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Developing an archaeological specific geodatabase to chronicle historical perspectives at Bethsaida, Israel

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Developing an archaeological specific geodatabase to chronicle historical perspectives at Bethsaida, Israel

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Content

Developing an Archaeological Specific Geodatabase to Chronicle Historical
Perspectives at Bethsaida, Israel

by

Cynthia Burrows

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)

December 2016

Copyright © 2016 by Cynthia Burrows

Dedication
This thesis is dedicated to my husband, Jim Schilling, my mom, Miriam Burrows, my
children, Alyssa and Ryan, and all of my incredibly supportive family members and friends for
their patience and encouragement throughout this process. Special thanks to Anton and Lenny
for their willingness to lay by my side for hours on end while receiving little more than
occasional belly rubs and treats.

Table of Contents
List of Figures .................................................................................................................................. i
List of Tables ................................................................................................................................. iii
Acknowledgements ........................................................................................................................ iv
List of Abbreviations ...................................................................................................................... v
Abstract .......................................................................................................................................... vi
Chapter 1: Introduction ................................................................................................................... 1
1.1 Study Area and Project Background ...........................................................................................2
1.1.1 Study Area ...........................................................................................................................2
1.1.2 Bethsaida Excavation Project History .................................................................................4
1.2 Motivation ...................................................................................................................................7
1.3 Methods Overview ....................................................................................................................10
1.4 Thesis Structure ........................................................................................................................12
Chapter 2: Background and Literature Review ............................................................................ 14
2.1 Literature Review .....................................................................................................................14
2.1.1 The Need to Move Toward Digital Analysis ....................................................................14
2.1.2 Potential for Increasing Collaborative Research ...............................................................15
2.1.3 Working with GIS in a Microcosm ...................................................................................16
2.1.4 Visualizing the Space-Time Continuum ...........................................................................18
2.1.5 The Case for Adoption of Handheld Devices in the Field ................................................20
2.2 Spatial Database Design ...........................................................................................................20
2.2.1 Ontological and Semantical Consistency ..........................................................................21
2.2.2 An Archaeological Project-Specific Database ..................................................................22
2.2.3 Best Practices in Geodatabase Design ...............................................................................23

Chapter 3: Methodology ............................................................................................................... 24
3.1 Overview of Methodology ........................................................................................................24
3.2 Research Design .......................................................................................................................25
3.2.1 Equipment and Software Used ..........................................................................................25
3.2.2 Statement of Data Description, Requirements, and Quality ..............................................26
3.3 Data Narrative ...........................................................................................................................27
3.3.1 Tabular Data ......................................................................................................................28
3.3.2 Stratification Data ..............................................................................................................30
3.3.3 CAD Drawing ....................................................................................................................30
3.3.4 Excavation Grid .................................................................................................................31
3.3.5 Aerial Photography ............................................................................................................32
3.3.6 Point/Polygon Field Data ..................................................................................................33
3.4 Geodatabase Development Rationale .......................................................................................36
3.4.1 Preexisting Entity Relationship Diagram ..........................................................................37
3.4.2 The Redesigned Entity Relationship Diagram ..................................................................39
3.4.3 Testing and Sample Queries ..............................................................................................42
Chapter 4: Results ......................................................................................................................... 44
4.1 Geodatabase Design and Relationships ....................................................................................44
4.2 Populating the Database ............................................................................................................47
4.3 Query Results ............................................................................................................................52
4.4 Visualizing the Data ..................................................................................................................56
4.5 Summary ...................................................................................................................................61
Chapter 5: Conclusions ................................................................................................................. 62
5.1 Lessons Learned .......................................................................................................................62
5.2 Onboarding Consortium Archaeologists ..................................................................................64

5.3 Recommendations for Field Collection ....................................................................................64
5.4 Future Development .................................................................................................................65
References ..................................................................................................................................... 67
Appendix A: Satellite Coverage in the Field ................................................................................ 70
Appendix B: Geodatabase Tables ................................................................................................. 72
i

List of Figures
Figure 1. Geographic location of the ancient city of Bethsaida, Israel. .......................................... 3
Figure 2. Current areas of interest within the Bethsaida Excavation Project. ................................ 4
Figure 3. Katsianis et al. (2007) Temporal Class Diagram. .......................................................... 19
Figure 4. Tennant's tables of ceramic in a geodatabase. ............................................................... 22
Figure 5. A portion of the Finds table. .......................................................................................... 28
Figure 6. Sample legacy data (right) successfully imported to ArcMap. ..................................... 30
Figure 7. Imported CAD drawing with three georeferenced points. ............................................ 31
Figure 8. Fishnet polygons applied in ArcMap. ........................................................................... 32
Figure 9. Entire excavation overview after georeferencing. ......................................................... 33
Figure 10. UTM zone 36N, region of study site. .......................................................................... 34
Figure 11. Trimble during data collection in Bethsaida, Israel, 2015. .......................................... 35
Figure 12. Data collected in the field using ArcPad via Windows Mobile. ................................. 36
Figure 13. Existing ERD. .............................................................................................................. 38
Figure 14. Finalized concept for the new spatial database. .......................................................... 40
Figure 15. Relationships within the ERD ..................................................................................... 45
Figure 16. Geodatabase structure as shown in ArcMap ............................................................... 47
Figure 17. Excel data, the Metal entity, prepared for loading into ArcMap ................................. 48
Figure 18. Points captured site wide in the field for georeferencing purposes ............................. 50
Figure 19. The five points used for georeferencing purposes ....................................................... 51
Figure 20. Close up of georeferencing points and resulting accuracy at city gate area. ............... 52
Figure 21. CAD drawing overlay in ArcMap at 40% transparency ............................................ 57
Figure 22. Example visualization of coin data .............................................................................. 58
ii

Figure 23. Variation of visualization of coin data ........................................................................ 59
Figure 24. Example visualization of varying categories of finds in an area ................................. 60
Figure 25. Example visualization of varying categories of finds in an area ................................. 61

iii

List of Tables
Table 1. Data Description, Sources, Requirements, and Quality .................................................. 26
Table 2. Coded Value Domains .................................................................................................... 41
Table 3. Sample Test Queries ....................................................................................................... 43
Table 4. Records Used to Populate Database ............................................................................... 48
Table 5. Query Results .................................................................................................................. 53
Table 6. Topology ......................................................................................................................... 72
Table 7. Relationship Classes ....................................................................................................... 72
Table 8. Domains .......................................................................................................................... 72
Table 9. Site Entity ....................................................................................................................... 73
Table 10. Grid Cell Entity ............................................................................................................. 73
Table 11. Area Entity .................................................................................................................... 74
Table 12. Loci Entity .................................................................................................................... 74
Table 13. Basket Entity ................................................................................................................. 74
Table 14. Strata Entity .................................................................................................................. 75
Table 15. Ceramic Entity .............................................................................................................. 75
Table 16. Glass Entity ................................................................................................................... 75
Table 17. Metal Entity .................................................................................................................. 76
Table 18. Organic Entity ............................................................................................................... 77
Table 19. Stone Entity ................................................................................................................... 77

iv

Acknowledgements
I would like to extend my sincere thanks to the Bethsaida Consortium for their
willingness to share all of their data with me in the development of this project. Special thanks to
Dr. Carl Savage for all the intricate details and huge data repository, to Dr. Harry Jol for the
high-resolution aerial photography, to Dr. Rami Arav for images and historical information, and
to Christina Etzrodt for the CAD drawings. Special thanks to Dr. Jerome Hall for his constant
encouragement and critique of my academic writing, and to Jim Schilling for his in-depth Excel
experience and knowledge sharing, and for helping me to shape this project from the beginning
during endless hours of discussion and brainstorming. Thank you to my USC thesis committee
members and faculty advisors for your knowledge and expertise, and for keeping me on track
and seeing me through this process.
v

List of Abbreviations
BCE Before Common Era
CAD Computer-aided design
CE Common Era
ERD Entity relationship diagram
GIS Geographic information system
GCS Geographic Coordinate System
GPS Global Positioning System
MS Microsoft
PDF Portable document format
PDOP Position Dilution of Precision
SSCI Spatial Science
USC University of Southern California
UTM Universal Transverse Mercator
WGS World Geodetic System

vi

Abstract
Annual fieldwork at the Bethsaida, Israel archaeological excavation project yields an unwieldy
amount of data that have historically been processed and managed via paper-based means and
have no associated spatial data. There has been little adoption of modern technology applications
to manage this data, even in recent years. The programming objective of this project involved
designing and implementing an intra-site, archaeological specific, spatial database for collecting
and managing excavation artifacts. A project-based approach was taken toward improved digital
data management, tracking, mapping, and visualization in the examination of temporal and
spatial archaeological data, thus facilitating the ability for archaeologists to gain new and
otherwise undetected insights through spatial pattern analysis. Legacy data, along with data
collected via a handheld Global Position System (GPS) device in 2015, aided in establishing the
dataset parameters, feature classes, attributes, and domains of the database. This excavation site
offered a unique opportunity to explore the space-time continuum through numerous human
settlements evidenced by the vertical archaeological record representing the 10
th
century before
Common Era (BCE) through the 1
st
century Common Era (CE). Visualization of the distribution,
concentrations, and spatial relationships of material culture to settlement groups potentially
illustrates social trends and cultural practices over the centuries. Data recording will become
more consistent and efficient through structured, predefined categories and attributes, bringing
greater organization via ontological and semantical consistency. Field collection will be further
streamlined and enhanced by the adoption of handheld devices working congruently as an
extension of the new geodatabase, collecting artifact information and spatial data, including
stratification, in real time. Ongoing research and global collaborative opportunities become
possible with the geodatabase, and greater cohesion amongst the diverse excavation team is
vii

enhanced. Archaeologists are further able to forecast areas for future excavation based on the
visualizations.
1
Chapter 1: Introduction
This project involves the creation of a project-specific archaeological artifact spatial database for
a Geographic Information Systems (GIS) application to a site case study at Bethsaida, Israel. The
study area encompasses a twenty-six-acre extent and reaches back in time to the tenth

