell output size of 0.113 meters. Next, the
raster value to point feature tool in ArcGIS was used to extract the interpolated date from the
raster for each burial point. This created a new attribute with the value from the raster, which
was named Int_Dates. This attribute allowed for the sorting of the burials into five date ranges
of one hundred years each. These values are presented in years before present (BP). This
allows for the examination of change over time presented and discussed in Section 8.3.
The results of the 156 burials that were suitable for the dating interpolation based on
radiocarbon dates and depth are listed in Table 25. There were 18 burials in the first date range
from 2450-2350 BP, 33 in the second date range from 2350-2250 BP, 70 in the third date range
from 2250-2150 BP, 31 in the fourth date range from 2150-2050 BP, and four in the fifth date
range from 2050-1950 BP.
Table 25: Burials by Interpolated Date Range.
Date Range (BP) Burials Total
2450-2350
13, 15, 31, 42, 66, 67, 68, 69, 82, 83, 86, 87, 125, 144, 145, 147, 155,
156
18
2350-2250
14, 16, 25, 26, 30, 40, 41, 44, 46, 47, 48, 49, 56, 60, 63, 64, 65, 78, 81,
105, 108, 120, 123, 124, 128, 131, 137, 143, 146, 150, 151, 157, 161
33
2250-2150
1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 17, 19, 20, 21, 23, 27, 33, 34, 35, 36, 37, 39,
43, 45, 50, 51, 53, 54, 58, 59, 62, 73, 76, 77, 79, 80, 85, 91, 92, 94, 95,
9798, 100, 103, 104, 106, 107, 109, 110,111, 112, 113, 114, 115, 116,
117, 118, 126, 127, 135, 136, 140, 141, 142, 149, 152, 153, 154, 158
70
2150-2050
2, 10, 18, 22, 24, 28, 29, 38, 52, 57, 71, 74, 75, 84, 89, 90, 93, 96, 99,
101, 102, 119, 121, 122, 129, 130, 132, 133, 134, 138, 148
31
2050-1950 55, 70, 72, 139 4
8.2 Comparison by Date Range from Radiocarbon Date Interpolation
Table 26 compares several attributes over the five one hundred year date ranges. The
number of burials peaks during the third date range from 2250-2150 BP with 70 individuals.
There are very few individuals buried later in time onsite from 2050-1950 BP.
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The ratio between the sexes remains fairly constant throughout all five date ranges with
there being slightly more females than males. In normal populations, the ratio should be closer
to 50:50. This is true for the second, fourth, and fifth date ranges. The first and third date
ranges show a greater discrepancy in this ratio. Brown (1981) might consider this evidence of
differential internment based on sex that varied through time onsite. A few possible
explanations for this discrepancy is that the ratio is actually closer to 50:50, only a
disproportionate percentage of males had poorer preservation which meant their sex could not
be positively identified, so they were classified as unknown. Another explanation is that the
some of the males died away from the village, and were unable to be transported back for
burial.
The ratio between ages also remains fairly constant across the date ranges. There are a
higher number of older individuals throughout all five date ranges. There are very few young
individuals being buried onsite. There is more variation in the middle date range, but that is
likely the result of the greater number of individuals present within the date range. This goes
against normal populations seen in nature, where the very young and elderly are more at risk.
At CA-NAP-399, it is possible that the very young did not die that often, and most adults
survived into middle adulthood before succumbing to death. Another possible explanation is
that the very young individuals from the site did not preserve well, or were missed entirely
during monitoring operations.
The flexure of the burials is predominantly tightly flexed through all five date ranges.
There is more variation in the middle date range from 2250-2150 BP which is again likely the
result of the greater number of individuals. The tight flexure may be related to the energy
expenditure theory, in that there would be additional effort or energy expenditure in excavating
146
a grave larger than was needed. The tightly flexed burials fit into the smallest graves that
require the smallest amount of soil removal.
The cremations are not clustered in any particular date range which could have
indicated a shift in burial practices. It seems more likely that these cremations are isolated
events that are person specific and are not reflections of society as a whole.
Orientation is predominantly westerly oriented throughout the five date ranges with
more variation in the second and third date ranges. This is again likely due to the larger number
of individuals.
The artifact distributions show a normal statistical distribution throughout the date
ranges. The majority of individuals have indirectly associated burial goods. Very few individuals
had no artifacts associated with them. There appears to be a slightly higher percentage of
individuals with no burial associated artifacts earlier in time. The percentage of individuals with
directly associated artifacts increases through time.
The side of internment is fairly consistent throughout time, with internment on the left
and right side being the most numerous. There is again more variation in the middle date range,
likely due to the large numbers of individuals.
147
Table 26: Comparison of Selected Attributes by Interpolated Date Range.
Burial Attribute
Date Range (BP)
2450-2350 2350-2250 2250-2150 2150-2050 2050-1950
Number of Burials 18 33 70 31 4
Sex
Male 2 (11%) 10 (30%) 21 (30%) 10 (32%) 1 (25%)
Female 7 (39%) 11 (33%) 34 (49%) 11 (36%) 2 (50%)
Unknown 9 (50%) 12 (37%) 15 (21%) 10 (32%) 1 (25%)
Age
0-3 1 (6%) 4 (12%) 6 (9%) 2 (6%) 0
3-12 0 2 (6%) 5 (7%) 4 (13%) 0
12-20 1 (6%) 0 2 (3%) 3 (10%) 0
20-30 2 (11%) 3 (9%) 10 (14%) 7 (23%) 1 (25%)
30-50 7 (39%) 12 (37%) 28 (40%) 12 (39%) 1 (25%)
50+ 3 (16%) 6 (18%) 15 (21%) 2 (6%) 2 (50%)
Unknown 4 (22%) 6 (18%) 4 (6%) 1 (3%) 0
Flexure
Loose 1 (6%) 1 (3%) 4 (6%) 1 (3%) 0
Flexed 1 (6%) 0 1 (1%) 2 (6%) 0
Semi 0 2 (6%) 8 (11%) 0 0
Tight 12 (67%) 23 (70%) 44 (63%) 23 (74%) 4 (100%)
Cremation 1 (6%) 0 2 (3%) 1 (3%) 0
Orientation
North 1 (6%) 1 (3%) 4 (6%) 2 (6%) 0
Northeast 0 2 (6%) 3 (4%) 1 (3%) 0
East 0 2 (6%) 2 (3%) 1 (3%) 1 (25%)
Southeast 0 1 (3%) 1 (1%) 0 0
South 0 2 (6%) 1 (1%) 0 1 (25%)
Southwest 4 (22%) 3 (9%) 5 (7%) 2 (6%) 1 (25%)
West 8 (44%) 9 (27%) 30 (43%) 17 (55%) 1 (25%)
Northwest 1 (6%) 5 (15%) 12 (17%) 2 (6%) 0
Artifact
Association
None 4 (22%) 10 (30%) 6 (9%) 1 (3%) 0
Indirect 12 (67%) 18 (55%) 46 (66%) 21 (68%) 2 (50%)
Direct 2 (11%) 5 (15%) 17 (25%) 9 (29%) 2 (50%)
Side
Dorsal 1 (6%) 3 (9%) 5 (7%) 3 (10%) 1 (25%)
Dorsal/Left 1 (6%) 2 (6%) 2 (3%) 1 (3%) 0
Dorsal/Right 1 (6%) 0 2 (3%) 0 0
Ventral 3 (16%) 0 4 (6%) 4 (13%) 1 (25%)
Ventral/Left 0 0 4 (6%) 0 0
Ventral/Right 0 0 2 (3%) 1 (3%) 0
Left 4 (22%) 11 (33%) 19 (27%) 11 (36%) 0
Right 5 (28%) 10 (30%) 20 (29%) 4 (13%) 1 (25%)
Sitting 0 0 0 1 (3%) 0
(%) denotes the percentage of the population during the date range with that attribute.
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Table 27 summarizes the results of the pathologies and anomalies found in the burials
by component from the interpolated burial component. There are a total of 112 cases of
anemia, 15 auditory exostoses, 41 dental caries, 33 individuals with healed fractures, 10
osteomyelitis, 82 cases of femurs demonstrating anterior-posterior flattening, and four Inca
Bone. Anemia, dental caries, osteomyelitis, healed fractures, and femurs demonstrating
anterior to posterior flattening are found in all five date ranges. Auditory exostoses and Inca
Bone are found in the middle three date ranges.
Table 27: Comparison of Pathologies and Anomalies by Interpolated Date Range.
Pathology or Anomaly
Date Range (BP)
Total
2450-2350 2350-2250 2250-2150 2150-2050 2050-1950
Anemia 14 (78%) 21 (64%) 52 (74%) 23 (74%) 2 (50%) 112
Auditory exostoses 0 7 (21%) 6 (9%) 2 (6%) 0 15
Dental Caries 4 (22%) 9 (27%) 19 (27%) 7 (21%) 2 (50%) 41
Healed Fractures 2 (11%) 5 (15%) 17 (24%) 8 (24%) 1 (25%) 33
Osteomyelitis 2 (11%) 2 (13%) 3 (4%) 2 (6%) 1 (25%) 10
Femurs A-P flattening 8 (44%) 16 (48%) 37 (53%) 19 (58%) 2 (50%) 82
Inca Bone 0 1 (3%) 2 (3%) 1 (3%) 0 4
(%) denotes the percentage of the population during the date range with that attribute.
Table 28 shows there are a total of 54 tools directly associated with the burials
according to the interpolated date range. A total of 27 individuals had directly associated tools.
The first date range from 2450-2350 BP had one individual, the second date range from 2350-
2250 BP had two, the third date range from 2250-2150 BP had 15 individuals, the fourth date
range from 2150-2050BP had seven, and the fifth date range from 2050-1950 BP had one
individual. Bifaces appear in all the date ranges, and account for 80% of all directly associated
tools. There were no directly associated bowl mortars, cores, core tools, drills,
millingslabs/metates, or unifaces.
149
Table 28: Comparison of Directly Associated Tools by Interpolated Date Range.
Tool
Date Range (BP)
Total
2450-2350 2350-2250 2250-2150 2150-2050 2050-1950
Bifaces 1 1 15 19 4 40
Bone Awls 0 0 4 1 0 5
Bone Pins 0 0 1 0 0 1
Bowl Mortars 0 0 0 0 0 0
Cores 0 0 0 0 0 0
Core Tools 0 0 0 0 0 0
Drills 0 0 0 0 0 0
Edge-Modified
Flakes
0 0 2 0 0 2
Handstones/Manos 0 1 1 0 0 2
Millingslabs/Metates 0 0 0 0 0 0
Pestles 0 0 1 1 0 2
Projectile Points 0 0 2 0 0 2
Unifaces 0 0 0 0 0 0
Total 1 2 26 21 4 54