century
BCE, chronicling several periods of human settlement in the vertical record. Objectives included
designing, building, populating, and testing the database as well as introducing a handheld device
field collection method as a companion to the database. The application was then reviewed and
presented to archaeologists for their feedback and recommended changes. The resulting logic,
time, and space representation satisfies an underlying void within the field of archaeology.
As a result of this project, archaeologists can gain a better understanding of and visualize
the distribution, concentrations, and spatial relationships of artifacts to settlement groups that
have inhabited this site from the 10th century BCE through the 1st century CE. Hyde, et al.
(2012) discuss the benefits and enhancements GIS brought to work at the San Diego Presidio
Chapel, especially in regards to synthesizing and analyzing intra-site data and reassembling past
cultural practices in order to draw conclusions about social trends. Similarly, Bethsaida
researchers will have improved abilities to target critical grids of archaeological interest within
each of the stratification layers, and to identify correlations and trends associated with specific
periods of inhabited time. This also serves as a tool in establishing direction for future seasonal
excavation work. Stratigraphic sequences introduce a new level of complexity when compared
with traditional GIS visualizations. Because Bethsaida was settled on numerous occasions over
the centuries, it offered a unique opportunity to introduce and exploit GIS capabilities. González-
Tennant (2009) concluded that GIS expands upon and creates new and superior visualization
opportunities in archaeology beyond the traditional section-style drawing.
2
In section 1.1 the project background is discussed. Section 1.2 reviews the motivational
factors that inspired the development of this geodatabase. Section 1.3 provides a brief methods
overview, and Section 1.4 introduces the remaining structure of this thesis.
1.1 Study Area and Project Background
This section is broken down into subsections that will explore the project background.
Subsection 1.1.1 provides relevant information about the geographic area of this case study.
Subsection 1.1.2 examines the Bethsaida Excavation Project history.
1.1.1 Study Area
The survey area for this case study consists of a 26-acre parcel in Israel. Figure 1
provides reference to site location. Bethsaida is more specifically located on the eastern bank of
the Jordan River at the northernmost end of the Sea of Galilee, in the larger region known as the
Golan Heights.
3

Figure 1. Geographic location of the ancient city of Bethsaida, Israel
(BibleHistory.com [last accessed 21 April 2014]).
Various selected areas at the excavation site are of primary focus each season when the
dig occurs, and recent activity (2012 – 2015) has been in the areas commonly known as Area A,
Area T, Area C, and Area B. These areas vary in size but are usually approximately 10 meters by
10 meters square. These terms for the various areas will be used throughout this paper to
describe and refer to the intra-site location. Figure 2 provides general proximity of these areas of
interest.
4

Figure 2. Current areas of interest within the Bethsaida Excavation Project.
1.1.2 Bethsaida Excavation Project History
Bethsaida was discovered in 1987 by archaeologist Dr. Rami Arav of the University of
Nebraska, Omaha. This ancient city was the capital city of the kingdom of Geshur in the 11
th

century BCE, and later, in the 1st century, became the city of Bethsaida, which is mentioned
several times in the New Testament of the Bible. Bethsaida is said to have been home to several
of the apostles (Peter, Andrew, Philip, James, and John), and according to scripture is where
Area C
Area T
5
Jesus performed notable miracles (feeding the multitudes, healing the blind man). Geshur was
home to the Armenian King, Talmai, whose daughter Maacah, became the wife of King David
(Laub 2016, Bethsaida Excavations 2016, Arav and Freund 2004). In fact, the royal palace ruins
are a significant part of what has been uncovered thus far at the excavation, as is a recently
discovered access tunnel behind the palace near the city gate.
As the significance of this discovery spread to the academic and religious communities, it
became the focus of great interest. Under Dr. Arav’s direction, excavation gets under way in late
May each year and lasts only six weeks in duration annually. Numerous basalt stone structures,
Roman roads, Iron Age floors and valuable artifacts have been uncovered, and several
civilizations are known to have inhabited the area based on the cultural materials left behind. The
current asset inventory of the digital artifact database contains nearly 40,000 items of varying
classifications (ceramic, glass, stone, bone, fishing, etc.), items associated with eleven centuries
of inhabited time, and collection dates ranging from 1989 to 2014.
The Consortium of the Bethsaida Excavations Project (hereafter known as the
Consortium, the major beneficiary of this project) and its university affiliates work under the
oversight of Dr. Rami Arav of the University of Nebraska, Omaha. Together with volunteers, the
Consortium annually excavates this ancient city. Today the ruins draw the attention of visitors
from around the world as a premier site to visit in northern Israel. Rondelli (2008) cites the
importance of settlement analysis and the need for understanding the spatial distribution of
artifacts and human adaptations in synthesizing the actions of past settlement systems. Eight
strata, or layers, have thus far been identified and currently represent historic time periods of
activity at Bethsaida. Tripcevish and Wenke (2010) further conclude that although stratigraphic
6
analysis remains one of the biggest problems faced in moving toward a digital environment, it is
also the area that stands to gain the most from it.
Prior to this project getting under way, data management was heavily paper-based and the
archaeologists were eager to move toward digital management of tracking, site mapping, and
visualization. González-Tennant (2009) has noticed that although the gains to be seen by
archaeologists from GIS implementation are great, there are insufficient educational resources as
they attempt to implement this new technology.
The data gathered from Bethsaida has been recorded using many different means over the
years, primarily non-digital until the recent adoption of a database software program housed on a
local laptop computer (Microsoft Access (MS) version 2013 database). At the end of each
excavation day, artifacts of significance are catalogued and data is entered, but the spatial data
was lacking. Further, the consistency of data recording was identified as an overarching problem
that could be vastly improved by establishing ontological and semantical consistency.
Classifications for each artifact category were further broken down into distinct types that are
known to the area, and this geodatabase provides and limits the available options for data input,
rather than allowing open-ended entry. Tennant (2007) examined in depth the benefits a
geodatabase can bring to site analysis when carefully designed feature sets with properly
established relationships and domains are included. Data entry efficiency and accuracy can be
greatly improved due to limited choices offered, eliminating invalid entries. Digital cataloging
establishes data consistency, and may serve as a model for other ancient sites in the region with
similar artifact find catalogues.
7
The entry of the vast amount of data owned by the Consortium is seen as a future or
ongoing project for excavation volunteers or grad students at one of the participating Consortium
universities, but was not a part of this project.
1.2 Motivation
This section examines the various motivational factors that inspired this project,
including the author’s personal connection to this excavation and reasons for investing in its
development. A system to manage, analyze, and visualize large volumes of data was needed.
With this system, greater collaborative research in the off-season could also be facilitated and the
potential for heightened understanding of the data could be realized. The ability to explore the
vertical measurement of the stratification layers was a unique opportunity as was the ability
make the workflow in the field more efficient.
The Bethsaida Excavation Project was first introduced to me during my work in Israel on
an unrelated archaeology research project in 2012. Since then, I have volunteered with the
annual summer excavation team in 2013, 2014, and 2015. My expertise in the field of technology
combined with my strong interest and undergraduate degree in anthropology and archaeology
afforded a unique opportunity to explore, introduce, and blend digital systems into existing
processes to expand collaboration potential and create more efficient site documentation. While
working alongside archaeologists out in the field, it became evident that much information was
being lost due to lack of immediate, detailed, and consistent recording, and that GIS capabilities
could join forces with the Consortium to ease not only the burden of their post-excavation
documentation analyses, but also to increase the level of accuracy and detail of the data
collected. In short, many problems with the existing workflow and processing of information
8
could be mitigated through the development and adoption of a well-designed geospatial database
and the resulting analyses could be greatly enhanced.
Excavations yield a great volume of data each season, and while the data is entered into
the MS Access database on a daily basis, that data is rarely processed promptly, catalogued
accurately, or in a manner laymen can comprehend. As experienced at Bethsaida, and as
Tripcevich and Wernke (2010) conclude, traditional fieldwork and reliance on paper and pencil
techniques has hindered the adoption of new technologies. Additionally, GIS spatial databases,
mapping, and visualization have not been frequently implemented on large-scale, intra-site
excavation projects, and there is great potential for discovering untapped trends amongst the
data. New techniques offer opportunities for creating greater organization amongst the existing
chaos. Clearly defined categories of common artifact types and features combined with the
ability to associate a time period and spatial data with an artifact creates an entirely new way to
query information for visual consumption and analysis.
One objective of this project was to greatly improve the temporal and spatial examination
of the archaeological record of the Bethsaida excavation site. This geodatabase establishes a
consistent reference for the excavation grid and site monuments, thoroughly and consistently
documents artifacts collected, associates finds with specific strata and spatial information, and
provides researchers with a tool to aid in understanding the historical land use and cultural
trends.
Geodatabases can be accessed remotely online by researchers worldwide. This means
potentially expanded collaborative research opportunities in the future. A server-based spatial
database facilitates year-round access and provides worldwide research opportunities, in contrast
to the Bethsaida data, which is available only on-site during the active excavation season. Hyde
9
et al. (2012) cite the research potential and analysis variety that GIS could yield during the post-
excavation period. This is a key point, given that the excavation season is limited to six weeks
each year during the summer months. Research progress goes unhindered by time constraints
given this implementation allowing expanded access for numerous studies.
Representing excavation data via visualization and illustrative techniques on maps and
models helps us to gain new insights through spatial analysis. It may be possible to establish
patterns through visualization that go undetected otherwise. González-Tennant (2009) believes
that the power introduced to archaeologists by GIS will allow them to more easily gauge spatial
relationships in a complex environment. Because of the complexity of this particular excavation,
significant value may be realized. Infographics are powerful depictions of raw data and can be
understood by non-experts in the field of archaeology, including students, visitors to the site, and
others with strong interests in the field. Density, clustering, proximity, and orientation pattern
analysis can be powerfully communicated through visual renderings. One goal of this project
was to accommodate the specific needs of the Bethsaida excavation data and types of artifacts
contained therein, offering the ability to query and visualize data that has been collected over the
years in a robust environment.
Numerous artifacts have been recovered from this historically significant site. This
excavation offered a study area where many settlements have resided over time and are
evidenced by the artifact record. GIS analytics provide the ability to spatially relate the six strata
to one another and to visualize materials from each inhabited era while also keeping them in
context with one another. Subsequent and previous settlement patterns can also be easily
visualized and compared. Hyde et al. (2012) emphasize the benefit of using 3D and other
10
visualization techniques to represent stratigraphy for a more thorough examination of the aspects
(horizontal and vertical) of the material culture at the Presidio.
The data described and tested herein creates a new and exciting blend of information that
has not come together in the past. Archaeologists and Consortium members that work at this
excavation site tend to focus their attention primarily on their own specific area of interest and
not on the project as a whole. Experts have specializations in osteology, pottery/ceramics,
ancient coins, Iron Age, biblical history, theology, and more. Therefore, the types of data
collected over the years may vary greatly, with different features and attributes recorded.
Data collection techniques via the use of handheld instruments with a high degree of
accuracy can facilitate the uniform collection and recording of finds in the field. González-
Tennant (2009) believes that data accuracy can be improved in the field by implementing a
workflow that includes handheld devices loaded with a data entry form that restricts users to
choosing from specific values. This improves the spatial context of the objects and provenience
by attaching precise latitude (x) and longitude (y) coordinates at the time of the find. Point data
collected in this manner in 2015 serves as an example of an enhanced and efficient workflow and
was amongst the sample data entered during the testing phase of the geodatabase. An extension
of this project and geodatabase is a data dictionary for handheld devices that can collect the data
in real time out in the field going forward.
1.3 Methods Overview
The existing database was critically examined and journal articles were investigated to
establish a foundation for the necessary range of entities, attributes, and relationships to be
considered for inclusion in this database as well as potential problems that may be encountered.
11
Most influential were Tennant (2007), Gonzalez-Tennant (2009), Katsianis et al (2008), Zeiler
and Murphy (2010, and Yeung and Hall (2007).
A needs assessment of the existing Microsoft MS Access database revealed a number of
entities that were not in use and not needed as well as an unnecessary number of relationships
and redundancies that could be reduced by a more streamlined design and incorporation of
foreign keys. Based on this assessment a list of database entities and tables including attributes
were assembled.
The scope then advanced to designing the entity relationship diagram (ERD) where the
relationships between the tables and key fields were established. Since the ERD is the essence
and blueprint of this geodatabase, careful design at this stage was critical to the success of the
project.
The preexisting database did not account for the stratification, or vertical/depth
measurements, so it was necessary to incorporate a means of categorizing depth for each of the
stratum. A schema was developed to serve the purpose for this model, and a suitable
categorization strategy was created for incorporation into this project. Note that actual elevation
readings are a part of the legacy data in the Loci table. However, those readings do not carry over
to each artifact individually.
The model was vetted for errors and completeness, and the approved ERD moved
forward to the construction phase of the database using ArcGIS. The ERD was translated into a
working model that was used for data entry.
A fishnet was used in ArcGIS to create a new master grid on a reference map. This grid
acts as an overarching framework and constant point of reference when measuring distances and
12
elevations in the field. This schema was created to serve as a substitute in this model since the
actual, and original, data was not available and had not been properly maintained over time.
High definition aerial photography was georeferenced and used as the basemap for this
project, which provides the foundational layer for subsequent analysis and adds visual interest.
Detailed computer-aided design (CAD) drawings were manually digitized and used for
referencing points and monuments and was compared to data collected in 2015. These drawings
were originally created in AutoCAD, however these data were provided in PDF format for this
thesis.
Tabular data exported from the MS Access database was formatted and edited in MS
Excel version 2013 to include spatial references and additional fields as necessary and then
imported to ArcGIS for visualization. This was a major amount of work. Data collected in 2015
was uploaded to the geodatabase for testing purposes and serves as an example of improved
workflow through mobile device integration in the field. This data was compared to the
georeferenced aerial imagery and the excavation grid to establish degree of error to previous
processes and documentation. This dataset consists of point and polygon shapefiles (feature
classes) with domains exhibiting the power of limited input options.
1.4 Thesis Structure
This thesis is broken down into five chapters. Following this introductory chapter,
Chapter 2, Background and Literature Review, examines other similar works and validates a
void in the field of archaeology that this project aimed to fill. Chapter 3, Methodology, provides
an in-depth look at the way in which this project came to fruition technically. Chapter 4, Results,
discusses the outcomes and analytical benefits gained through the development of this
13
geodatabase. Chapter 5, Conclusions, is a reflection of the lessons learned, successes and
obstacles encountered during this project creation.

14
Chapter 2: Background and Literature Review
This chapter explores some of the problems encountered during excavation periods, and reviews
relevant literature for the development of this archaeological geodatabase. The literature consists
of articles from leading anthropology and archaeology journals, volumes of published research
on Bethsaida, publications from GIS journals, and Esri publications. Section 2.1 provides an in-
depth look at literatures addressing similar problems as those faced at Bethsaida and
substantiates field-related needs. Section 2.2 reviews geodatabase designs, components, and
features that influenced specific needs of this project, as well as general best-practices in spatial
database design.
2.1 Literature Review
Many in the field of archaeology share overarching difficulties, but as trends toward
digital techniques and analysis become more prevalent, powerful, and efficient, technically
skilled individuals entering the field find numerous opportunities for improvement, as evidenced
by the literatures cited here. Section 2.2.1 explores the need for adoption of digital technologies
in the field of archaeology. Section 2.2.2 discusses how digital enhancements can increase
collaborative research. Section 2.2.3 validates the use of GIS on a large-scale project. Section
2.2.4 highlights the unique opportunity to model and analyze an archaeological excavation’s
stratification contents. Section 2.2.5 recognizes the value in adopting a field strategy that
incorporates handheld devices that can be synchronized with the geodatabase.
2.1.1 The Need to Move Toward Digital Analysis
Consistency is one of the overarching problems with the data reporting at Bethsaida.
There were no limited options or selections to choose from during data entry, everything
currently was open ended so queries did not return accurate or complete results. Brovelli and
15
Maurino (2000) present a concept for structuring archaeological data in the context of a GIS
environment and XML format model. The development of ARCHEOGIS, a web-based
geodatabase application, aims to improve access and entry of excavation data remotely. In doing
so, they present a thoughtfully considered hierarchical structure for a site and metadata model
which was referred to during this project development. They address the difficulty presented by
the lack of computer fluent professionals in the archaeology field and stress the need for
consistency in data reporting, and for standardized models for site and metadata.
At an excavation site in Peru, Tricevich and Wernke (2010) use a GIS data-gathering
system to digitize and accelerate the gathering of artifacts and structural feature information at an
excavation site in order to improve recording and analysis. The authors highlight the benefit of
having an entry method, or mobile form, which is foolproof in gathering accurate spatial
provenience in the field, moving away from handwritten recording, emphasizing the need for a
complex artifact attribute collection. This is a byproduct of a well-designed spatial database.
Examples of data dictionaries and the manner in which the stratigraphy is recorded are also
discussed and addressed. This type of system eliminates typographic errors and increases
efficiency of data entry through the use of drop-down selections, radio button choices, and check
boxes whether data is collected in the field or post excavation.
2.1.2 Potential for Increasing Collaborative Research
One of the primary difficulties experienced in post-excavation analysis is the physical
distance between the Consortium members, which include: University of Nebraska Omaha,
University of Hartford, Wartburg College, University of San Diego, University of Tulsa, Sacred
Heart Seminary and School of Theology, St. Francis Theological College, College of Idaho,
Truman State University, and Drew University (Bethsaida Excavations, 2016). Hyde et al.
16
(2012) illustrate in their study at the San Diego Presidio Chapel, that the implementation of GIS
enabled a higher degree of synthesis and collaboration in the analysis of their study, and
concurred that post-excavation research and collaboration benefitted and was facilitated by its
adoption. The Consortium was eager to have a process that was more inclusive of the members
during the off-season, one that would increase and improve communication and analytical
processes.
Reed et al. (2015) stress the need for collaborative research environments in spatial
databases in Digital Data Collection in Paleoanthropology. They emphasize the need for sharing
in order to advance research in the scientific field of paleoanthropology and introduce a model
for a web-based structure to facilitate this type of collaboration. Research, documentation, and
publication at Bethsaida all benefit from the ability to share data and work in greater unity and
less in silo operations. Additionally, the authors endorse the need for mobile recording adoption
and effective database design to improve workflows dramatically over previously used (paper)
methods.
2.1.3 Working with GIS in a Microcosm
The small geographic context that is the subject for analysis of this project is one of the
unusual aspects introduced here. Bethsaida is a 26-acre parcel, and active grid squares within that
area may be only 100 square meters in size. Because equipment today has much greater capacity
for accuracy than in the past, recording finds can be accurate to the nearest centimeter in some
cases. One challenge in conducting this literature review was to find other studies that had
effectively implemented GIS strategies on a large-scale project. And while the geographic extent
itself has little bearing on the geodatabase design, the resulting analyses will benefit greatly from
use of equipment with centimeter level accuracy. In Memories of a world crisis: The
17
archaeology of a former Soviet nuclear missile site in Cuba, author Burstrom (2009) discusses
the investigation by archaeologists and GIS professionals into the Cuban Missile Crisis in the
Cuban town of Santa Cruz de Los Pinos. Their goal is to identify and document remaining
evidence of missile sites using GIS, resulting in many untold stories from the area. This study
being so similar to that of an archaeological excavation documents success of a GIS study in a
microcosm environment, or small geographic area, and also establishes the relevance of the
ability to accurately detect, record, and document objects in a small geographic environment.
Similarly, researchers document select Inuit settlement details with a high degree of
accuracy by implementing GIS to catalog and map archaeological monuments and settlement
patterns in the Kazan River region, most specifically, caribou crossing, intending to tie oral
tradition to the archaeological record. Authors Stewart et al. (2000) discuss the process and needs
in developing feature classes and a spatial database to support the use of GIS in a small
geographic area (500 by 500 meters).
Kacey Pham’s (2015) master’s thesis work is very similar in nature to the project
proposed here. And I did, in fact, work closely with Pham on her prototype spatial database in
SSCI-582, which was further modified and applied to her thesis work. While Pham’s work
relates specifically to a paleontological application at the La Brea Tar Pits, the database proposed
here will bear a similar foundational structure resulting from the work done in partnership in the
Spatial Databases class. Her work in a very small geographical context speaks to the ability of
GIS to be precise and thorough in the documentation and handling of archaeological finds and to
exploit the depth, or vertical nature of time and the law of superposition.
18
2.1.4 Visualizing the Space-Time Continuum
One of the greatest challenges to overcome with the Bethsaida project was designing and
accommodating the z, or depth, data. Researchers from San Diego State University used existing
data from the San Diego Presidio Chapel excavation in their case study and performed spatial
analysis on burials at the site to reconstruct the ruins using 3D models and GIS technology. Hyde
et al. (2012) explain how visualization, data management, and analysis were incorporated in this
successful project, which illustrated the space-time continuum by plotting the z coordinate, or
stratification/vertical depth measurement. Their study has much in common with the proposed
project, specifically the visualization elements, and the inclusion of the vertical measurement to
represent the stratification. In addition to Pham’s (2015) study, this exemplifies the incorporation
of the z measurement for study of the vertical record.
Katsianis et al. (2007) discuss the challenges in visualizing the spatiotemporal nature of
stratification in archaeology. The authors point out that an artifact can have “multiple temporal
values” in that it could have been manufactured at one time, used at another, deposited in the
archaeological record at yet another. They discuss the problem presented by stratigraphy, cite the
need for spatial databases to accommodate the complex nature of an object, and propose a
solution that is “object-space-time” conscious. Examples of their entity relationship diagram
proved to be extremely helpful in the development of this project. Figure 3 shows the data
handling in their Temporal Class Diagram.
19