Table 29 shows the total number of tools, including both direct and indirect association,
by interpolated burial component. A measure of the tools diversity is also included. A total of
121 individuals had tools. The first date range from 2450-2350 BP had 14 individuals with tools,
the second date range from 2350-2250 BP had 19 individuals, the third date range from 2250-
2150 BP had 7 individuals, the fourth date range from 2150-2050 BP had 27 individuals, and the
fifth date range from 2050-1950 BP had four individuals buried with tools. A total of 636 tools
were recovered from the burials. The number of total tools by date range consists of 39 for the
first, 90 in the second, 327 in the third, 168 in the fourth, and 11 in the fifth. The tool diversity
index shows a normal distribution for the maximum value across the five date ranges, peaking in
the third date range with a value of 0.38. The average number of tools increases from the first
date range over time, peaking in the fourth date range which had an average of 5.39 tools per
burial, slightly higher than the third date range at 4.614.
150
Table 29: Comparison of Total Tools by Interpolated Date Range.
Tool
Date Range (BP)
Total
2450-2350 2350-2250 2250-2150 2150-2050 2050-1950
Bifaces 33 75 270 152 10 540
Bone Awls 0 2 10 2 0 14
Bone Pins 0 0 4 0 0 4
Bowl Mortars 0 1 4 0 1 6
Cores 0 1 0 1 0 2
Core Tools 0 3 0 0 0 3
Drills 0 0 1 0 0 1
Edge-Modified
Flakes
6 5 23 7 0 41
Handstones / Manos 0 1 2 3 0 6
Millingslabs /
Metates
0 1 0 0 0 1
Pestles 0 0 6 2 0 8
Projectile Points 0 1 5 1 0 7
Unifaces 0 0 3 0 0 3
Total 39 90 327 168 11 636
Average Tools 2.17 2.72 4.64 5.39 2.75

Highest Tool Index 0.15 0.23 0.38 0.31 0.23
Median Tool Index 0.08 0.08 0.08 0.08 0.08
Mean Tool Index 0.07 0.075 0.103 0.103 0.135
Table 30 summarizes the directly associated wealth items by interpolated burial
component. Only a total of 12 individuals were buried with directly associated wealth items.
There were four individuals in the second date range from 2350-2250 BP, seven individuals in
the third date range from 2250-2150 BP, and one individual in the fourth date range from 2150-
2050 BP. One individual from the second date range was buried with 112 quartz crystals,
accounting for the vast majority of directly associated wealth items for that component. Five
other individuals, all from the third date range, had 30 or more items while the remainder of all
other burials had less than two wealth items total. There were a total of 422 directly associated
wealth items directly associated with the burials. There were no directly associated bird bone
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beads or whistles. Shell beads (n=182) and quartz crystals (n=173) were the most abundant
directly associated wealth items.
Table 30: Comparison of Directly Associated Wealth Items by Interpolated Date Range.
Wealth Item
Date Range (BP)
Total
2450-2350 2350-2250 2250-2150 2150-2050 2050-1950
Bird Bone Beads 0 0 0 0 0 0
Charmstones 0 1 3 1 0 5
Obsidian Needles 0 0 29 0 0 29
Pendants 0 1 3 0 0 4
Quartz Crystals 0 113 59 1 0 173
Shell Beads 0 0 182 0 0 182
Stone Beads 0 0 29 0 0 29
Whistles 0 0 0 0 0 0
Total 0 115 305 2 0 422
Table 31 summarizes the total number of wealth items by interpolated burial
component. A measure of wealth item diversity is included. A total of 54 individuals had wealth
items. There was one individual from the first date range from 2450-2350 BP, 11 in the second
date range from 2350-2250 BP, 28 in the third date range from 2250-2150 BP, 12 in the fourth
date range from 2150-2050 BP, and two in the fifth date range from 2050-1950 BP. A total of
538 wealth items were recovered with the burials. The first date range only had two wealth
items, two obsidian needles. The second date range had 134 wealth items while the third date
range had 348. The fourth date range had a total of 47 wealth items while the fifth date range
had two. The most abundant wealth items consist of shell beads (n=195), quartz crystals
(n=193), and obsidian needles (n=52). The average number of wealth items peaks in the second
date range with 5.52 wealth items per burial. The wealth diversity index shows a normal
distribution with the highest value of 0.75 found in the third date range.
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Table 31: Comparison of Total Wealth Items by Interpolated Date Range.
Wealth Item
Date Range (BP)
Total
2450-2350 2350-2250 2250-2150 2150-2050 2050-1950
Bird Bone Beads 0 3 2 25 0 30
Charmstones 0 3 13 2 0 18
Obsidian Needles 2 2 44 3 1 52
Pendants 0 1 5 0 1 7
Quartz Crystals 0 113 62 13 0 193
Shell Beads 0 1 190 4 0 195
Stone Beads 0 11 29 0 0 40
Whistles 0 0 3 0 0 3
Total 2 134 348 47 2 538
Average Wealth 0.118 4.06 5.12 1.52 0.5