Figure 3. Katsianis et al. (2007) Temporal Class Diagram.
Birkenfeld et al. (2015) conclude “the application of precision piece plotting (point
provenance) of finds to obtain their precise x, y, and z coordinates continues to be sporadic.” In
their approach to work at Wonderwerk Cave, they employ a “reverse stratigraphic construction”
technique. They present a very methodical and logical approach to solving the complexity of
stratigraphy in GIS.
Anderson and Burke (2008) document the difficulty that can be presented with complex
stratigraphy in an intra-site archaeological analysis and cite the gains to be made through
analysis of the vertical, third, or z, dimension, distribution of artifacts. They argue that
stratification cannot be arbitrarily determined and does not remain consistent, that considerations
have to be made for interruptions in the stratigraphy by human or other means.
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2.1.5 The Case for Adoption of Handheld Devices in the Field
Data collection and entry for the Bethsaida Excavation can be vastly improved through
the adoption of handheld devices in the field as demonstrated by the workflow introduced in this
project and collection methods used in 2015. Newhard et al. (2013) highlight two archaeological
case study surveys in Anatolia and conclude that the adoption of handheld devices in the field
allowed for the rapid gathering of data and fast processing time for further analysis using new
GIS techniques. They found that although the data collection process can become more
cumbersome in the field, the reduced post-processing time greatly improved productivity and
workflow efficiency, citing increased accuracy of point placement within the excavation survey
area, which was favorable to visualizing a general or random distribution of point density. They
accurately illustrate and replicate actual artifact concentrations.
Reed et al. (2015) outline the benefits and strategy for using mobile devices in the
collection of field data, citing the affordable nature of GPS devices today along with dependable
integrated workflows to spatial databases, which presumably ease the level of difficulty in
implementation and offers a wider availability of tools and software applications that can be
employed. Tripcevich and Wernke (2010) concur and acknowledge that the workflow and
efficient capture of data are great benefits realized by the adoption of a digital field strategy at
the excavation site.
2.2 Spatial Database Design
This section addresses some of the unique needs of an archaeological specific
geodatabase. Section 2.2.1 discusses the need for ontological and semantical consistency in
order to classify objects according to a structure that is adhered to. Section 2.2.2 references
21
actual geodatabases that have been designed for similar implementation. Section 2.2.3 reviews
best practices in geodatabase design.
2.2.1 Ontological and Semantical Consistency
As previously mentioned, inconsistent data entry presents numerous problems in analysis
and query results. Sharon, Degan, and Tzionit (2004) cite similar findings to Newhard et al.
(2013), highlighting the value in implementing GIS technology at an archaeological survey
project in Ramat Beit Shemesh, Israel. They find that handheld data capture revolutionized the
ability to quantify spatial information gathered at the site. This study is extremely relevant and in
close geographic proximity as well. They also point out the significance and stress the need for
an extremely well organized data collection system and well-defined ontology as necessary
foundations for successful outcomes, arguing that archaeologists in general should begin to move
toward GIS since other data sources and documentation are moving in that direction. The authors
had several objectives including the need for a database that was unified in all aspects of
collection, analysis, and output, citing that lack of a common format amongst the data was a
critical hindrance to the collaborative work.
Rondelli et al. (2008) address the need for improvement of artifact classification at
archaeological sites. The interpretation and consistency of reporting currently varies from
location to location and the authors propose semantic and ontological standards for analytical
purposes and deeper analytical discovery. They further argue that large repositories of
information could be greatly enhanced and more effectively queried with the proposed
standardization. Pre-defined choices, well thought out entities, attributes, domains, and
relationships are critical to the success of the geodatabase.
22
2.2.2 An Archaeological Project-Specific Database
Many database designs were investigated before proceeding with development, and it
became clear that each and every archaeological excavation needs a customized entity
relationship diagram as a foundation. González-Tennant (2009) determined the needs for and
implemented a GIS model of data acquisition and presentation through hands-on fieldwork at
four gold mining locations in New Zealand, citing improved accuracy over traditional methods
with continuously improving equipment and software. The diagram and discussion of the
geodatabase that resulted from their work is similar to this project. The need to organize, filter,
and visualize data from these sites drove the development of a predictive GIS model. The author
argues that digital technologies are continuously improving as equipment and software become
increasingly more precise and accurate.
Tennant (2007) chronicles theoretical collection of excavation data using GIS and
proposes a design for and implementation of a geodatabase complete with detailed breakdown of
feature classes, attributes and relationships. Figure 4 is an example of how ceramics and
relationships might be handled in a geospatial database according to Tennant.

Figure 4. Tennant's tables of ceramic in a geodatabase.
This sound model and categories for feature classes was most useful during project
development, however, Tennant does not account for stratification. Instructions for testing and
23
downloading sample data to determine the soundness of the database before to implementation
are also included. Because of the potential for great variability amongst cultural materials and
assemblages present at an archaeological site, the author stresses the importance of project/site-
specific database design to suit the environment, confirming earlier thoughts during the review of
the artifact catalog. Because sites vary greatly from one area and time period to another, it is
highly desirable to design a project-specific geodatabase to manage relationships and feature
classes.
2.2.3 Best Practices in Geodatabase Design
The Esri publication by Arctur and Zeiler (2004) provided sound recommendations for
planning the design and construction of the geodatabase. Their advice was to adopt an existing
database schema template as the starting point, then to modify the template to suit the specific
project needs. While that would have been highly desirable, a template could not be identified
with closely aligned qualities needed for this project. The model proposed by Tennant (2007),
discussed above, provided the nearest basis for comparison. In addition, Arctur and Zeiler
suggest recommendations for a workflow, which was followed in the development of this
geodatabase. It provides for continuous improvement and testing through a six step process: 1)
obtain or develop a design, 2) modify the design, 3) load data, 4) build topological relationships,
5) test the model, and 6) revise the model. Each of these major steps was further broken down
into a logical progression of small tasks required to bring the project to fruition. Careful analysis
of the data, layers, feature classes, feature datasets, topology, domains, and relationships created
a foundation upon which to build.
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Chapter 3: Methodology
This chapter discusses the rationale and methodology behind the development plans for the
Bethsaida Excavation spatial database project. Section 3.1 provides a general methods overview.
Section 3.2 looks at the data requirements, equipment and software needed. Section 3.3
investigates each of the data sets, their contents, and how each was prepared and processed for
use. Section 3.4 reviews the geodatabase programming needs, and section 3.5 includes a detailed
project timetable with dependencies and expected completion dates.
3.1 Overview of Methodology
This section discusses the types of data that were needed and the field expertise required
of individuals who were the intended end users of this application. A previous project in the
SSCI-582 Spatial Databases class under the oversight of Dr. Jordan Hastings served as
inspiration for this approach.
Data from an archaeological excavation comes in a wide variety of formats, sources, and
in varying degrees of accuracy. One of the goals here was to unite those sources into a single
cohesive structure to improve the quality of the data and demonstrate that the data sets can be
combined spatially to produce robust visualizations based on a well-organized framework and
classification system.
Much of the data received from the Consortium was in tabular format and required some
cleanup, preparation and reestablishing relationships before it could be imported into Esri
ArcMap version 10.4. MS Access database tables, Excel spreadsheets, Portable Document
Format (PDF) documents, and high-resolution imagery are examples of the types of data that all
needed to come together. Spatial data consists of point and polygon shapefiles. The process for
completing this task is outlined in Section 3.3.1.
25
Two datasets that were identified as necessary early on could not be obtained (the
elevation [stratification] data, and the excavation master grid [point] data). This did not hinder
the development of the project, however, as it was possible to create schemas to substitute for the
actual measurements and points that have been used historically in the field by the
archaeologists.
Frequent communication, mostly via email, with several of the Consortium
archaeologists has been instrumental in shaping the needs of this project. They form a core group
of advisors during the development of this application. Key individuals participating in this
process were Dr. Rami Arav (University of Nebraska, Omaha), Dr. Carl Savage (Drew
University), Dr. Jerome Hall (University of San Diego), Dr. Harry Jol (University of Wisconsin
– Eau Claire), and Dr. Nicolae Roddy (Creighton University).
3.2 Research Design
Existing archaeological geodatabases were examined for features and entities,
relationships and domains, feature classes and fields, and then were compared to the pre-existing
(non-spatial, MS Access) database during needs analysis. Based on the literature examined, there
were many ideas that contributed to the final research design. The closest comparable project
was found in Kacey Pham’s USC thesis paper, the result of which stemmed from a common
group project in the SSCI-582 Database class. The original concept for this database was a
collaborative effort by Kacey Pham, Jennifer Titus, and myself.
3.2.1 Equipment and Software Used
Equipment used includes a MacBook Pro laptop running O/S X Yosemite (10.10.4) and
also running Windows 8 on a dual boot system. A Trimble GeoXH 3000 (running ArcPad
software 10.0, GPS Controller 2.22, and Windows Mobile 6.1) was used for field collection in
26
2015. ArcGIS (10.4) and MS Office 2011 and 2013 were instrumental in the development of this
database. Lucidchart was also employed to diagram the relationships and entities during the
design phase.
3.2.2 Data Description, Requirements, and Quality
All data sets (except those that were self-gathered) are the property of the Bethsaida
Consortium, and the quality varies. Unfortunately, there are no alternative sources as the data
sets are unique to the excavation and largely legacy data, ranging in date from 1989 to 2014.
Also note that much of the data is gathered by volunteers or untrained amateur archaeologists,
and therefore often inaccurate in original descriptions.
Table 1 below succinctly summarizes the data involved in this project. It includes the
data description, the spatial or non-spatial nature of the data, data sources, formats and types of
data, project requirements, and quality.
Table 1. Data Description, Sources, Requirements, and Quality
Data Description Data Type & Requirements Data Quality
Tabular data – artifact
information
Source: Bethsaida Consortium
Non-Spatial
Type: MS Access database on
local computer
Requirements: Must be able to
export from MS Access to Excel
Must have sufficient detailed
information to categorize data
Cannot be assessed – quality of
content depends greatly on data
entry and knowledge of the
individual assessing each find.
Legacy data must be assumed to
be correct due to these
limitations.
Stratification data
Non-spatial - categorical
assignment