Highest Wealth
Diversity Index
0.125 0.25 0.75 0.375 0.125
Median Wealth
Diversity Index
0.0 0.0 0.0 0.0 0.0
Mean Wealth
Diversity Index
0.007 0.059 0.085 0.073 0.063
8.3 Changes in Attributes over Time
This section provides a tentative view of changes over time for various attributes
through time. It would be interesting to see if these changes apply only to the site or can
perhaps be applied to other nearby sites on a regional analysis.
Sex
The ratio of the sex between individuals at CA-NAP-399 remains fairly consistent across
all five arbitrary date ranges. There are slightly more females in all the date ranges. There are a
few possible explanations for this.
The first is that the ratio is actually close to even; only a disproportionate number of
males are located in the unknown sex class. The second is that perhaps some males from every
generation tended to die away from the village, perhaps due to accidents or infections, where
153
the body could not be returned to the village. The third possibility is that perhaps there were
just a higher number of women each generation buried onsite.
Age
The age ratio of the individuals remains fairly constant across the date ranges. There
are mostly adults and elderly individual’ interred onsite, with few children. In a normal
biological model, the distribution of death should be bimodal with peaks among the young and
elderly, who tend to be more vulnerable to nature.
The highest number of young and elderly individuals who succumbed to death is found
in the third date range from 2250-2150 BP, which had more individuals than the other date
ranges. This could represent the apex of the village during this date range. There appears to be
a drop-off in the number of individuals after this date range.
This could represent the inhabitants moving to another village nearby, and that those
interred onsite afterwards held a connection to the site and/or their ancestors buried there.
The number of young children drops off substantially which could also represent families
moving to the other village.
Flexure
The ratio of flexure between burials remains fairly consistent across all date ranges.
Tightly flexed is the most common method, but there is more variation when there are a higher
number of burials during a date range. The most likely explanation is the effort expenditure for
digging the graves. It appears as if the graves were excavated to be just big enough, to move
the least amount of material, to fit the individual into the grave.
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Orientation
The orientation of the burials is primarily to the west throughout all the date ranges.
This is likely in relation to the setting sun, a common theme in prehistoric societies thought to
symbolize the afterlife. There appears to be more variation during the second date range for
some unknown reason. The third date range also had a large amount of variation, but this is
likely explained by the larger number of individuals.
Variations in orientation may be due to post depositional shifts in the body or changes
in beliefs or references to sacred locations over time. The higher variation seen in the second
date range may reflect this.
Artifact Association
The percentage of individuals who had no associated grave goods peaked during the
first second range and decreased afterwards. This may indicate poorer conditions occurred
earlier at the site.
The percentage of individuals with directly associated grave goods starts low, at around
10% during the first date range from 2450-2350 BP. This increases over time to just under 30%
for the fourth date range. The low percentage of directly associated grave goods during the first
date range may also reflect poorer conditions onsite at this time.
Side
Given the somewhat random nature of the side of internment, it is difficult to make
definitive statements regarding the implications of its change over time. The left and right side
seem to be the preferred side of internment. Once again, the highest diversity is seen during
the middle date range from 2250-2150 BP, likely the result of a larger population.
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Tools
Bifaces are the most common tool found with the burials. They account for 80% of
directly associated grave tools and 85% of all tools. The peak in directly associated bifaces is not
in the middle date range from 2250-2150 BP which had more individuals, but rather in the
fourth date range from 2150-2050 BP. The number of total bifaces peaks during the middle
date range. If the site was an important biface production area for trade, it could be expected
that certain craftsmen could have bifaces directly associated with them.
Bone awls are found in the middle three date ranges, and peak in the third. Most (80%)
of the directly associated bone awls were from burials in the middle date range, and one from
the fourth. This could indicate increased basket production since this distribution mimics the
number of edge-modified flakes which can also associated with basketry. Looking at the sex of
the individuals with bone awls however shows that most were male. Males are typically not
associated with basket production. This would seem to indicate the awls are related to some
other activity, possibly hide preparation.
Bone pins are found during the third date ranges. These may also give an indication into
possible basket production or clothing manufacture. Bone pins could even be considered
articles of adornment, and may represent certain hair styles in which pins were used to hold up
the hair.
Bowl mortars are used in conjunction with pestles and indicate a reliance on acorns. It
is possible that the increasing number of bowl mortars seen in the date ranges, coupled with the
decrease in the number of millingslabs and handstones, could give an indication of increased
reliance on acorns. It is also hypothesized that this increased reliance on acorns could affect the
health of the individuals, leading to higher numbers of dental caries and even anemia if the
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tannins are not leached correctly or thoroughly enough. This is examined further in the health
and pathologies subsection below.
Cores are rare in the burial assemblage. This is likely due to these being little used
during the occupation of the site when the burials were interred. Cores are typically found later
in time in the area, when the demand for finished tools manufactured from smaller pieces of
debitage or flakes was higher. During the Upper Archaic, the production of large bifaces seems
to have been from individual cobbles of obsidian and large reduction flakes, not cores (Holson et
al. 2013).
Core tools are like cores but they have been used as tools. A more functional term
would be choppers or even scrapers. The three tools in this case are likely choppers, and are
found in the second date range from 2350-2250 BP. This could represent a specific activity type
that occurred onsite earlier, and then discontinued, disappeared, or left no visible trace
afterward. Wood working is one possible explanation.
There was only one drill found with the burials in the middle date range from 2250-2150
BP. This artifact was not shaped, and was actually created from an odd nodule of obsidian with
a nice thumb hold for a right handed user. It is likely that this represents an exception, or even a
personal tool, rather than a stylized and shaped drill. Drills were not very common onsite during
this time.
Edge-modified flakes can be considered expedient tools used for cutting or scraping.
They are used with a variety of tasks such as food processing, basketry, and clothing
manufacture. There is a slight dip during the second date range from 2350-2250 BP, followed by
the peak in the third date range. These artifacts from the burials may be related to basketry.
However the idea of them as expedient tools seems to be at odds of them as grave goods, if one
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usually thinks of grave goods as containing the nicest things. This could mean that they were
inserted as tools for the deceased to use in the afterlife. However, given that there were only
two directly associated edge-modified flakes, a likelier answer would be that they are likely part
of the midden.
Handstones are used in conjunction with millingslabs to process grass seeds. Their
presence indicates that the inhabitants of the site continued to focus on grass seeds as a food
staple. Their temporal placement indicates that seed processing coexisted with acorn
processing.
Projectile points are used to hunt game. They are found in the second and third date
ranges, peaking during the third date range from 2250-2150 BP. This could indicate an
increased focus on hunting, but a more likely explanation is that there was simply the same
percentage of hunters in a larger group of people in the community at that time hunting. An
odd thing is that five of the projectile points were associated with women.
Unifaces are only found in the middle date range, and may represent task specific
activities such as scraping hides. This could also be a reflection of unifaces being present in the
midden onsite, or the presence of an activity area where unifaces were utilized.
Wealth Items
Bird bone beads are found in the second, third, and fourth date ranges. These are likely
made onsite from the remains of birds brought to the site as food. These could represent a
demand for beads, but no way of acquiring other types such as shell or stone, so they made
their own. This fits well with the fourth date range where there was a much higher number of
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bird bone beads, but little shell and no stone beads. They could also have been used as
adornment for baskets.
Charmstones are found in the second, third, and fourth date ranges, peaking in middle.
Given the unknown nature of the charmstones themselves, it is difficult to make definitive
conclusions regarding their changing number over time. It could be as simple as people just
liked them, or as complex as being physical representations of religious rights or representations
of abstract concepts.
The obsidian needles peak during the middle date range from 2250-2150 BP, but are
found in all the date ranges. The peak during the middle date range can be credited to one
individual who had approximately 27 needles. The remainder of the needles can likely be
attributed to occurring naturally in the soil matrix surrounding the burials, which would account
for the clustering of the indirectly associated needles.
The pendants are found in the second, third, and fifth date ranges, peaking in the
middle. These are likely indicator of rank, status, and/or wealth given that very few individuals
had these, and that they were clustered together.
Quartz crystals are found in the second, third, and fourth date ranges, peaking during
the second date range from 2350-2250 BP where one individual was buried with 112 crystals.
Another individual from the middle date range was buried with over 50. These are the only two
individuals who possessed a large number of these crystals, while the other individuals had only
a few. These may be indicators of status, rank, or possibly religious importance. These items
are usually found associated with burials assumed to be shamans or other types of important
religious figures. While there is no spatial clustering, it is interesting to see the decrease in
numbers over time which could indicate increasing scarcity or difficulty in obtaining via trade. It
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could also indicate a change in religious practices if the crystals are in fact ceremonial in nature.
These are possibly Lake County diamonds from the vicinity of Clear Lake.
Shell beads first appear during the second date range, peak in third, and drop of sharply
after that. These olivella beads were obtained from the ocean either via trade or direct
procurement. Either option involves dealing with neighboring tribes. Their sudden appearance,
could suggest that trade routes were established during this time period.
Whistles are only found in the middle date range, and could indicate items of rank.
Increasing complexity in ceremonies could also be a factor; however their appearance in only
one time component suggests a singular event.
Pathologies and Anomalies
Anemia is present during all the components. The number of cases peaks during the
middle date range from 2250-2150 BP; however, this actually represents a slight dip in the
percentage of the overall population of the date range at 73.5%. The peak is from the second
date range which is at 76.0% while other date ranges occur in the 65%-75% range. Due to the
low number of individuals from the last date range, the percentage dips to half. This is a very
high percentage of the population that is fairly consistent through time. It is unknown if this
could be the result of a genetic abnormality or another disease such as scurvy which can present
itself in the bones same way. Possibly not leeching the tannic acid in acorns is another
possibility.
Auditory exostoses is present during the second, third, and fourth date ranges, peaking
in number during the third. However 20% of the population during the second date range had
this condition in comparison with just under 10% of the third date range. This could represent
repeated forays by select individuals to either the ocean or San Francisco Bay. The increasing
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number over time could be a reflection of increased trade or direct procurement of ocean or
bay goods.
Dental caries are present during every date range. The number peaks in the third date
range at 19 cases, which represents 28.9% of the date range population. All the date ranges
have between 20% and 30% of individuals demonstrating dental caries, except for the fifth date
range which has a lower number of individuals. It was thought that increasing reliance on
acorns would lead to an increase in the number of dental caries, however, this does not appear
to be the case at CA-NAP-399.
Healed fractures are present in all the date ranges, peaking in the third with 22 cases
representing 26.5% of the population in this date range. This is a marked increase from the
previous date ranges where only around 12% of the population exhibited evidence of healed
fractures. This could perhaps indicate an increase in risky behavior during the third date range.
Most of the fracture types appear to be the result of accidents, suggesting this happened fairly
regularly to a portion of the population.
Osteomyelitis is found in all five date ranges. It appears to be randomly distributed and
is likely dependent on the individual.
Femurs with anterior to posterior flattening peak in number during the third date range.
There is a gradual increase in the percentage of the population that demonstrated femurs with
anterior to posterior flattening. The first date range had 41.2% of the population afflicted,
increasing to 48% during the second date range, and peaking in the third at just under 60%. This
then decreases to 44.4% in the fourth date range. This indicates an increased workload or
longer walking over difficult terrain. This again gives credence to the theory of establishing
trade routes, as more walking over difficult North Coast terrain would produce these results.
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The Inca bones only occur in the middle three date ranges. It does not appear that this
represents a genetic or familiar marker that would indicate familial cemetery plots. It could
indicate the influx of a small group of individuals with this trait into the village over time
however. Genetic testing may hold the key for future analysis.

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Chapter 9 – Conclusions
This chapter examines considerations for future mortuary analysis using GIS and
summarizes the findings using GIS from CA-NAP-399.
9.1 Considerations for Future Burial Analysis
There are a number of improvements to suggest regarding data capture of burials in the
future. The first is to recommend that excavation techniques attend to preserving the data that
on the basis of its recovery alone make some of these detailed studies possible. The second
(and this is more relevant for the study in this thesis) is that a GPS device be used to record the
positions of the body. This would reduce the chance for error, and also cut down on person
hours converting physical data to digital data. Any device that can gain sub-meter accuracy
would be ideal. Given the amount of time it takes to remove the soil around a burial, a GPS
device could easily be gently set on the burial to record points for hours on end if need be. It
would also potentially help avoid differing data values and locations.
While considering burials as point locations has been used often, there is a loss of
information regarding the overall presence of the burial. Instead of seeing articulated burials or
the shapes of grave outlines in close association to one another, all one sees is a point. Instead
of taking a single solitary point on the burial, a series of lines might be better at distinguishing
the position, orientation, and burial type. A line feature can be created with the first point on
the head, the second on the hips, the third on the knee, and the fourth on the feet. A second
line could start with the position of the hands, then move on to the elbows, and finally end at
the shoulders. The point on the head can even be buffered to provide the rough size of the
crania. This would create a sort of stick figure which would allow archaeologists to better gauge
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how the position, orientation, and burial type vary across the site. If we were to represent the
burials as polygons one wonders how different the results would be. Future studies on flexure
should include a range from fully extended to tightly flexed.
Depending upon the accuracy of the GPS device in use, a total station might be the best
option at recording the depth and location of the burials. A difference of even a few
centimeters regarding the depth may be enough to skew the data and suggest different
temporal placement. Archaeologists should remember to take plenty of readings regarding the
surface elevation to allow for better recreation of the original site topography. Multiple points
along the outline of the prepared grave if observable could be of use as well.
It would also be worthwhile to begin to incorporate DNA analysis into the spatial data as
well. While this is unlikely to occur in the United States due to the politically sensitive nature of
Native American burials, it could conceivably gain acceptance in Europe and elsewhere. By
integrating DNA analysis, archaeologists would be able to examine genetic relationships
between individuals. Intra site analysis would be the most useful aspect at first, but as the
database grows over time, it would allow for a greater examination of human migration and
relationships over time and space.
The number and type of burial attributes can reach overwhelming levels quickly
depending on the level of analysis and questions one wishes to pursue. For example the indirect
and directly associated bifaces could be refined further into the five stages of bifaces and
possible projectile points, each one with a direct, indirect, and total attribute. Selecting only
essential attributes for study can allow for a more generalized study of burials, but some of the
smaller pictures may end up getting lost. Projectile points too could be broken down into
temporally sensitive types.
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Shell beads are another example where the attributes could be split almost indefinitely.
Olivella shell bead types are useful as chronological indicators. In a site with numerous burials
that occupy a wider range of time with a more diverse variety of beads, the beads can be used
like the radiocarbon dates in this thesis to extract burials into particular temporal ranges. The
only problem is that there are over a dozen types or classes of olivella beads, each with
numerous subtypes or sub classes. There is at least fifty, which translates into at least 150
attributes for the olivella beads alone based on direct and indirect association along with a type
or subtype total. A whole separate shapefile devoted to just the bead types could be a possible
solution.
Incorporating health information regarding the individuals such as disease, signs of
interpersonal violence, and other abnormalities, present a technical challenge. A single
attribute column for each disease can be used, with a simple presence or absence value. This
was experimented within this paper with mixed results. However, this would greatly increase
the number of attributes for the burials, further stretching the GIS. A list of the top ten or
twenty most common afflictions may be the answer. An attribute for interpersonal violence as
a present or absent attribute would also be useful. Interpersonal violence has been singled out
recently as an indicator of inter-societal pressure, and presents a skewed view of the past
focusing on instances of violence. A second column to counter this would be recommended
called interpersonal compassion. This attribute would be a simple present or absent value. It
would be present if the individual showed signs of hardships that would have likely killed them
on their own, so the interpersonal compassion could be seen as keeping them alive. Examples
include amputation and sever cases of osteomyelitis.
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The uses of codes representing nominal classifications have been problematic during
this study. The very act of coding itself is almost like weighting the results. One naturally
presents the best or most desired attribute at the end. This larger value then tends to
overshadow all the other codes in terms of weighted totals and means. This does not mean that
the results are wrong; it is merely something to keep in mind for future studies.
GIS can be very useful in distinguishing between prehistoric burial components where
temporally diagnostic artifacts are present. Sorting through the burials based on the Olivella
bead typology or projectile points can allow archaeologists to better characterize components,
instead of examining potentially thousands of years worth of burials en masse. Creating
attributes for diagnostic projectile points and temporally diagnostic beads can help to
differentiate between burial components and allow for a finer grained spatial inspection of
those components and how the burials were interred onsite over time.
Perhaps we should be wary of conducting a study with such a fine focus. At which point
are the attributes seen the reflection of individual choices or that of society’s? What this means
is that at a certain point, at a certain level of focus, all the attributes and choices can be
considered individual and not societal.
GIS analysis represents a powerful tool for analytical study. However that tool is
wielded by the human mind which must understand and comprehend the reasons behind using
that tool. A novice user may use that tool to find a faulty assumption or biased results. Proper
cognitive reasoning, coupled with GIS, can provide a wealth of information when used properly.
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9.2 Site Summary
Given the incomplete nature of the dataset, only two solid conclusions can be reached.
The first is that there was clustering of bone preservation. The second is that the results of
analysis indicated differences in excavation techniques. The incomplete nature of the data set
means that the results are not valid for formal archaeological analysis and should not be relied
upon by others who may be seeking comparisons for their own arguments about ancient socio-
cultural patterns. The following section reports on results that are implied by the analysis of the
given data and are provided as a means of assessing the usefulness of spatial analysis, not as
conclusions about this specific set of archaeological data.
Based on the Binford-Saxe model, if the underlying dataset were reliable, then it would
appear that CA-NAP-399 during the Upper Archaic could be considered evidence of a stratified,
hierarchal, and complex society. Wealth and or status were in the hands of the few indicating a
stratified society. If the experimental date interpolation is true, then this society became
stratified quickly. One of the six individuals with a large number of wealth items was too young
to have achieved status, suggesting that wealth was concentrated in familial groups.
As the largest prehistoric cemetery discovered so far in Napa County, there are not
many nearby sites to compare with. The site is unique when compared to the other sites
discussed in Section 3.2.2. CA-NAP-399 does match with the wealth inequality at CA-SCL-128, a
mixture of middle and late period components, discussed by Cartier et al. (1993). CA-NAP-399
does not demonstrate the same type of determinants, such as age and sex, which determined
the location of burials from CA-SCL-38 as discussed by Bellifemine (1997). It is similar to Luby’s
(2004) examination of CA-ALA-328 where inequality was found earlier in the site structure.
Wiberg’s (2005) study is the closest site to CA-NAP-399. Wiberg found that the late period site
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had activity areas outside the burials areas which did not occur at CA-NAP-399 as numerous ash,
hearth, and rock features were found within the burial area. Sub adults were also located along
the periphery of the burial area, which did not occur at CA-NAP-399.
There were a large number of artifacts found with the burials. Examining the
distribution of direct versus indirect artifacts, it was found that wealth items were more likely to
be directly associated with burials. The low percentage of directly associated tools suggest that
many of the tools were present in the burial fill, likely becoming intermixed into the burial
matrix by the excavation of graves into midden soil.
In this study it has been shown that GIS can provide a useful tool for mortuary analysis.
It can be used to identify spatial clustering of artifacts or burials traits, as well as to interpolate
dates for the burials. Depositional events of the burials show that the cemetery gradually
moved to the west, with each subsequent date range focusing on a westward expansion of the
cemetery.
Spatial analysis using GIS has demonstrated that there is spatial autocorrelation among
burial attributes such as depth, bone preservation, direct artifact association, and total tools,
the tool diversity index, and the wealth diversity index. Spatial autocorrelation was also found
for indirectly associated bifaces, indirectly associated edge-modified flakes, indirectly associated
unifaces, indirectly associated pestles, directly associated shell beads, indirectly associated
natural obsidian needles, and directly associated pendants. Dental caries were found to be
dispersed. Most of these can likely be attributed to excavation techniques, natural phenomena,
or possible activity areas onsite where discarded tools became intermixed in the burial matrix.
The directly associated shell beads and pendants are likely indicative of a high status or wealth
area onsite.
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The spatial cluster and outlier analysis singled out burials that were part of clusters or
outliers. This will allow the osteologist to reexamine specific burials to ask questions regarding
the connections between certain burials. Three distinct clusters of burials were observed by
depth. Each cluster contained at least one radiocarbon date allowing for the relative dating of
the other burials within that cluster. A small cluster of burials with a high number of wealth
items was observed which correspond to the clustering of shell beads, pendants, and wealth
diversity index seen in the spatial autocorrelation study.
The grouping analysis examined the burials to determine if they were part of unique
groups. Grouping was observed for preservation, depth, artifact association, total tools, and
wealth diversity. Total wealth items and orientation had possible grouping. The other
attributes studied did not show signs of grouping. Examining if clusters of burials based on
depth date to the same time period provided mixed results.
The experimental burial date interpolation allowed for a more fine grained examination
of the burials through time. It has the potential to allow for mortuary studies based on
generations, not centuries or millennia. While not exact, it does suggest a few trends that might
be examined in depth in the future at other sites in the area. These include the sudden increase
of wealth, the co-occurrence of millingslab and mortar technology, the progression of femurs
demonstrating anterior to posterior flattening through time, and if other sites in the area show
similar distinct spatial clustering based on attributes.
GIS, used most simply as a spatial visualization tool, allows for the visual analysis of the
spatial distribution of burial attributes, artifact numbers, and health anomalies and pathologies.
By exploring spatial autocorrelation in the data, it can be tested if the data is clustered,
dispersed, or random. It will not tell you which points are parts of these spatial patterns
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however. Cluster and outlier analysis can be used to show spatial clusters of high or low values
and outliers, but it does not detect meaning in the spatial distribution of intermediate values.
Grouping analysis can be used to explore spatial groups within the dataset, but it is important to
recognize that not every resulting group will actually represent a true group of data. One must
examine the mean of each group and the coefficient of determination in order to determine the
distinctness of each group. An experiment with cokriging using radiocarbon dates and depth to
produce a date surface was undertaken in an attempt to assign unknown date values missing
from the full set of burials. In this case, the likely complex topography of the original site
surface, coupled with the variable depth at which individuals were buried, calls into question
the value of the date surface as a valid analytical result. However, despite all of the
shortcomings in the interpretative results reported here, this study has demonstrated that with
a reliable, methodologically excavated or scientifically sampled dataset; an archaeologist should
be able to enhance their interpretation in a particular prehistoric mortuary analysis using these
spatial analysis techniques.