Type: MS Excel spreadsheet
Requirements: Must have
sufficient information for
applying breaks to strata.
Stratification categories apply
site-wide as blanket coverage
A schema was designed to
accommodate stratification
classification in lieu of actual
elevation measurements.
27
Data Description Data Type & Requirements Data Quality
CAD drawing
Source: Bethsaida Consortium
Non-spatial (prior to
georeferencing)
Type: Adobe PDF format
Requirements: High
resolution/print quality
document that when scanned
and imported to ArcGIS renders
clear imagery
Successful geocoding revealed
that the accuracy of the CAD
drawing has sufficient
resolution.
Excavation grid
Non-spatial (prior to
georeferencing)
Type: Created vis Fishnet Tool
in ArcMap.
Requirements: None
A schema was designed to
replace data that was unavailable
and inaccurate/incomplete. This
serves as a reference grid
covering the entire 26-acre site.
Aerial Photography – high
resolution imagery
Source: Bethsaida Consortium
Non-spatial (prior to
georeferencing)
Type: JPEG format
Requirements: High-resolution
imagery to meet the needs of
large-scale visualization.
Provides sufficient visual detail
to 5 meters
Excellent resolution (360 dpi)
with only a small area of non-
coverage, which does not lie
within an active area.
Point and polygon data
Source: Field collection
Spatial
Type: Shapefiles – point and
polygon
Requirements: Points to include
significant monuments,
structures, roads. Polygons to
include active areas
Meter level accuracy based on
visual inspection and
configuration
3.3 Data Narrative
The following sections describe each of the data sets that were incorporated into the
resulting final project. Section 3.3.1 reviews the MS Access database. Section 3.3.2 is an
overview of the stratification data’s role. Section 3.3.3 discusses the CAD drawings, what and
how they are used here. Section 3.3.4 examines the excavation grid. Section 3.3.5 looks at the
aerial imagery. Section 3.3.6 investigates the point and polygon data collected in the field.
28
3.3.1 Tabular Data
Data from the MS Access database tables was easily exported into Excel spreadsheets.
Subsequently there were a number of challenges involved in getting the tables formatted
properly and accepted into ArcMap. Figure 5 below provides an example of the data, including
errors, that was exported from the MS Access database (shown here is a portion of the Finds
table). Typical errors that needed to be corrected manually included things like Roman Nails
that were categorized as Pottery or Bronze and Silver Coins that were categorized as Pottery or
Bone that was classified as Stone, etc. A total of 40,147 Finds and 31,999 Baskets were
recorded within the pre-existing relational database. Other entities (tables) were recreated during
the programming phase.

Figure 5. A portion of the Finds table exported from MS Access into Excel spreadsheet. The data
was often irrelevant, incomplete, or nonsensical, as evidenced here.
29
The process outlined here for formatting the Excel tables was complex and time
consuming. The MS Access information, when exported to Excel, became static and
relationships between fields and attributes no longer existed. Excel was chosen as the desired
software to reestablish those relationships because of its ability to associate cells with one
another by using the formula building feature, specifically VLOOKUP. A new Excel workbook
was created and all relevant columns from the multiple MS Access tables were added to a single
master worksheet with corresponding header labels. New worksheets, one for each entity, or
table, were created to mirror the design of the ERD for loading into ArcMap. The Finds_ID was
the single unique identifier that could be tied back to, and associated with, all other tables. Finds
were sorted by type, then copied and pasted into the five sheets representing the five Finds
entities. For example, all of the Find_ID’s for Pottery were pasted into the Ceramic sheet. It was
then necessary to find the Basket_ID, Basket_No, Loci_ID, Loci_No, Area_ID, and Strata_ID
that corresponded to each unique Finds_ID. By using the VLOOKUP feature, this was
accomplished and all relevant information related to each Finds_ID was restored on the master
worksheet, resulting in a complete record for each artifact.
A total of 65 pairs of Easting and Northing coordinates from point data collected in the
field were entered into the Excel spreadsheets (for testing purposes only) prior to importing a
small sample of data into ArcMap, resulting in the desired spatial rendering as seen in Figure 6
below. Plotted points represent each record after importing to ArcMap.
30

Figure 6. Sample legacy data (right) successfully imported to ArcMap with resulting points
(left).
3.3.2 Stratification Data
A schema for stratification data was created for this project in order to accommodate
depth. One column in the Excel spreadsheet handles the z measurement in a categorized manner,
associating each database record (when known) as belonging to one of 8 different strata; 1 being
closest to the surface, and 8 currently being the deepest. This eliminates the need for elevation
readings for each artifact record, although actual elevation readings are included as a part of each
Loci record for reference in legacy data as well as new collections.
3.3.3 CAD Drawing
Detailed CAD drawings of 4 active excavation squares were obtained from the
Consortium in PDF format. These were used to further confirm accuracy of reference points and
monuments. Each was digitizing into JPG format, imported to ArcMap as a data layer, and
georeferenced using field collection data points via the Georeferencing tool in ArcMap. Figure 7
shows one of the CAD drawings after import and with 3 georeferenced points visible.
31

Figure 7. Imported CAD drawing with 3 georeferenced points.
3.3.4 Excavation Grid
It would have been highly desirable to geolocate the original excavation grid onto the
basemap, however, that information was not available during the course of this project
development and it was noted that many of the site markers are missing, have been moved, or
have been vandalized over time, thus rendering it inaccurate. The grid acts as an overarching
framework at the site to provide “constant” reference points when measuring distances and
elevations in the field. It also provides historical monument information and assists in confirming
geolocation accuracy of new data. Since this data was deemed a loss, a fishnet was applied in
32
ArcMap as a suitable substitute strategy in order to establish consistent reference points in the
digital environment. These new reference points do not have any similar absolute or relative
locations to the original on-site markers, but the new digital schema can actually be considered a
more reliable and consistent source than the old, on-site markers due to significant disruption in
the field. Figure 8 below shows the fishnet applied over a data layer.

Figure 8. Fishnet polygons applied in ArcMap.
3.3.5 Aerial Photography
High-resolution aerial photography of the excavation was obtained from the Consortium
and added to ArcMap as a data layer. This layer became the basemap for the project. The
imagery of this part of the world is not readily available via web services to an extent needed to
33
carry out this project, so it was necessary to procure photography with a high level of detail. The
Consortium had made special arrangements for this photography in 2014 in the hopes that it
would enhance ongoing research opportunities. Point data collected in the field was added to the
map as layers, which were then used to georeference the imagery using the Georeferencing tool
in ArcMap. Figure 9 shows the aerial imagery after import to ArcMap and with some
georeferencing points in place.

Figure 9. Entire excavation overview after georeferencing (left), and southern end of excavation
shown in more detail (right).
3.3.6 Point/Polygon Field Data
Several sets of point data were collected in late June and early July of 2015 on-site at the
excavation. The collection was executed using Trimble GeoXH-3000 running ArcPad 10.0 and
GPS Controller 2.22 software on a Windows Mobile 6.1 operating system. GPS Controller and
ArcPad software were configured to require a maximum Position Dilution of Precision (PDOP)
tolerance of 6, and a minimum satellite configuration number of 5, at a minimum of 15 degrees
34
above the horizon, settings recommended as striking a nice balance between precision and
productivity (Texas Tech University, 2012). Data was collected in the Geographic Coordinate
System (GCS) World Geodetic System (WGS) 1984, and projected in Universal Transverse
Mercator (UTM) zone 36N (See Figure 10 below for geographic coverage) in ArcMap. Real
time satellite coverage diagrams for precise location information can be found in Appendix A for
each of dates June 28 through July 1, 2015.

Figure 10. UTM zone 36N, region of study site (image from SpatialReference.org [last accessed
30 May 2016]).
Various data sets include greater site monuments (for reference, objects that are not likely
to change or move), individual active square corners, grid stakes (master grid) and parameters,
and significant structures (walls, roads, city gate area). Each was then added to ArcMap in
individual layers. Figure 11 shows what the data looks like during the collection process on the
handheld Trimble device.
35

Figure 11. Trimble during data collection in Bethsaida, Israel, 2015.
Figure 12 shows the various points and layers that were then added to ArcMap as the
Initial Survey Points layer. Some difficulty was encountered with the import of the data to
ArcMap due to unfamiliarity with the workflow, however, once the proper files were associated
and indexed in the layer properties it became clear as to what data storage structure requirement
functionality was needed for this workflow. Post processing data correction was not
accomplished or possible, as the GPS log files were not captured during the collection process
due to an oversight during device configuration. Based on the large-scale nature of the project
and the ability to overlay the points onto the high-resolution photography, combined with the
PDOP and satellite restrictions that were in place during the collection process, it is believed that
the evidence for data accuracy is quite reliable. Visual inspection of the plotted data aligns as
expected and according to detailed notes taken during the collection process. In addition, the
geographic location is nearly free of canopy cover and obstructions, and the Trimble GeoXH
3000 provides 1 to 3-meter accuracy, which can be further enhanced to subfoot accuracy with
real time or post-processing (Trimble, 2012).
36

Figure 12. Data collected in the field using ArcPad via Windows Mobile operating system on
Trimble GeoXH-3000 brought from ArcPad into ArcMap and visualized in layers.
The Initial Survey Points layer acts as a foundational layer for the overarching site
georeferencing. Points were taken throughout the 26-acre site in order to work with the aerial
photography and gain better accuracy when working within a single collection square, a smaller
geographic unit, often about 10 x 10 meters in size. The handheld collection also served as an
example of improved workflow through mobile device integration in the field. Point and polygon
shapefiles (feature classes) with domains exhibit the power of limited input options.
3.4 Geodatabase Development Rationale
Many other databases and articles were consulted in the design phase of the new entity
relationship diagram (ERD). It was essential to develop a keen sense for the necessary range of
materials and attributes to be considered for inclusion in this database as well as potential
problems that might be encountered. It was also necessary to keep it as simple as possible. This
research involved gaining as much insight as possible from the archaeologists and directors of
the Consortium. Early on, the local MS Access database was evaluated for suitability and flaws,
37
and a resulting list of database tables including datasets, entities, feature classes, and attributes
emerged. See Appendix A for all database tables.
Section 3.4.1 reviews the structure of the MS Access database. Section 3.4.2 provides an
overview of the redesigned spatial database. Section 3.4.3 illustrates examples of simple queries
for testing purposes.
3.4.1 Preexisting Entity Relationship Diagram
The diagram from the existing MS Access database is shown in Figure 13 below. It was
much more complex than what was needed and contained entities that were omitted in the
development of this project. Entities circled in yellow were selected for revised inclusion and
were deemed necessary for the desired visualization. Basket and artifact photos will be collected
in the field concurrently with artifact information on the handheld devices; however, the
photographs are not stored in the geodatabase during this phase of the project, just referenced
there. Licensing entities were also eliminated as they were not currently in use or needed. Notes
are now handled as an inclusive field within each of the remaining entities rather than having
Notes as an entity of its own. Stratum used to be handled as a field within the Baskets entity but
will become an entity/table itself in the new design. Domains will be added as drop-down menu
choices for data entry to aid in the ontological and semantical consistency desired vs. open ended
data entry.
38

Figure 13. Existing ERD, yellow circles indicate entities that have been translated into the new geodatabase.
39
3.4.2 The Redesigned Entity Relationship Diagram
Once the elements to be included in the database had been determined, the scope
advanced to designing the Entity Relationship Diagram (ERD) where the relationships between
the tables and key fields were established. Multiple revisions of the ERD resulted in this final
design. It is the essence and blueprint of this geodatabase. Careful design at this stage was
critical to the success of the project and USC faculty thesis advisors and Consortium members
were asked to contribute their feedback, expertise, and suggestions for programming and
improvement. Figure 14 below depicts the basic ERD conceptual diagram that was settled on for
development.
40

Figure 14. Finalized concept for the new spatial database, the Entity-Relationship Diagram.
41
The inclusion of Coded Value domains in the geodatabase infrastructure will ensure that
proper categorization during the collection process aids in querying the data during the analysis
phase. Coded Values also ensure that typographical errors do not influence data input and that
only relevant options present themselves for selection within each entity. This reinforces the
ontological and semantical consistency that was lacking in the MS Access database. Table 2
describes some of the Coded Value domains that were included in this design to create an
efficient analysis environment.
Table 2. Coded Value Domains
Entity Domain Name Domain Selections
Ceramic
Glass
Condition Whole
Fragment
Scatter
Glass Color Clear
Green
Blue
Yellow
Red
Other
Metal Material Bronze
Gold
Iron
Lead
Silver
Metal Type Coin
Jewelry
Tool
Organic Material Bone
Leather
Textile
Wood
Stone Material Basalt
Flint
Limestone
Stone
42
It was also necessary to identify and classify each of the strata. A domain schema was
developed which will allow users to select a strata from a drop-down menu during the data entry
process and the schema has been applied site-wide as a blanket approach at this time.
Once the model was thoroughly vetted for errors and completeness, the actual
construction phase of the database in ArcGIS began. The ERD was translated into a working
model that was used for data entry.
3.4.3 Testing and Sample Queries
A large sampling of legacy data from the MS Access database (ranging in date potentially
from 1989 to 2014) was selected according to category and uploaded into the system to test the
degree of compatibility and accuracy, and to aid in understanding any additional programming
needs. The resulting visualizations of various layers were added to the basemap. Layers included
a variety of artifact types in addition to vertical stratification categories in order to demonstrate
the power of the z spatial component in the output. Sample queries were run and included spatial,
temporal, and attribute field data input. Table 3 provides examples of queries that were used to
assess the general functionality and complexity possible.
43
Table 3. Sample Test Queries
Test Queries
Query 1 Query the Stone attribute table

Find all of the Flint records in the Stone entity that were excavated from Strata 6
Query 2 Query the Organics attribute table

Find all of the Bone records in the Organic entity that were excavated on a specific
date
Query 3 Query the Metal attribute table

Find all of the Bronze records in the Metals entity that are classified as Coin and
were excavated from Strata 2
Query 4 Query the Glass attribute table

Find all of the Blue records in the Glass entity that are classified as Fragment
Query 5 Query the Ceramics attribute table

Find all of the pottery from the Ceramic entity that are classified as Whole and
that were found in Area A and also were excavated from Strata 6
Query 6 Query the Ceramics attribute table

Find all of the pottery from the Ceramics entity that are classified as Whole and
comprised of less than six pieces
Query 7 Query the Organic attribute table

Find all of the Bone in the Organic entity that was excavated within a specific
range of dates

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Chapter 4: Results
This chapter provides an overview of the results that were achieved in the development of this
geodatabase. Section 4.1 discusses the final design and relationships included in the model.
Section 4.2 describes population of the spatial database. Section 4.3 summarizes query results
during the testing phase, and section 4.4 illustrates data visualization. Section 4.5 summarizes the
results herein.
4.1 Geodatabase Design and Relationships
The ERD discussed in Chapter 3 and pictured in Figure 14 was translated and built in
ArcMap without further revision. The simplicity of the final design combined with the ability to
perform complex queries resulted in a system that will prove useful in performing the most
common and typically needed functions. As mentioned in Chapter 3, many entities were omitted
from the MS Access legacy database in order to streamline data entry both in the field and when
working on a desktop system. Figure 15 provides a closer look at the relationships between the
entities. The foreign keys associated with the Finds feature classes are directly responsible for
and provide the power to carry out various and specific queries.
45

Figure 15. Relationships within the ERD
Grid Cell entities are strictly for referencing field activity and provide a consistent
location association with all parts of the greater site. They are numbered from 1 to 200 beginning
in the upper left of the fishnet. Area is the entity used to associate artifacts, baskets, and loci to
an active excavation pit. The Site, the overarching highest level entity, has a one to many
relationship with the Grid Cells, Areas, and Strata. The Areas have a one to many relationship
with the Loci, Baskets, and Artifacts. Likewise each Loci can only belong to a single area, but
may result in multiple baskets and artifacts. A Basket can only be associated with a single Loci,
One to Many
Unique Finds
Foreign Keys
46
but again, many artifacts may be contents within a single basket. An artifact (anything included
in one of the 5 Finds feature classes) can only belong to one Basket, one Loci, one Area, and one
Strata. Finds do not have relationships with one another. The Find records are the core of the
database, each having a single and unique record entry.
The ERD was translated into a working model in ArcMap and resulted in the structure
shown in Figure 16. Feature classes, shapefiles, tables, relationships and topology are illustrated
here along with some resulting layers and imagery, basemap and CAD drawings.
47

Figure 16. Geodatabase contents as viewed in ArcMap
4.2 Populating the Database
It was originally hoped and planned to enter approximately 200 legacy records into the
database along with field data collected in 2015 - enough to visualize, query, and thoroughly test
the design. After extensive time was spent on the tabular data formatting and master spreadsheet
creation outlined in Chapter 3, it was possible to load a total of 25,427 records into the new
geodatabase. These records did not include any spatial information, however, so 65 sets of
48
coordinates were used to load into the majority of these records for testing purposes only,
covering Areas A and T. This provided ample opportunity to demonstrate the power of the x and
y coordinates. In addition, the categorized schema for stratification attaches a depth classification
to each artifact record (which is assumed to be accurate and was included in the legacy data)
making it possible to return specific results as to which artifacts have been excavated from each
level, and in each area. Figure 17 is an example of the tabular data including test spatial
coordinates, ready for loading into ArcMap. Notice that the ‘z’ coordinate column remains
empty, but that the Strata_ID_FK gives each record a depth classification.

Figure 17. Excel data, the Metal entity, prepared for loading into ArcMap
A summary of the data that was used to populate the geodatabase follows. Table 5 breaks
down the categories of data into shapefiles and tables generated, along with the complete number
of records each contains.
Table 4. Records Used to Populate Database
Name of Entity Type of Entity Number of Records Added
Site Polygon Feature Class 1
Area Polygon Feature Class 7
Grid Cell Polygon Feature Class 200
Strata Table 7
Baskets Table 3,340
Loci Table 998
49
Name of Entity Type of Entity Number of Records Added
Ceramic Multipoint Feature Class 17,721
Glass Multipoint Feature Class 310
Metal Multipoint Feature Class 423
Organic Multipoint Feature Class 1,422
Stone Multipoint Feature Class 998
The basemap was imported as a .jpg file and georeferenced using 5 points of field data in
ArcMap. The results depict quite accurate placement of the points, usually within a foot or two
of the actual reading location. Figure 18 gives a site-wide overview and scope of the points that
were captured in the field for georeferencing purposes, while Figure 19 shows which points were
used to georeferenced the image. Figure 20 provides a close-up view of the Iron Age city gate
area and resulting accuracy of the data collection in most cases. Note the rectangular structural
formations, the granary bins, and where the point locations fall at the corners.
50

Figure 18. Points captured site wide in the field for georeferencing purposes
51

Figure 19. The five points used for georeferencing purposes indicated with blue circles
52

Figure 20. Close up of georeferencing points and resulting accuracy at city gate area. Inset blue
rectangle depicts the area of focus. Note the scale.
4.3 Query Results
Chapter 3 outlined several queries that would be run to test the effectiveness and
thoroughness of the geodatabase. These queries represent typical use case scenarios, questions
similar to what might frequently be asked by, and of, the archaeologists. Using the Select by
Attribute feature in ArcMap records meeting only designated criteria were easily sorted for
mapping. Quick turnaround time on these types of inquiries has not been possible in the past.
Tables 6 provides a look at the results of those queries.