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Glossary
Term Definition
ArcGIS A geographic information system developed by Esri for working with
maps and spatial information.
AMS Stands for accelerated mass spectrometry, used in radiocarbon dating,
which measures the actual C14 atoms and not their decay.
Aspect Aspect is termed by Fredrickson as a sequence of phases within a single
area or smaller geographic area.
Average Standard
Error
The average of the prediction standard errors.
Biface A piece of stone that has been flaked on two sides, may be a finished
tool or in the process of manufacture
Bird Bone Beads Small tubular beads fashioned from bird bones, usually ground along
the ends.
Burial The result of a series of ritualized practices performed in relation to
death (Fahlander and Oestigaard 2008)
Charmstone A shaped stone of varying design with no utilitarian purpose.
Cool Spot Statistically significant cluster of low values.
Core Any mass of stone that has had flakes removed from it for the purpose
of manufacturing those flakes into tools
Core Tool A core that has been used as a tool
Dart Points Medium to large projectile points on a small shaft propelled through the
air with an atlatl (throwing stick)
Debitage/Flakes Waste material from the manufacture of stone tools
Drill A piece of worked stone with a distinctive bit used to drill through
objects, may or may not have a handle.
Edge-Modified Flake A piece of debitage that has been intentionally retouched or
inadvertently retouched during use as an expedient tool
Egalitarian A society where all individuals are considered equal, typically one of the
more primitive manifestations of society.
Faunal Refers to non-human animal or shell remains
Feature A non-movable, human created, object. Examples include hearths,
house floors, and burials.
GIS Abbreviation for geographic information system or sciences, a
technology that is used to visualize, analyze, interpret, and understand
spatial data by analyzing trends, relationships, and patterns.
Ground stone Bowl mortars, pestles, milling slabs, and manos. Implements used in the
processing of plant materials.
Handstone/Mano A groundstone implement held in the hand, that crushes seeds between
the handstone and a metate in a back and forth grinding motion.
Hot Spot Statistically significant cluster of high values.