53
Table 5. Query Results
Query Results
Query 1 Query the Stone attribute table

Goal Find all of the Flint records in the Stone entity that were excavated
from Strata 6

Query: Material = ‘Flint’ AND Strata_ID_FK = 6
Results Returned 43 out of 916 records

Query 2 Query the Organics attribute table
Goal Find all of the Bone records in the Organic entity that were excavated
on a specific date

Query: Material = ‘Bone’ AND Date = date ‘2014-06-30 00:00:00’
Results Returned 4 out of 1422 records

54
Query 3 Query the Metal attribute table
Goal Find all of the Bronze records in the Metals entity that are classified
as Coin and were excavated from Strata 2

Query: Material = ‘Bronze’ AND Type = ‘Coin’ AND
Strata_ID_FK = 2
Results Returned 18 out of 423 records

Query 4 Query the Glass attribute table
Goal Find all of the Blue records in the Glass entity that are classified as
Fragment

Query: Color = ‘Blue’ AND Cond = ‘Fragment’
Results Returned 46 out of 310 records

55
Query 5 Query the Ceramics attribute table
Goal Find all of the pottery from the Ceramic entity that are classified
as Whole and that were found in Area A and also were excavated
from Strata 6

Query: Condition = ‘Whole’ AND Area _ID_FK = 0 AND
Strata_ID_FK = 6
Results Returned 12 out of 17721 records

Query 6 Query the Ceramics attribute table
Goal Find all of the pottery from the Ceramics entity that are classified
as Whole and comprised of less than 6 pieces

Query: Condition = ‘Whole’ AND Pieces <6
Results Returned 119 out of 17721 records

56
Query 7 Query the Organic attribute table
Goal Find all of the Bone in the Organic entity that was excavated
within a specific range of dates

Query: Material = ‘Bone’ AND Date >= date ‘2001-01-07
00:00:00’ AND Date <= date ‘2001-06-04 00:00:00’
Results Returned 50 out of 1422 records

4.4 Visualizing the Data
The ability to target and combine elements in a query greatly improves the ability to
visualize specific elements. The following maps demonstrate the data output as a visual
representation.
One of the goals of this project was to be able to use the CAD drawings in a spatial
context. Figure 20 provides an example of a CAD drawing that was imported and georeferenced
using field data. The accuracy provides archaeologists with the ability to see the changes of the
excavation areas over time. As each dig year passes, the active areas change considerably so this
can provide a powerful means of reconstructing what has been removed from the ground in prior
years.
57

Figure 21. CAD drawing overlay in ArcMap at 40% transparency - Area A (South). Note the
accuracy in the alignment of the corners of the granary bins at the top of the map
The ability to geographically show artifact find locations, distributions and
concentrations is another frequently needed function. In the following examples (Figures 21
through 24) various artifacts have been added to maps using differing criteria. Using ArcMap, an
archaeologist can get a complete and detailed listing of these objects by opening the attribute
table. Once again, the spatial data was added for purposes of this demonstration only. Artifacts
58
do indeed belong to the areas in which they are shown, however, the exact spatial coordinates
were unknown.

Figure 22. Example visualization of coin data

59

Figure 23. Variation of visualization of coin data
60

Figure 24. Example visualization of varying categories of finds in an area
61

Figure 25. Example visualization of varying categories of finds in an area
4.5 Summary
The results described in this chapter confirm and demonstrate the completion of the
project objectives including the conceptualization, design, creation, testing and implementation
of a spatial database for specific use at the archaeological excavation in Bethsaida, Israel.
Lessons learned and considerations for possible future work and development are discussed in
Chapter 5.
62
Chapter 5: Conclusions
Final thoughts and discussion are presented in this chapter as are ideas for continued work and
enhancement of this project. Section 5.1 reviews what went well and what did not, lessons
learned. Section 5.2 covers plans for sharing the database with the Consortium and getting
individuals trained on simple data entry techniques and how to run queries. Section 5.3
summarizes the importance of the investment in mobile field collection devices for accurate data
gathering, and section 5.4 provides thoughts on future work and development.
5.1 Lessons Learned
The redesigned database resulted in a much simpler and more streamlined data flow. All
entities have a purpose and the relationships are through the use of foreign keys and not separate
entities as existed previously. The time invested on numerous ERD revisions seemed endless but
proved to be well spent once the testing phase got under way and queries were returned smoothly
and with a great deal of accuracy and complexity.
One of the greatest concerns initially was data acquisition, which admittedly, could have
stopped the project in its tracks. It was known going into the final phase of the database
construction that little, if any, communication with the Consortium would be possible as
members would be out in the field during the months of May, June, and July. Fortunately,
communication was started very early on, and all of the necessary requirements were obtained.
The aerial imagery was especially helpful in bringing life to the subject area and creating a sense
of the topography in the region.
Data preparation was another series of complex tasks that consumed a lot of time. Data
from the MS Access database, when exported to Excel tables, required enormous amounts of
manual cleanup. Many additional hours still need to be spent combing through the Finds and
63
properly categorizing the records. Having more in-depth working knowledge of MS Excel would
have been of great benefit. During the query testing phase, inconsistencies were noted such as
some metal objects were classified as pottery, and bone as stone. Coin records did seem to have
the necessary details included (although some were also discovered as improperly categorized),
which is important as they are the only objects found in the archaeological record that contain
dates (mint dates), helping to establish the historical time period of other objects nearby. Due to
the rare nature of some of these finds (many are now in museums), it is extremely important to
be able to show the exact extraction point location in order to validate surrounding finds. This
will be a specific point for presentation to the Consortium and a justification for investing in
handheld field collection devices going forward. It was noted that most other artifact records
contained little or no description at all, making many records somewhat useless except to act as
an item counted.
ArcPad software was new to the author, and the workflow into ArcMap was a bit of a
challenge. Once the ArcPad files were properly associated with the various layers, the .mxd files
rendered the captures as expected, which was extremely gratifying given that this process could
not be tested in the field. It was noted during the data testing phase that GPS logging had not
been enabled on the Trimble during collection. Thus no satellite data was recorded during the
data gathering process, something to be aware of in the future when setting up the systems. See
Appendix A for satellite coverage during hours of data collection for evidence that a minimum of
5 satellites were in place.
A much broader understanding of the excavation as a whole was achieved during the
development of this project. The many components, various responsibilities, and workflows that
have to be maintained both in the field and in the research process require skilled and
64
experienced individuals with a keen attention to detail. Future seasons in the field will be seen
from a different perspective and with a renewed appreciation for all of the mechanisms that work
together.
5.2 Onboarding Consortium Archaeologists
It is anticipated that Consortium archaeologists will be introduced to the new spatial
database in November 2016, when they gather for a common conference. Due to the fact that
many live in remote locations, it may be possible to set up a webinar style overview sooner. If
possible, meetings will be set to work with individual(s) that are local to the San Diego area prior
to November, where they will be shown the process from start to finish. A basic tutorial can be
screen captured, recorded, and distributed via YouTube or web link. The likelihood that grad
students and other volunteers will be doing much of the data input is high, and therefore a short
recording highlighting the importance of steps in the workflow could be very useful. The author
will continue to be involved with maintenance for the immediate future until server based access
is achieved.
5.3 Recommendations for Field Collection
One of the most significant outcomes of this project is the justification for handheld field
collection devices. The demonstrations in Chapter 4 reveal the power of spatial placement in the
archaeological record using test data, but to have extraction points that are nearly exact would
emphasize concentrations and distribution of artifacts visually, creating complete scenes within
specific time periods. The data can then be further queried to show only artifacts that were
gathered during a particular dig season, or to reveal all artifacts belonging to a specific historical
time period. Furthermore, the added feature of the Coded Value Domains ensures that entries are
properly and consistently catalogued for ontological and semantical accuracy.
65
The Trimble GeoXH 3000 that was used for this project was an affordable piece of
equipment (purchased for $1500 USD in 2015) and easy to travel with and configure. This
device is extremely rugged and durable, has a long battery life and submeter accuracy which
could be further improved with the adoption of a range finder antenna. Rotating new field roles
for volunteers who gather data could be established and would allow students as well as
archaeologists to become experienced in the process. It is recommended that one device is
dedicated to each active area for the duration of the season annually.
Field collection also easily captures and exploits the value of the z-coordinate, which
could replace, or be used in addition to, the strata categorization technique that is currently in
use. The Trimble is also capable of capturing a photograph as a part of each point, line, or
polygon, creating an all-inclusive and well-documented object in just a moment’s time.
One of the greatest impacts introduced by the use of the mobile field collection devices is
the accuracy to be gained in the description process. The Coded Value domains that are
programmed into the database allow users a list of choices rather than open ended entry in blank
fields. The potential for increased consistency in data reporting is worth the investment all on its
own.
5.4 Future Development
Currently the application is a desktop application, but collaborative research would be
greatly enhanced via a ArcMap installation on a shared server. This project could then be
accessed by all individuals that are participants of the research team, and could allow data entry
year round. This will be a recommendation that will be passed on to the Consortium members for
consideration.
66
A web application for sharing the Bethsaida excavation data with academics and
archaeology enthusiasts worldwide would serve the purpose of promoting the excavation to
volunteer workers and as a platform for gaining support for continued work at the site. The
Bethsaida web site (on the University of Nebraska, Omaha site) might be able to draw larger
audiences and increase interest through the inclusion of a web map browser-based and
accompanying mobile app highlighting the artifacts.
There are numerous historical CAD drawings that could be digitized, georeferenced, and
added to a catalog in ArcMap. This may be a project for future graduate students and could also
be done year round.
Each of the legacy Finds records has an associated photograph, which could be added to
the record and called up in ArcMap. This would be an arduous process, but could again be an
ongoing part of work completed by graduate students or volunteers interested in learning more
about the objects that have been excavated from this site.
The very nature of this project suggests that continued improvements will be suggested as
it is put to use. It is hoped that other students of GIS who volunteer at this excavation will show
interest in contributing to the continued development and expansion of this project.
67
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Appendix A: Satellite Coverage in the Field
Satellite Coverage for Field Collection Dates
Data retrieved from the web site
InTheSky.org:
https://in-the-sky.org/index.php

June 28, 2015 – 10:00am

June 28, 2015 – 12:00pm

June 29, 2015 – 10:00am

June 29, 2015 – 12:00pm
71

June 30, 2015 – 10:00am

June 30, 2015 – 12:00pm

July 1, 2015 – 10:00am

July 1, 2015 – 12:00pm

72
Appendix B: Geodatabase Tables
Table 6. Topology
Topology
Entity Rule Description
Grid_Cell Must not overlap Grid cells must not overlap

Table 7. Relationship Classes
Relationship Classes
Entity/Attribute Entity/Attribute Relationship Comments
Site_ID Area_ID One-to-many One Site contains
many Areas
Site_ID Strata_ID One-to-many One Site contains
many Strata
Area_ID Loci_ID One-to-many One Area may
contain many Loci
Loci_ID Basket_ID One-to-many One Loci may
contain many
Baskets

Table 8. Domains
Domains
Entity Domain Name Domain Type Domain Values Description
Ceramic/Glass Condition Coded Value Whole
Fragment
Scatter
Condition of
artifact found
Glass Color Coded Value Clear
Green
Blue
Yellow
Red
Other
Color of glass
found
Metal Material Coded Value Copper
Bronze
Silver
Gold
Iron
Lead
Type of metal
found
73
Domains
Entity Domain Name Domain Type Domain Values Description
Metal Type Coded Value Coin
Jewelry
Tool
Other
Object’s
intended use
Stone Material Coded Value Basalt
Flint
Limestone
Stone
Type of stone
found
Strata Strata_No Coded Values 1, 2, 3, 4, 5, 6, 7, 8 Sediment layer
representing a
distinct period
of time
Area Area_Name Coded Values 0, 1, 2, 3, 4, 5 Active dig areas

Table 9. Site Entity
Site - Polygon
Field Name Description Data Type Null Values Unique Keys
Site_ID Site ID Text NotNull Unique Primary
Site_Name Site Name Text NotNull
Latitude Latitude Long Int. Null
Longitude Longitude Long Int. Null
Notes Notes Text Null

Table 10. Grid Cell Entity
Grid Cell - Polygon
Field Name Description Data Type Null Values Unique Keys
Grid_Cell_ID Grid Cell ID Text NotNull Unique Primary
Grid_Cell_No Grid Cell
Number
Short Int. NotNull
Notes Notes Text Null

74
Table 11. Area Entity
Area - Polygon
Field Name Description Data Type Null Values Unique Keys
Area_ID Area ID Text NotNull Unique Primary
Area_Name Area Name Text NotNull
Length Length in
Meters
Short Int. Null
Width Width in
Meters
Short Int. Null
Opened Date Opened Date Null
Notes Notes Text Null

Table 12. Loci Entity
Loci - Table
Field Name Description Data Type Null Values Unique Keys
Loci_ID Loci ID Text NotNull Unique Primary
Loci_No Loci Number Text NotNull
Length Length in
Meters
Short Int. Null
Width Width in
Meters
Short Int. Null
Opened Date Opened Date Null
Stratum Stratum No. Short Int. Null
Level Elevation
Reading
Short Int. Null
Notes Notes Text Null
Area_ID_FK Area ID Text NotNull Foreign

Table 13. Basket Entity
Basket - Table
Field Name Description Data Type Null Values Unique Keys
Basket_ID Basket ID Text NotNull Unique Primary
Basket_No Basket
Number
Text NotNull
Date Date Collected Date Null
Notes Notes Text Null
Loci_ID_FK Loci ID Text NotNull Foreign
Area_ID_FK Area ID Text NotNull Foreign
75

Table 14. Strata Entity
Strata - Table
Field Name Description Data Type Null Values Unique Keys
Strata_ID Strata ID Text NotNull Unique Primary
Strata_No Strata Number Text NotNull
Description Description Text Null
Notes Notes Text Null

Table 15. Ceramic Entity
Ceramic – Point/Multipoint Feature Class
Field Name Description Data Type Null Values Unique Keys
Ceramic_ID Ceramic ID Text NotNull Unique Primary
Cond Condition Coded
Value
Null
Pieces No. Pieces
Found
Text NotNull
x X coordinate Long Int. Null
y Y Coordinate Long Int. Null
z Z Coordinate Long Int. Null
Date Date Found Date Null
Notes Notes Text Null
Area_ID_FK Area ID Text Null
Loci_ID_FK Loci ID Text Null
Basket_ID_FK Basket ID Text Null
Strata_ID_FK Strata ID Text Null

Table 16. Glass Entity
Glass – Point/Multipoint Feature Class
Field Name Description Data Type Null Values Unique Keys
Glass_ID Glass ID Text NotNull Unique Primary
Pieces No. Pieces
Found
Text NotNull
Material Material Text Null
Color Color Coded
Value
Null
76
Glass – Point/Multipoint Feature Class
Field Name Description Data Type Null Values Unique Keys
Cond Condition Coded
Value
Null
x X coordinate Long Int. Null
y Y Coordinate Long Int. Null
z Z Coordinate Long Int. Null
Date Date Found Date Null
Notes Notes Text Null
Area_ID_FK Area ID Text Null
Loci_ID_FK Loci ID Text Null
Basket_ID_FK Basket ID Text Null
Strata_ID_FK Strata ID Text Null

Table 17. Metal Entity
Metal – Point/Multipoint Feature Class
Field Name Description Data Type Null Values Unique Keys
Metal_ID Metal ID Text NotNull Unique Primary
Material Material Coded
Value
NotNull
Type Type Coded
Value
Null
x X coordinate Long Int. Null
y Y Coordinate Long Int. Null
z Z Coordinate Long Int. Null
Date Date Found Date Null
Notes Notes Text Null
Area_ID_FK Area ID Text Null
Loci_ID_FK Loci ID Text Null
Basket_ID_FK Basket ID Text Null
Strata_ID_FK Strata ID Text Null

77
Table 18. Organic Entity
Organic – Point/Multipoint Feature Class
Field Name Description Data Type Null Values Unique Keys
Organic_ID Organic ID Text NotNull Unique Primary
Desc Description Text Null
Cond Condition Text Null
x X coordinate Long Int. Null
y Y Coordinate Long Int. Null
z Z Coordinate Long Int. Null
Date Date Found Date Null
Notes Notes Text Null
Area_ID_FK Area ID Text Null
Loci_ID_FK Loci ID Text Null
Basket_ID_FK Basket ID Text Null
Strata_ID_FK Strata ID Text Null

Table 19. Stone Entity
Stone – Point/Multipoint Feature Class
Field Name Description Data Type Null Values Unique Keys
Stone_ID Stone ID Text NotNull Unique Primary
Pieces No. Pieces
Found
Text NotNull
Material Material Coded
Value
Null
Desc Description Text Null
Cond Condition Text Null
x X coordinate Long Int. Null
y Y Coordinate Long Int. Null
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Abstract (if available)

Abstract Annual fieldwork at the Bethsaida, Israel archaeological excavation project yields an unwieldy amount of data that have historically been processed and managed via paper-based means and have no associated spatial data. There has been little adoption of modern technology applications to manage this data, even in recent years. The programming objective of this project involved designing and implementing an intra-site, archaeological specific, spatial database for collecting and managing excavation artifacts. A project-based approach was taken toward improved digital data management, tracking, mapping, and visualization in the examination of temporal and spatial archaeological data, thus facilitating the ability for archaeologists to gain new and otherwise undetected insights through spatial pattern analysis. Legacy data, along with data collected via a handheld Global Position System (GPS) device in 2015, aided in establishing the dataset parameters, feature classes, attributes, and domains of the database. This excavation site offered a unique opportunity to explore the space-time continuum through numerous human settlements evidenced by the vertical archaeological record representing the 10th century before Common Era (BCE) through the 1st century Common Era (CE). Visualization of the distribution, concentrations, and spatial relationships of material culture to settlement groups potentially illustrates social trends and cultural practices over the centuries. Data recording will become more consistent and efficient through structured, predefined categories and attributes, bringing greater organization via ontological and semantical consistency. Field collection will be further streamlined and enhanced by the adoption of handheld devices working congruently as an extension of the new geodatabase, collecting artifact information and spatial data, including stratification, in real time. Ongoing research and global collaborative opportunities become possible with the geodatabase, and greater cohesion amongst the diverse excavation team is enhanced. Archaeologists are further able to forecast areas for future excavation based on the visualizations.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Burrows, Cynthia L. (author)

Core Title Developing an archaeological specific geodatabase to chronicle historical perspectives at Bethsaida, Israel

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Publication Date 09/12/2016

Defense Date 08/09/2016

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

Tag archaeology,Bethsaida,geodatabase,handheld,intra-site,Israel,Mobile,OAI-PMH Harvest,ontology,semantics,spatial database,stratigraphy,Visualization

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Loyola, Laura (committee member), Swift, Jennifer (committee member), Yang, Wei (committee member)

Creator Email cburrows@usc.edu,cyd.burrows@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-302895

Unique identifier UC11280569

Identifier etd-BurrowsCyn-4784.pdf (filename),usctheses-c40-302895 (legacy record id)

Legacy Identifier etd-BurrowsCyn-4784.pdf

Dmrecord 302895

Document Type Thesis

Format application/pdf (imt)

Rights Burrows, Cynthia L.

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