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Term Definition
Locality Locality is a geographical location which exhibits complete cultural
homogeneity at any given time (Fredrickson 1973). Milliken et al. (2007)
divide the Bay Area into 18 localities.
Mean Error The averaged difference between the measured and predicted values.
The lower the number, the better the result.
Midden Accumulation of manmade soils resulting from decomposing organic
material, ash, charcoal, faunal remains, and artifactual debris
Millingslab/Metate A large stone, usually with a slightly concave surface, that holds seeds as
they are ground using a handstone in a back and forth motion.
Natural Obsidian
Nodule
Long, skinny natural obsidian needles believed to be used in ceremonies
for their tinkling sound as they hit one another
Obsidian Hydration Obsidian is a naturally occurring volcanic glass. Each source possesses a
distinctive chemical signature allowing for sourcing. Obsidian also
absorbs water very slowly whenever a fresh surface is exposed. A thin
cross section of the obsidian artifact is removed, sanded down, and
fitted to a slide. An electron microscope then examines the size of the
hydration rind present along the edge. The thicker the rind, the older
the artifact.
Obsidian Needle Naturally occurring masses of obsidian, typically long, tabular, and thin.
These were used in ceremonies as “tinklers” or “bangles” for the sound
they would make as they banged into one another, usually attached to
dress garbs.
Olivella Bead A bead manufactured from the shell of the sea snail Olivella biplicata.
The shapes and types of these beads change over time and are used as
diagnostic chronological indicators.
Pattern Patterns are units of culture defined by distinct ceremonial beliefs,
economic modes, and technological adaptations common over a wide
area.
Pendant A pierced object fashioned from stone or shell, usually worn around the
neck on a string.
Period A time span determined by archaeologists to be chronologically distinct
based on observed cultural patterns seen in the archaeological record.
Phase Phases are the smallest units of related site components limited to
smaller geographic areas.
Projectile Point Any bifacially modified mass of stone with a distinctive hafting element
used in conjunction with spear, dart, and bow and arrow technology.
Pseudo F-Statistic A ratio reflecting within-group similarity and between-group differences
(ArcGIS 2012).
P-Value A probability score, the closer to zero, the more likely the even it not
the result of random distribution.
Raster A data structure representing a grid of pixels of uniform size, each with
a value.

172
Term Definition
Root Mean Square Indicates how closely the model predicts the measured values, the
smaller the error, the better the model.
Root Mean Square
Standardized Error
This value should be close to one if the prediction standard errors are
valid. If the root mean square standardized error is greater than one,
the model underestimates the variability in the predictions. If the root
mean square standardized error is less than one, the model is
overestimating the variability in the predictions.
ROW Standardization When row standardization is selected, each weight is divided by its row
sum (the sum of the weights of all neighboring features). Row
standardized weighting is often used with fixed distance neighborhoods
and almost always used for neighborhoods based on polygon contiguity.
This is to mitigate bias due to features having different numbers of
neighbors. Row standardization will scale all weights so they are
between 0 and 1, creating a relative, rather than absolute, weighting
scheme (ArcGIS 2012).
Shapefile A geospatial vector data format used with GIS software.
Spatial
Autocorrelation
The similarity between observations as a function of the distance
between them. This means that objects that are closer in space tend to
be more similar than objects further away.
Spatial Outlier An object that is beyond the expected spatial distribution of nearby
objects.
Uniface A mass of stone that has been flaked along one face.
Z-Score Measures of standard deviation.

173
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180
Appendix A: Burial Shapefile Attributes Table
Table A-1: Burial Shapefile Attributes.
Name
Extended
Name
Data Type NotNull Unique
Domain/Restrictions/Not
es
ObjectID Object ID Object ID NotNull Unique
Unique identifying number
for each object, numbers
are sequential with object
creation
SHAPE* Shape Geometry Point
BurialNumber
Burial
Number
Short
Integer
NotNull Unique
Unique identifying number
for each burial, numbers
are sequential with
discovery.
Age Age
Short
Integer
The age of the individual
(see Table 5 for codes).
Sex Sex
Short
Integer
The sex of the individual
(see Table 6 for codes).
Depth Depth
Short
Integer
In centimeters below the
main site datum.
Depth_in_m
Depth in
meters
Short
Integer
In meters below the main
site datum.
BurialGoods Burial Goods
Short
Integer
Discerns between those
burials with directly
associated artifacts (2),
unassociated (1), or no
artifacts (0).
Orientation Orientation Text
The direction the
individual was laid to rest
facing, in cardinal
direction terms.
OrientationDegrees
Orientation
Degrees
Short
Integer
The direction the
individual was laid to rest
facing, sighted in
compass degrees.
Position Position Text
The type of flexure for the
burial.
Side Side Text
Which side the individual
was laid to rest on.
Preservation Preservation Text
Described integrity of the
bone preservation.
BonePreservation
Bone
Preservation
Short
Integer
Coded values for bone
preservation (see Table
9).
DirectArtifacts
Direct
Artifacts
Short
Integer
The number of directly
associated artifacts.
IndirectArtifacts
Indirect
Artifacts
Short
Integer
The number of indirectly
associated artifacts.
TotalArtifacts Total Artifacts
Short
Integer
The number of direct and
indirectly associated
artifacts.
RadioCarbonDateBP
Radiocarbon
Date (Years
BP)
Short
Integer
AMS dates calibrated in
years before present.
181
Table A-1 Continued: Burial Shapefile Attributes.
Name
Extended
Name
Data Type NotNull Unique
Domain/Restrictions/Not
es
PositionID Position ID
Short
Integer
The position/flexure of the
individual (see Table 7 for
codes).
SideID Side ID
Short
Integer
The side the individual
was laid on (see Table 8
for codes).
DirPPTs Direct PPTs
Short
Integer
Count of directly
associated projectile
points.
IndPPTs Indirect PPTs
Short
Integer
Count of indirectly
associated projectile
points.
DirBIFs Direct BIFs
Short
Integer
Count of directly
associated bifaces.
IndBIFs Indirect BIFs
Short
Integer
Count of indirectly
associated bifaces.
DirEMFs Direct EMFs
Short
Integer
Count of directly
associated edge-modified
flakes.
IndEMFs Indirect EMFs
Short
Integer
Count of indirectly
associated edge-modified
flakes.
DirOTHFLSTools
Direct Other
Flaked Stone
Tools
Short
Integer
Count of directly
associated cores, core
tools, and drills.
IndOTHFLSTools
Indirect Other
Flaked Stone
Tools
Short
Integer
Count of indirectly
associated cores, core
tools, and drills.
DirDEB Direct DEB
Short
Integer
Count of directly
associated debitage.
IndDEB Indirect DEB
Short
Integer
Count of indirectly
associated debitage.
DirFAU Direct FAU
Short
Integer
Count of directly
associated faunal
remains.
IndFAU Indirect FAU
Short
Integer
Count of indirectly
associated faunal
remains.
DirMOS Direct MOS
Short
Integer
Count of directly
associated modified
stone.
IndMOS Indirect MOS
Short
Integer
Count of indirectly
associated modified
stone.
DirMOB Direct MOB
Short
Integer
Count of directly
associated modified bone.
IndMOB Indirect MOB
Short
Integer
Count of indirectly
associated modified bone.
DirQZC Direct QZC
Short
Integer
Count of directly
associated quartz
crystals.
182
Table A-1 Continued: Burial Shapefile Attributes.
Name
Extended
Name
Data Type NotNull Unique
Domain/Restrictions/Not
es
IndQZC Indirect QZC
Short
Integer
Count of indirectly
associated Quartz
crystals.
DirBEDs Direct BEDs
Short
Integer
Count of directly
associated beads.
IndBEDs Indirect BEDs
Short
Integer
Count of indirectly
associated beads.
DirNON Direct NON
Short
Integer
Count of directly
associated natural
obsidian nodules/needles.
IndNON Indirect NON
Short
Integer
Count of indirectly
associated natural
obsidian nodules/needles.
DirPEN Direct PEN
Short
Integer
Count of directly
associated pendants.
IndPEN Indirect PEN
Short
Integer
Count of indirectly
associated pendants.
DirGDS Direct GDS
Short
Integer
Count of directly
associated ground stone.
IndGDS Indirect GDS
Short
Integer
Count of indirectly
associated ground stone.
DirWealth Direct Wealth
Short
Integer
Summed totals from
DirMOS, DirQZC,
DirBEDs, DirNON, and
DirPEN.
IndWealth Total Wealth
Short
Integer
Summed totals from
DirMOS, IndMOS,
DirQZC, IndQZC,
DirBEDs, IndBEDs,
DirNON, IndNON, DirPEN
and IndPEN.
DirTools Direct Tools
Short
Integer
Summed totals from
DirPPTs, DirBIFs,
DirEMFs,
DirOTHFLSTools,
DirMOB, and DirGDS.
IndTools Total Tools
Short
Integer
Summed totals from
DirPPTs, IndPPTs,
DirBIFs, IndBIFs,
DirEMFs, IndEMFs,
DirOTHFLSTools,
IndOTHFLSTools,
DirMOB, IndMOB,
DirGDS, and IndGDS.
Total_Artifacts_Minus_DEB_
and_FAU
Total Artifacts
Minus
Debitage and
Faunal
Remains
Short
Integer
Total_Artifacts minus
IndDEB an IndFAU.
DirCHA Direct CHA
Short
Integer
Count of directly
associated charmstones.
IndCHA Indirect CHA
Short
Integer
Count of indirectly
associated charmstones.
183
Table A-1 Continued: Burial Shapefile Attributes.
Name
Extended
Name
Data Type NotNull Unique
Domain/Restrictions/Not
es
DirSTB Direct STB
Short
Integer

Count of directly
associated stone beads.
IndSTB Indirect STB
Short
Integer

Count of indirectly
associated stone beads.
DirBBB Direct BBB
Short
Integer

Count of directly
associated bird bone
beads.
IndBBB Indirect BBB
Short
Integer

Count of indirectly
associated bird bone
beads.
DirSHB Direct SHB
Short
Integer

Count of directly
associated shell beads.
IndSHB Indirect SHB
Short
Integer

Count of indirectly
associated shell beads.
DirWHI Direct WHI
Short
Integer

Count of directly
associated whistles.
IndWHI Indirect WHI
Short
Integer

Count of indirectly
associated whistles.
Wealth_Diversity_Index
Wealth
Diversity
Index
Short
Integer

Measure of the diversity
of wealth items with each
burial.
DirBLM Direct BLM
Short
Integer

Count of directly
associated bowl mortars.
IndBLM Indirect BLM
Short
Integer

Count of indirectly
associated bowl mortars.
DirPES Direct PES
Short
Integer

Count of directly
associated pestles.
IndPES Indirect PES
Short
Integer

Count of indirectly
associated pestles.
DirMSL Direct MSL
Short
Integer

Count of directly
associated millingslabs.
IndMSL Indirect MSL
Short
Integer

Count of indirectly
associated millingslabs.
DirHST Direct HST
Short
Integer

Count of directly
associated handstones.
IndHST Indirect HST
Short
Integer

Count of indirectly
associated handstones.
DirCOR Direct COR
Short
Integer

Count of directly
associated cores.
IndCOR Indirect COR
Short
Integer

Count of indirectly
associated cores.
DirCRT Direct CRT
Short
Integer

Count of directly
associated core tools.
IndCRT Indirect CRT
Short
Integer

Count of indirectly
associated core tools.
DirDRI Direct DRI
Short
Integer

Count of directly
associated drills.
IndDRI Indirect DRI
Short
Integer

Count of indirectly
associated drills.
DirUNF Direct UNF
Short
Integer

Count of directly
associated unifaces.
IndUNF Indirect UNF
Short
Integer

Count of indirectly
associated unifaces.
184
Table A-1 Continued: Burial Shapefile Attributes.
Name
Extended
Name
Data Type NotNull Unique
Domain/Restrictions/Not
es
DirAWL
Direct Bone
Awls
Short
Integer

Count of directly
associated bone awls.
IndAWL
Indirect Bone
Awls
Short
Integer

Count of indirectly
associated bone awls.
DirPIN Direct PIN
Short
Integer

Count of directly
associated pins.
IndPIN Indirect PIN
Short
Integer

Count of indirectly
associated pins.
Total_Tools Total Tools
Short
Integer

Count of total tools (all
directly and indirectly
associated PPTs, BIFs,
EMFs, DRIs, CORs,
CRTs, UNFs, BLMs,
PESs, MSLs, HSTs,
AWLs, and PINs.
Anemia Anemia
Short
Integer

Denotes presence (1) or
absence (0) of anemia.
Inca_Bone Inca Bone
Short
Integer

Denotes presence (1) or
absence (0) of Inca bone.
Auditory_Exostoses
Auditory
Exostoses
Short
Integer

Denotes presence (1) or
absence (0) of auditory
exostoses.
Dental_Caries Dental Caries
Short
Integer

Denotes presence (1) or
absence (0) of dental
caries.
Healed_Fractures
Healed
Fractures
Short
Integer

Denotes presence (1) or
absence (0) of healed
fractures.
Osteomyelitis Osteomyelitis
Short
Integer

Denotes presence (1) or
absence (0) of
osteomyelitis.
Femurs_with_AP_Flattening
Femurs with
AP Flattening
Short
Integer

Denotes presence (1) or
absence (0) of femurs
with anterior to posterior
flattening.
Int_Dates
Interpolated
Dates
Short
Integer

Interpolated dates derived
from the resulting raster of
cokriging
RadioCarbonDateBP with
depth in meters.

185
Appendix B: Cokriging Prediction of Error Analysis Table for
Interpolated Dates
This appendix will analyze the various interpolation models used in the date
reconstruction. The geostatistical wizard from ArcGIS provides a series of prediction errors for
cokriging models. These vary slightly based on which particular type of cokriging is used. These
prediction errors allow the user to judge how valid the interpolation model was. The mean
error is the averaged difference between the measured and predicted values, the lower the
value, the better. The root mean square indicates how closely the model predicts the measured
values, the smaller the error, the better. The average standard error is the average of the
prediction standard errors. The root mean square standardized error should be close to one if
the prediction standard errors are valid. If the root mean square standardized error is greater
than one, then the model is underestimating the variability in the prediction. If the root mean
square standardized error is less than one, then the model is overestimating the variability in
the predications.
There are several different types of kriging. Cokriging, the method used in this paper,
uses the main variable of interest, its spatial autocorrelation, and cross correlations between the
variable of interest and other variables to make better predictions. Each type of kriging or
cokriging can produce a specific output. There are five outputs, with some not available to
certain kriging and cokriging types. Prediction creates a raster of predicted values. Quantile
creates a surface that classifies data into a certain number of categories with an equal number
of units in each category. Probability produces a surface that maps the probability the values
match one another. The prediction standard error maps the predicted standard errors across
186
the point distribution area. The standard error of indicators maps the standard error across the
area. Probability, prediction standard error, and the standard error of indicators were not
useful outputs for obtaining the interpolated date data. All models also had the potential to be
optimized, in which ArcGIS automatically optimizes the model to produce the lowest amount of
error.
Ordinary kriging is used if there is a simple constant that is unknown. The geostatistical
wizard allows for the data to be transformed using either Box-Cox, Arcsine, Log, or Normal Score
transformations to best fit the data trend. In this model there was no transformation used. For
the initial interpolation analysis, the regular method produced a mean value of 5.145, a mean
standardized error of 0.015, and a root mean square standardized score of 1.003. The mean is a
bit high; however the mean standardized and root mean square standardized scores are at
expected values for a valid model. Optimizing the model actually increases the values, but
lowers the root mean square and average standard error. The best model is the optimized as it
has the lowest average standard error. In the refined model the regular model produced a
mean of 1.805, a mean standardized of 0.011, and a root mean square standardized score of
1.788. These numbers are acceptable however the mean and root mean square standardized
score are a bit high. Optimizing the model increases the mean and mean standardized score but
decreases the root mean square, root mean square standardized, and average standard error
values. The best model is the optimized again, as it had the lower average standard error.
Simple kriging is used where the trend is completely known, and is the least general.
This method also allows for the user to determine the number of bins for the study. In this case,
the default was eight bins. Experimenting with the number of bins did not seem to have a
noticeable effect. In this case study the trend was not known, so this method would likely not
187
have been used anyway. It was run for the sake of completeness and curiosity. The original
interpolation was run using the regular model which produced a mean of -0.938, a mean
standardized of -0.007, and a root mean square standardized score of 0.992. The mean is a bit
off; however the other values do not appear to be too bad. Optimizing the model greatly
increases the mean and mean standardized, and decreases the other values. In this case the
root mean square standardized value moved further away from one indicating it might not be
the better model. Using the refined data for the regular analysis the mean value as -12.646, the
mean standardized was -0.094, and the root mean square standardized was 0.732. The mean is
way off in this model. Optimizing the model however shows significant improvements to all the
values.
Universal kriging is used for trends that vary, where the regression coefficients are
unknown. This uses indicator functions instead of the process itself. In other words, the model
conducts the regression analysis with the spatial coordinates as the explanatory variable. For
the original interpolation study using the regular model, the mean was 5.145, the mean
standardized was 0.015, and the root mean square standardized was 1.003. The mean is high,
but the other values appear acceptable. Optimizing the model actually increases all the values
slightly. Using the refined data to run the regular model provides a mean value of 1.805, a mean
standardized of 0.011, and a root mean square standardized value of 1.788. The root mean
square standardized value is a bit high in this model. Optimizing the model decreases the root
mean square and root mean square standardized error but increases the other values.
Indicator kriging measures the probability a value is above a certain threshold. The
threshold value of 2200BP was used. This model produces and output of errors and likely would
not have been used in the study.
188
Probability kriging produces a probability or standard error of indicators map. This
method also uses a threshold that was set at 2200BP.
Disjunctive kriging is a nonlinear generalization of kriging. It is used to predict the value
itself or an indicator. It has a large number of requirements in order to be valid. It requires a
bivariate normality assumption and approximates certain functions. The assumptions are
difficult to verify and the solutions are mathematically and computationally complicated. It is
included here for the sake of completeness and curiosity.
Table B-1 provides the prediction of errors for the initial interpolation of radiocarbon
dates using the 21 radiocarbon dated burials coupled with depth.
For future studies it is not necessary to run all the different methods and models of
kriging and cokriging in order to obtain data. Knowing which particular methods are best for the
user’s data set and the possible outputs are essential starting points. After these are known, the
user may attempt to create a more valid model through the use of transformations or bins in
order to better fit the data.
In the case of this study, the optimized ordinary prediction method of cokriging was the
best model as it had the lowest average standard error. While this essentially gives a window of
78 years for each burial, consider the radiocarbon dating windows are also typically 60 years.
While this data cannot be considered factual, it is an interesting attempt to determine the ages
of the burials, and it is more information than we had before.

189
Table B-1: Cokriging Prediction of Error Analysis Table for Interpolated Dates.
Cokriging
Type
Results
Prediction Quantile Probability
Prediction Standard
Error
Standard Error of
Indicators
Regular Optimized Regular Optimized Regular Optimized Regular Optimized Regular Optimized
Ordinary
Mean 5.145 5.670 5.145 5.670 0.173 0.196 5.145 5.670
Not An Option
Root Mean
Square
120.358 115.206 120.358 115.206 0.490 0.531 120.358 115.206
Mean
Standardized
0.015 0.021 0.0153 0.021
Not Given
0.015 0.021
Root Mean
Square
Standardized
1.003 1.0158 1.003 1.015 1.003 1.015
Average
Standard
Error
109.508 108.688 109.508 108.688 109.508 108.688
Simple
Mean -0.938 2.488 -0.938 2.488 0.145 0.154 -0.938 2.488
Not An Option
Root Mean
Square
130.784 114.508 130.784 114.508 0.500 0.459 130.784 114.508
Mean
Standardized
-0.007 0.023 -0.007 0.023
Not Given
-0.007 0.023
Root Mean
Square
Standardized
0.992 0.899 0.992 0.899 0.992 0.899
Average
Standard
Error
131.513 124.179 131.513 124.179 131.513 124.179
Universal
Mean 5.145 6.614 5.145 9.224 0.173 0.139 5.145 9.224
Not An Option
Root Mean
Square
120.385 125.144 120.385 119.195 0.490 0.479 120.385 119.195
Mean
Standardized
0.015 0.027 0.0153 0.037
Not Given
0.015 0.037
Root Mean
Square
Standardized
1.003 1.033 1.003 1.032 1.003 1.032
Average
Standard
Error
109.508 110.097 109.508 110.365 109.508 110.365

190
Table B-1 Continued: Cokriging Prediction of Error Analysis Table for Interpolated Dates.
Cokriging
Type
Results
Prediction Quantile Probability
Prediction Standard
Error
Standard Error of
Indicators
Regular Optimized Regular Optimized Regular Optimized Regular Optimized Regular Optimized
Indicator
Mean
Not An Option Not An Option
0.008 0.103
Not An Option
-0.229 -0.227
Root Mean
Square
0.458 0.439 0.664 0.699
Mean
Standardized
0.166 0.009 -1.441 -2.998
Root Mean
Square
Standardized
0.932 0.877 3.468 7.603
Average
Standard
Error
0.455 0.472 0.455 0.472
Probability
Mean
Not An Option Not An Option
-0.006 -0.111
Not An Option
-0.245 -0.249
Root Mean
Square
0.423 0.410 0.746 0.717
Mean
Standardized
0.010 -0.009 -3.764 -1.859
Root Mean
Square
Standardized
0.905 1.008 9.698 4.433
Average
Standard
Error
0.444 0.387 0.444 0.387
Disjunctive
Mean -0.936 2.509
Not An Option
0.145 0.154 -0.936 2.509 -0.928 -0.839
Root Mean
Square
130.783 114.514 0.500 0.459 130.783 114.514 0.502 0.557
Mean
Standardized
-0.007 0.018 0.291 0.328 -0.007 0.0183 -0.186 -0.188
Root Mean
Square
Standardized
0.994 0.908 1.004 0.957 0.994 0.908 1.008 1.160
Average
Standard
Error
131.555 124.203 0.498 0.481 131.555 124.203 0.498 0.481

191

Abstract (if available)

Abstract This thesis uses a geographic information system (GIS) to demonstrate spatial analysis techniques in order to examine changes to a prehistoric society of Native American Wappo dating from 2450 to 1950 years before present (BP) from the Upper Archaic Period in the Napa Valley of California. This cemetery was excavated by Pacific Legacy Inc., a private cultural resources management firm, in compliance with the National Historic Preservation Act (NHPA) and the California Environmental Quality Act (CEQA) for a flood control project. While Pacific Legacy Inc. analyzed the burials on an individual basis, they did not conduct a spatial analysis. They incorporated their data into a simple spreadsheet to look for patterns. This thesis serves as a complimentary spatial examination of the burials based on spatial data. ❧ The dataset is incomplete as it was not collected using a consistent, systematic methodology. Additional burials related to the dataset had also been removed from the site before excavation by erosion and other archaeological excavations. This paper demonstrates select spatial analysis techniques using this dataset as an example. ❧ This thesis examines the distribution of the burials within the cemetery to identify spatial patterns based on burial attributes and artifact distribution. Spatial autocorrelation, cluster analysis, and grouping analysis focus on identifying burial clusters and individual burial outliers. ❧ A form of interpolation known as kriging was used to estimate the dates for the burials that were not subjected to Accelerator Mass Spectrometry (AMS) Radiocarbon dating. The burials were then grouped into corresponding date ranges covering one hundred year time spans. This experimental study allows for identification of changes to society by analyzing the change in burial attributes and artifact types over the course of the Upper Archaic Period. Due to the incomplete nature of the dataset, only two conclusions could be reached with the remaining findings considered suggestive. There is clustering based on bone preservation and the spatial analysis results tend to vary depending on different excavation techniques. Possible clustering of depth, wealth diversity index, directly associated shell beads, and directly associated pendants may reflect certain aspects of ancient society. The possible clustering of artifact association, total tools, tool diversity index, indirectly associated bifaces, indirectly associated edge-modified flakes, indirectly associated unifaces, and indirectly associated pestles can likely be explained due to differing excavation techniques. Possible clustering of natural obsidian needles may be explained as naturally occurring in the soil. Dental caries were found to be possibly dispersed, which is likely just a random occurrence. The experimental radiocarbon date interpolation allowed for an examination of changes to CA-NAP- 399 over a five hundred year period. Thus results from the analyses in this report should not be seen as definitive nor should they be used as foundations for further archaeological analysis. The main purpose here is to demonstrate how spatial analysis may be used with data of this type.

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Asset Metadata

Creator Schrader, Lucian N., III (author)

Core Title Demonstrating GIS spatial analysis techniques in a prehistoric mortuary analysis: a case study in the Napa Valley, California

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Publication Date 02/21/2013

Defense Date 02/20/2013

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag archaeology,GIS,mortuary analysis,OAI-PMH Harvest,spatial analysis

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Kemp, Karen K. (committee chair), Dodd, Lynn Swartz (committee member), Garrison, Thomas G. (committee member)

Creator Email lnschrader@archaeologist.com,lschrade@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-221325

Unique identifier UC11294834

Identifier usctheses-c3-221325 (legacy record id)

Legacy Identifier etd-SchraderLu-1446.pdf

Dmrecord 221325

Document Type Thesis

Rights Schrader, Lucian N., III

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA