University of Southern California Dissertations and Theses

GIS data curation and Web map application for La Brea Tar Pits fossil occurrences in Los Angeles, California

(USC Thesis Other)

GIS data curation and Web map application for La Brea Tar Pits fossil occurrences in Los Angeles, California

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content GIS DATA CURATION AND WEB MAP APPLICATION FOR LA BREA TAR PITS

FOSSIL OCCURRENCES IN LOS ANGELES, CALIFORNIA

by

Kacey Johnson Pham

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)

Manuscript Completed: September 2015
Degree Conferral: December 2015

Copyright 2015 Kacey Johnson Pham

ii
DEDICATION
I dedicate this thesis to my husband, John, my parents, Guerry and Diane, and my sister, Angie,
as well as my dog, Pinky, for their love and encouragement.

iii
ACKNOWLEDGMENTS
I am grateful to the Page Museum staff for allowing me to use their fossil data and for lending
their paleontological expertise to help me develop this project. Thanks especially to Aisling
Farrell for her friendship and mentorship as well as for spending so much time reviewing the
application and database. I would also like to give a special thank you to my friend and colleague
at International Medical Corps, Nadezda Sekularac, who lent her SQL expertise to help me with
some of the database queries for this project. I would also like to thank the rest of the
Information Technology staff at International Medical Corps for cheering me on as I approached
the completion of my thesis. Lastly, I would like to thank the amazing faculty at USC, especially
my advisor Dr. Jennifer Swift, and my committee for their encouragement, knowledge and
support.

iv
TABLE OF CONTENTS
DEDICATION ii
ACKNOWLEDGMENTS iii
LIST OF TABLES viii
LIST OF FIGURES x
LIST OF ABBREVIATIONS xiii
ABSTRACT xiv
CHAPTER 1: INTRODUCTION 1
1.1 Project Overview 3
1.2 Motivation 4
1.3 Methodological Overview 7
1.4 Thesis Structure 9
CHAPTER 2: BACKGROUND AND RELATED WORK 10
2.1 Fossil Sites of the La Brea Tar Pits 10
2.1.1 La Brea Tar Pits Background 10
2.1.2 La Brea Tar Pits Literature Review 13
2.1.3 Fossil Collection Background 14
2.2 Review of Existing Museum Databases and Applications 15
2.2.1 Existing Database Review 15
2.2.2 Existing Museum Collection Application Platforms and Functionality 19
CHAPTER 3: METHODOLOGY 24
3.1 Method Overview 24

v
3.2 Data Sources 26
3.2.1 Spatial Data 26
3.2.2 Non-Spatial Data 28
3.3 Data Background and Processing Methods 29
3.3.1 Specimen Data 34
3.3.2 Spatial Data Accuracy 35
3.3.2.1 Fitness of Use for Spatial Analysis 37
3.4 Development Approach and User Requirements for the Web GIS Application 39
3.4.1 Development Approach 39
3.4.2 User Requirements 39
3.4.2.1 Functional Requirements 39
3.4.2.2 Nonfunctional Requirements 40
3.5 ArcGIS Server and ArcGIS Online Application Development 40
3.5.1 Development Environment 40
3.5.1.1 Web GIS Software and Environment 40
3.5.1.2 ArcGIS Server Environment 41
3.5.1.3 ArcGIS Online Environment 41
3.5.2 ArcGIS Server Development 42
3.5.3 ArcGIS Online Development 42
3.5.4 Web Application Requirements Review 50
3.6 Chapter Summary 50
CHAPTER 4: RESULTS 52

vi
4.1 Data Model: Geodatabase Diagram and Schema 52
4.1.1 Geodatabase Tables, Fields, and Properties 56
4.1.2 Data Model Review 56
4.1.3 Database Update User Guide 56
4.2 Web Application Screenshots and Functionality 57
4.3 Web Application User Guides 57
4.4 Use Cases 58
4.5 Field Data Collection Geodatabase Prototype 58
4.5.1 Field Data Collection Geodatabase Diagram and Schema 59
4.6 Chapter Summary 63
CHAPTER 5: DISCUSSION AND CONCLUSIONS 64
5.1 Long-Term Geodatabase and Application Maintenance 64
5.2 Future Work 65
5.2.1 Interactive Museum Exhibit 65
5.2.2 Field Data Collection 66
5.2.3 Three-dimensional (3D) Data Visualization 66
5.3 Next Steps 67
REFERENCES 68
APPENDIX A: REQUIRED RESEARCH SKILLS 71
APPENDIX B: DATA SOURCES AND DESCRIPTIONS 72
APPENDIX C: KE EMU DATA 74
APPENDIX D: FOSSIL POSITIONAL DATA FOR 3D DISPLAY 76

vii
APPENDIX E: GEODATABASE CREATION NOTES AND SQL QUERIES 77
APPENDIX F: SQL QUERIES FOR SPECIMEN SUMMARY TABLES 82
APPENDIX G: REQUIREMENTS FOR THE WEB GIS APPLICATION 89
APPENDIX H: WEB APPLICATION GIS SERVICES 92
APPENDIX I: LA BREA TAR PITS GEODATABASE 93
APPENDIX J: LA BREA TAR PITS FIELD DATA COLLECTION GEODATABASE 97
APPENDIX K: GEODATABASE UPDATE USER GUIDE 100
APPENDIX L: WEB GIS APPLICATION USER GUIDE 102
APPENDIX M: WEB GIS APPLICATION UPDATE USER GUIDE 111
APPENDIX N: RECOMMENDATIONS FOR TECHNOLOGY TRANSFER 114

viii
LIST OF TABLES
Table A-1 Required Research Skills ............................................................................................. 71
Table B-1 Data Sources and Descriptions .................................................................................... 72
Table C-1 KE EMu Extract - Ecatalogue ..................................................................................... 74
Table C-2 KE EMu Extract - SitSiteR .......................................................................................... 75
Table D-1 Example of fossil positional data before transforming for 3-dimensional display ...... 76
Table D-2 Example of fossil positional data after transforming for 3-dimensional display ........ 76
Table G-1 Functional Requirements ............................................................................................. 89
Table G-2 Nonfunctional Requirements ....................................................................................... 89
Table G-3 Functional Requirements Review ................................................................................ 90
Table G-4 Nonfunctional Requirements Review .......................................................................... 90
Table G-5 Feedback and Changes Requests - July 13, 2015 Review ........................................... 91
Table I-1 Relationship Class ......................................................................................................... 93
Table I-2 LaBrea_FossilLocalities Entity ..................................................................................... 93
Table I-3 HC_PitDates .................................................................................................................. 93
Table I-4 Specimen_Site_Catalogue Entity .................................................................................. 94
Table I-5 SumbyDeposit (Joined to LaBrea_FossilLocalities Feature Class) .............................. 95
Table I-6 Boreholes_USGS Entity ................................................................................................ 96
Table I-7 USGSBoreholesDescription Entity ............................................................................... 96
Table J-1 Topology ....................................................................................................................... 97
Table J-2 Domains ........................................................................................................................ 97
Table J-3 Relationship Class ......................................................................................................... 97
Table J-4 Surveyor Entity ............................................................................................................. 98

ix
Table J-5 Spit Entity ..................................................................................................................... 98
Table J-6 Pit Deposit Entity .......................................................................................................... 98
Table J-7 Grid Entity .................................................................................................................... 99
Table J-8 Grid Cell Entity ............................................................................................................. 99
Table J-9 Fossil Element Entity .................................................................................................... 99

x
LIST OF FIGURES
Figure 1 La Brea Tar Pits location map .......................................................................................... 1
Figure 2 Areas with maximum fossil concentration and fossil sites that have been discovered
during construction activities in the vicinity of Hancock Park (Shaw & Quinn, 1986) ................. 6

Figure 3 1913-1915 fossil excavations at the La Brea Tar Pits (Page Museum 2015c) ............... 11
Figure 4 Project 23 crated fossil deposits (Page Museum 2015c) ................................................ 12
Figure 5 Example string laid out in 1x1 m grid at Pit 91 excavation site (Shaw 1982) ............... 14
Figure 6 KE EMu taxonomy module (KE Software 2015c) ......................................................... 16
Figure 7 KE EMu ArcExplorer report (KE Software 2015d) ....................................................... 17
Figure 8 MioMap data structure (University of California 2015) ................................................ 18
Figure 9 Is the World Full or Empty (Smithsonian 2015) ............................................................ 20
Figure 10 VertNet showing specimen from the Royal Ontario Museum (National Science
Foundation 2015) .......................................................................................................................... 21

Figure 11 MioMap: Miocene Mammal Mapping Project (University of California 2015) .......... 22
Figure 12 La Brea Tar Pits web GIS application methodology .................................................... 25
Figure 13 Scanned TIFF of original paper survey map of Hancock Park ................................... 27
Figure 14 Esri Basemap and boreholes (green circles) used to georeference the survey map ..... 30
Figure 15 Below depth (BD), northing (N), and westing (W) measurements taken for a
mammal femur (Shaw 1982) ........................................................................................................ 32

Figure 16 Example grid Layout: Project 23 Deposit 1 ................................................................. 33
Figure 17 Smilodon fatalis long bone specimens classified by ontogenetic age .......................... 34
Figure 18 A parking lot was built over Oil Lake and several pits during the 1970s .................... 36
Figure 19 Clustering of fossil pits and fossil excavation sites ...................................................... 38
Figure 20 Flow Diagram of ArcGIS development process from raw data to web GIS
application ..................................................................................................................................... 41

xi
Figure 21 Custom symbols ........................................................................................................... 42
Figure 22 Search by Deposit # Query widget configuration ........................................................ 45
Figure 23 Search for Most Abundant Deposits Query widget configuration ............................... 45
Figure 24 Search for Deposit by Most Abundant Species Query widget configuration .............. 46
Figure 25 Search for Deposit by Most Abundant Element Query widget configuration ............. 46
Figure 26 Number of Specimens by Deposit Chart widget configuration .................................... 47
Figure 27 Total Number of Mammals, Reptiles, Birds, and Amphibians in Collection
(associated with a deposit) Chart widget configuration ................................................................ 48

Figure 28 Number of Mammals by Deposit Chart widget configuration ..................................... 48
Figure 29 Number of Reptiles by Deposit Chart widget configuration ........................................ 49
Figure 30 Number of Birds by Deposit Chart widget configuration ............................................ 49
Figure 31 Number of Amphibians by Deposit Chart widget configuration ................................. 50
Figure 32 La Brea Tar Pits geodatabase ERD detailed view ........................................................ 53
Figure 33 Enlarged view of main structure of the La Brea Tar Pits geodatabase ERD showing
the relationships between the point feature classes and tables ..................................................... 55

Figure 34 The final version of the La Brea Tar Pits web application as of August 2015 ............. 57
Figure 35 La Brea Tar Pits field data collection geodatabase ERD detailed view featuring all
Fossil Element Subtypes ............................................................................................................... 60

Figure 36 Enlarged view of main structure of the La Brea Tar Pits field data collection
geodatabase ERD showing the relationships between attributes and the feature class type of
each entity ..................................................................................................................................... 61

Figure L-1 How to expand the Layer List .................................................................................. 102
Figure L-2 The Layer List after expanding in Step 3a ................................................................ 103
Figure L-3 Example zoomed in area of the map focusing on a cluster of pits ........................... 103
Figure L-4 Example pop-up contents for a pit perimeter showing the bottom elevation
recorded on the 1913 survey map ............................................................................................... 104

xii
Figure L-5 How to scroll to the next layer’s pop-up when several map layers are selected
at once ......................................................................................................................................... 104

Figure L-6 Example pop-up contents for Hancock Collection deposit 3 (HC - 3) ..................... 105
Figure L-7 Example pop-up contents for Hancock Collection deposit 3 (HC - 3) in detail
shown the top of the pop-up contents (a) and the bottom of the pop-up contents (b) ................ 105

Figure L-8 Example radiometric dating table appears after clicking “Show Related Records”
for Pit 3 ....................................................................................................................................... 106

Figure L-9 Sorting of Pit Dates by “Number of Dates” ascending (a) and descending (b)
order ............................................................................................................................................ 106

Figure L-10 Closing the attribute table ....................................................................................... 107
Figure L-11 Queries .................................................................................................................... 107
Figure L-12 Query example ........................................................................................................ 108
Figure L-13 Example query results and pop-up display results ................................................. 108
Figure L-14 List of available charts ............................................................................................ 109
Figure L-15 Example Number of Specimens by Deposit chart with Deposit HC – 16
highlighted in the chart and on the map ...................................................................................... 110

Figure M-1 Web AppBuilder editing interface Theme Tab ....................................................... 111
Figure M-2 Web AppBuilder editing interface Map Tab ........................................................... 112
Figure M-3 Web AppBuilder editing interface Widget Tab ....................................................... 112
Figure M-4 Web AppBuilder editing interface Attribute Tab .................................................... 113

xiii
LIST OF ABBREVIATIONS
AGOL ArcGIS Online
API Application Programming Interface
APRMI ArchaeoPaleo Resource Management, Inc
CSS Cascading Style Sheet
CSV Comma Separated Variable
GIS Geographic Information System
GIST Geographic Information Science and Technology
HC Hancock Collection
HTML Hypertext Markup Language
LACMA Los Angeles County Museum of Art
MXD Map Exchange Document
NHM Natural History Museum of Los Angeles County
P23 Project 23
REST Representational State Transfer
SSI Spatial Sciences Institute
URL Uniform Resource Locator
USC University of Southern California
USGS U.S. Geological Survey
WGS84 World Geodetic System 1984

xiv
ABSTRACT
The occurrence of asphaltic fossil localities within and surrounding the Page Museum at the La
Brea Tar Pits in Los Angeles, California is extensive and has been recorded for decades as non-
spatial data collected in a non-spatial database. The motivation for this project stemmed from the
author’s time as a volunteer at the Page Museum over the course of one year. The Page museum
staff requested an efficient way to cartographically display fossil data to assist staff with
visualizing the taphonomy of fossils. At the time of this study, this thesis is the first GIS project
that the Page Museum had ever supported for mapping of fossils. Most current literature
describing fossil-related web GIS applications reports data displayed at small-scales, and exact
locations of fossils are not generally provided through the applications. The main objectives of
this thesis project were to design and implement a fossil excavation spatial database, digitally
curate data that previously only existed in paper form, display fossil data in an interactive web
GIS application, and develop a framework to support spatial analysis and live data feeds of fossil
data in the future. As part of this thesis project, known fossil localities were digitized from a La
Brea Tar Pits survey map maintained since 1913. The fossil specimen location records from the
museum’s existing database were then joined to those newly digitized features to support the
development of the spatial database of existing fossil localities within the park. The fossil
features contained in the spatial database were then published to the web through the web GIS
application also developed as part of thesis research, as a proof of concept intended to guide
future Page Museum GIS projects. Visualizing the location of fossils is intended to help better
communicate the paleontology of the La Brea Tar Pits to the museum staff, and eventually to the
general public. Lastly, it is anticipated that this web GIS application will contribute to the current
literature on documentation and visualization of extensive fossil deposits.

1
CHAPTER 1: INTRODUCTION
In the heart of Los Angeles the vast paleontological treasure of the George C. Page Museum of
La Brea Discoveries, the La Brea Tar Pits, can be found nestled between high-rise buildings and
busy streets (Page Museum 2015a). This pre-historic treasure consists of an exhumed collection
of over 3 million fossils, and possibly millions more yet to be unearthed below the surface within
the local asphalt and asphalt-rich sediments (Figure 1).

Figure 1 La Brea Tar Pits location map

2
The mission of the George C. Page Museum of La Brea Discoveries and the Natural
History Museum (which manages the Page Museum) is to protect and study the fossils found at
the La Brea Tar Pits (Page Museum 2015b). Since the early 1900’s, paleontologists and
volunteers at the museum have diligently recorded the locations, positions, and other relevant
characteristics such as, age, gender, and condition of the fossils excavated at the La Brea Tar
Pits. The fossil data is continuously compiled by museum staff into the non-spatial database KE
EMu (KE Software 2015a), software, which is currently used by museums across the world (KE
Software 2015b). At the time this study was initiated the location data associated with this non-
spatial database was contained mostly on a single copy of a historical paper map maintained
since 1913 (Noble 1913). It would be a profound loss if this paper map were to be damaged or
lost. Thus, two of the author’s primary goals were to create a spatial database design and
implementation for the museum collection staff to support spatial visualization of the collections
in GIS-related projects in a web application, and to preserve the geospatial data contained within
the paper map within the new spatial database. Additionally, it is anticipated that spatial
visualization of fossil excavation data may aid daily curation activities by helping staff easily
view and check their data to spot trends and outliers. Significantly, this project has the distinction
of being the first spatial database and interactive web mapping application of the La Brea
localities and specimen data ever created (Aisling Farrell, per comm, 2013).
This chapter provides a description of this thesis project scope, motivation, and
methodology for creating the La Brea Tar Pits web GIS application. Section 1.1 contains an
overview of the web application project, and section 1.2 presents the motivation for the web
application. Section 1.3 outlines the methodology for building the La Brea Tar Pits application,

3
while section 1.4 summarizes the chapters and structure of this remainder of this thesis
manuscript.
1.1 Project Overview
This thesis study consisted of two initiatives aimed specifically at supporting future La Brea Tar
Pits fossil data collection and visualization by the Page Museum staff: (1) an Esri geodatabase
was designed and implemented for efficient compilation of recorded locations of the La Brea Tar
Pits fossil pits and individual fossil samples within specific pits, and (2) utilizing Esri ArcGIS
version 10.2.1
1
, the fossil records within the new geodatabase were published as feature layers
via Esri ArcGIS Server 10.2.1
2
and included in an innovative, interactive pilot web GIS mapping
application.
At present, the geodatabase created for this project can be updated from the museum
staff’s current KE EMu database via SQL queries, providing the added benefit of digitally
spatializing the fossil locations. The spatial support provide by a geodatabase allows fossils to be
easily viewed in GIS layers as two dimensional (2D) or three-dimensional (3D) representations
that provide a new way for the museum paleontologists to answer important questions about the
taphonomy of the fossils and the shape of a given fossil deposit, as well as to easily track the
extent of their subsurface excavations. In the context of this thesis, taphonomy is defined as how
the individual specimens were fossilized, meaning the environmental conditions that affected the
preservation (Shipman 1981, Spencer et al 2003).

1
Esri. 2015. “Mapping_and_visualization_in_ArcGIS_for_Desktop.” Esri. Accessed August 22, 2015.
http://resources.arcgis.com/en/help/main/10.2/#/Mapping_and_visualization_in_
ArcGIS_for_Desktop/018q00000004000000/.

2
Esri. 2015. “What is ArcGIS for Server.” Esri. Accessed August 22, 2015. http://resources.
arcgis.com/en/help/main/10.2/#/What_is_ArcGIS_for_Server/01540000037p000000/.

4
Each record in the geodatabase corresponds to a unique fossil record. Each fossil record
can be published as a point on a web map within the pilot web GIS mapping application, which
also contains an overview of all fossil pits in the Page Museum park as well as other important
local data, including topography, water bodies, streams, and gas and oil vents within the park in
1913 (Turner 2006, Quinn et al 2000, Noble 1913). Many older pits, water bodies, streams, and
oil and gas vents today covered by grass, buildings, and parking lots can now be visualized in the
web GIS mapping application. In its current stage of development, the main function of the web
GIS application is to allow a museum staff user to select a pit and view information pertaining to
the pit, such as age, number of fossils, and types of fossils, alongside a 3D visualization of the
orientation of the fossil specimens before excavation. In the context of this thesis, a pit is defined
as an asphaltic fossil deposit excavation and the terms pit and fossil excavation are
“synonymous” (Woodard and Marcus 1973).
1.2 Motivation
The main motivation for development of a spatial database (geodatabase) and the pilot
web GIS application of the La Brea Tar Pits is to support the future development of a web GIS
application that will keep visitors coming back to the museum by encouraging hands-on
interaction with the application through visual exploration of the pits around the museum
grounds. The Page Museum exhibits currently have no digital content. In order to appeal to
future generations of visitors, it is important for the museum to start using the power of digital
content such as interactive web maps. Moreover, as previously stated, this project has the
distinction of being the first attempt at creating a spatial database (geodatabase) of fossil
locations connected to a web GIS application (Aisling Farrell, per comm, 2013).

5
It is intended for this pilot web GIS application to be a starting point for future web
applications that will be connected to a geodatabase updated nightly with the latest La Brea Tar
Pit fossil finds from a given day. The intent is that this web application could eventually provide
visitors with daily updates on the museum’s predominantly 1970’s era exhibits. The web
application would be a method to communicate to the museum visitors the large numbers of
fossils found on a daily basis. The main motto of the museum found on the Page Museum of La
Brea Tar Pits home page is “discoveries made daily” (Page Museum 2015a). So with every visit
it may be observed that the count of fossils has increased and view where the latest fossils came
from in the live web GIS application. The timely communication of the La Brea Tar Pit finds to
the public is very important to invigorate the public’s interest in paleontology and the progress
that this field of study has contributed to the understanding of climate change (Akersten, Shaw,
and Jefferson 1983). The museum staff believe that future public, interactive GIS-based exhibits
would greatly encourage museum visitors’ interest in paleontology, geology, and even GIS,
which ultimately might help the museum gain more funding opportunities from donors.
In turn, bringing awareness of the presence of the fossils in and around the park will
emphasize the need to preserve these fossils. The web GIS application will show visitors that
fossils can be found as far as a half-mile away in the heart of Los Angeles (Figure 2).

6

Figure 2 Areas with maximum fossil concentration and fossil sites that have been discovered
during construction activities in the vicinity of Hancock Park (Shaw & Quinn, 1986)

Increasing public awareness of fossil locations in and surrounding the park will help ensure that
the well-preserved asphalt covered fossils of the La Brea Tar Pits are protected from damage
inherent at construction sites near fossil localities. As previously stated, the fossils of Rancho La
Brea provide significant information on the climate in Los Angeles during the Pleistocene Epoch
that is important for the understanding of past and present climates (Akersten, Shaw, and
Jefferson 1983). For example, using the fossils scientists can conduct radiocarbon dating studies
and isotopic studies to learn about the diet of the La Brea mammals, which offers important clues
about the climate during the Pleistocene Epoch (Coltrain et al. 2004).
Another knowledge gap that this thesis study addresses includes the lack of development
of large-scale GIS applications for visualizing fossil finds published in the literature. This web
GIS application is relatively large scale and high resolution compared with previously published

7
studies, the intent being that fossil occurrence information can be visualized at the meter and
centimeter scale.
1.3 Methodological Overview
The geodatabase and web GIS application were created using the Esri ArcGIS suite of software
including ArcGIS Desktop 10.2.1
3
, ArcGIS Server 10.2.1
4
, ArcGIS Online
5
, and ArcGIS for
JavaScript API
6
. The geodatabase was designed and developed using Microsoft SQL Server
2008 R2
7
.
The schema for the geodatabase was developed based on the museum’s collections
database KE EMu (KE Software 2015a) and was minimally changed so that geodatabase updates
from KE EMu can be easily made. It was a requirement of the Page Museum staff that the KE
EMu database be kept as the main database and the geodatabase simply be an extension of the
existing database. In addition, a museum staff member reviewed the database schema that was
developed as part of this thesis effort.
The geodatabase creation step consisted of database schema development, database
creation, fossil data editing, fossil data loading, and preparation for publishing fossil records as
feature services to be integrated into the web GIS application. The museum’s 1913 paper survey

3
Esri. 2015. “Mapping_and_visualization_in_ArcGIS_for_Desktop.” Esri. Accessed August 22, 2015.
http://resources.arcgis.com/en/help/main/10.2/#/Mapping_and_visualization_in_
ArcGIS_for_Desktop/018q00000004000000/.

4
Esri. 2015. “What is ArcGIS for Server.” Esri. Accessed August 22, 2015. http://resources.
arcgis.com/en/help/main/10.2/#/What_is_ArcGIS_for_Server/01540000037p000000/.

5
Esri. 2015. “ArcGIS Online Help.” Esri. Accessed August 22, 2015. http://doc.arcgis.com/en/ arcgis-online/.

6
Esri. 2015. “ArcGIS API for JavaScript.” Esri. Accessed August 22, 2015. https://developers.
arcgis.com/javascript/.

7
Microsoft. 2015. “SQL Server 2008 R2.” Microsoft Developer Network. Accessed August 29, 2015.
https://msdn.microsoft.com/en-us/library/hh278297(v=sql.10).aspx/.

8
map, which is aged and showing significant signs of wear and tear, was scanned and manually
digitized, then the resulting features were joined to the geodatabase. Also, geographic
coordinates for the most recent Project 23 excavations of the Los Angeles County Museum of
Art parking garage excavations were extracted from a pdf report authored by ArchaeoPaleo
Resource Management, Inc. (Turner 2006). The Project 23 data was converted from the pdf
report first to a .csv format table, then to a geodatabase table, and finally to a point feature class
then merged with the manually digitized data. Next, the museum provided an extract of specimen
data from the KE EMu database as a pilot data extract for this thesis project. The data extract
was imported into SQL Server and transformed using SQL database queries before loading into
ArcMap. Supporting tables such as radiometric dates and borehole geologic data were also
loaded into the ArcMap map project (mxd), converted into geodatabase tables, then were related
to the feature classes manually digitized from the paper map.
Next, the mxd was published as a map service using ArcGIS Server 10.2.1. Once
published, the web GIS application development was accomplished by editing and customizing
an ArcGIS Online web map and application template. The web GIS application template utilized
for developing this study’s web GIS application was an ArcGIS Online Web AppBuilder
template, a JavaScript-based application
8
.
All customization of the web GIS application followed standards outlined after
interviews with museum staff and research on La Brea fossils and fossil collections methodology
(Aisling Farrell, per comm, 2015, Shaw 1982) in order to create basic requirements for a fossil
database and web GIS application to be used in this museum. After the web application was

8
Esri. 2015. “Web AppBuilder for ArcGIS.” Esri. Accessed August 22, 2015. http://doc.arcgis. com/en/web-
appbuilder/.

9
finalized, museum staff users were given the opportunity to test the application and provide
feedback before the final version was released.
1.4 Thesis Structure
The remainder of this thesis consists of four additional chapters. Chapter 2, Background and
Related Work, provides a review of the La Brea Tar Pits’ history and information regarding
fossil excavations and also of other web-based applications supported by the Page Museum as
well as other similar studies. Chapter 3, Methodology, describes in detail the methods used for
developing both the geodatabase and the web GIS application, while Chapter 4, Results, presents
the outcome and describes the user guides developed for the web GIS application as well as for
updating the geodatabase. Lastly, Chapter 5, Conclusions and Future Work, describes the
successes and challenges encountered during the project and explains the anticipated future
direction of the La Brea Tar Pits geodatabase and web GIS application development.

10
CHAPTER 2: BACKGROUND AND RELATED WORK
This chapter describes the background and related work reviewed before developing the La Brea
Tar Pits geodatabase and web GIS Application. Section 2.1 gives an overview of the La Brea Tar
Pits fossil localities. Section 2.2 provides a description of exiting fossil and museum-centric
databases found in the current literature. Lastly, section 2.3 describes the related web
applications that were reviewed to establish the value and possible functionality such
applications could add to museum database and fossil web application development
communities.
2.1 Fossil Sites of the La Brea Tar Pits
This section describes the history of the La Brea Tar Pits and provides background on fossil
collection at the Page Museum. Section 2.1.1 reviews the history of the La Brea Tar Pits
excavations. Section 2.1.2 gives a concise overview of the literature sources considered most
pertinent to understanding the content of the KE EMu database fossil records accumulated to
date. Section 2.1.3 provides background on fossil collection methodology currently in use at the
museum.
2.1.1 La Brea Tar Pits Background
The La Brea Tar Pits excavations began in 1913 (Figure 3) after the area was first discovered to
have a rich deposit of asphalt (Stock and Harris 1992). During initial asphalt excavations, large
fossils of startling proportions were discovered, including eccentricities such as mammoths and
saber-toothed cats with 6-inch long teeth. After the significance of the discovery was understood,
excavations soon began to recover fossils from the asphalt. The La Brea Tar Pits still continues
to be excavated to this day and the richness of fossils still to be uncovered is assumed to be vast.

11

Figure 3 1913-1915 fossil excavations at the La Brea Tar Pits (Page Museum 2015c)
The latest excavations are called Project 23 (also referred to as P23) and consist of the 23
asphalt deposits that were discovered in 2005 while excavating to build an underground parking
garage commissioned by the Los Angeles County Museum of Art (LACMA) (Aisling Farrell,
per comm, 2015). For the purposes of this study, a deposit is defined as a concentration of fossils
found below the surface (Woodard and Marcus 1973). In general, deposits’ latitude, longitude,
and depth are recorded; then the fossils are carefully wrapped and boxed in wooden crates
(Figure 4), and finally removed by cranes and placed within the Page Museum grounds in
Hancock Park. The origin location (pit) of each crate is recorded by the monitoring and recovery
experts from ArchaeoPaleo Resource Management, Inc (APRMI) within Hancock Park near the
Page Museum (Aisling Farrell, per comm, 2015, Turner 2006).

12

Figure 4 Project 23 crated fossil deposits (Page Museum 2015c)
To date, over 3 million fossils have been found in the La Brea Tar Pits and surrounding
area, and more are discovered every day. However, of the over 1 million specimens that have
been recovered from the La Brea Tar Pits, only around ~400,000 are digitally curated in the
museum collections database (Aisling Farrell, per comm, 2015). In this collection, curation is
defined as the collecting and cataloguing of fossils in as systematic manner so that the
information collected can be available for future studies (Jones 2006). The museum began the
initiative to database all specimens into a museum database system called KE EMu in 2006 (KE
Software 2015). As mentioned in Chapter 1, the museum uses the KE EMu museum database
software to digitally catalogue individual fossil specimens as well as create information (KE
Software 2015). Pre-2006 excavations can only be used in an aggregate form due to the older
data not being fully digital (entered into the KE EMu database) and data inconsistencies with
older catalogued specimens, such as spelling errors in multiple database attributes.
Encouragingly, post-2006 data such as Project 23 can be used for detailed studies involving
multiple individual specimens, including 3D visualizations of fossils contained in entire deposits.

13
2.1.2 La Brea Tar Pits Literature Review
The La Brea Tar Pits is a subject that the author studied in depth in order to better understand
fossil deposits at the La Brea Tar Pits and the content of the KE EMu fossil database. It was
considered essential to obtain a good working knowledge of paleontology in order to
successfully and accurately design an efficient spatial database (geodatabase) that could easily
interface with KE EMu as well as support an interactive web GIS application.
The author used her background in geology and direct access to paleontologists at the
Page Museum as resources for her research during her tenure as a Page Museum intern. Other
sources on the geology and paleontology of the La Brea Tar Pits include an article by Woodard
and Marcus (1973) that describes the stratigraphy and geology of La Brea Tar Pits fossil deposits
in detail. Also, O’Keefe et al. (2009) and Friscia et al. (2008) discussed the bone distribution,
density, and radiocarbon dates of Pit 91 at the La Brea Tar Pits. Lastly, at the time of this study,
Stock and Harris (1992) was the most cited reference on La Brea fossils and contains a wealth of
information on the fossil localities to be displayed in the web application. Shaw (1982) discussed
excavation techniques developed for Pit 91 and used currently for Project 23 excavations and his
paper was helpful for better understanding the excavation and curation process.
The Woodard and Marcus (1973) article provided context regarding the geology and
stratigraphy of the La Brea Tar Pits, which helped inform the creation of the geodatabase and 3D
fossil visualizations generated from the KE EMu database extract as a proof of concept of the
spatial support provided by a geodatabase. The O’Keefe et al. (2009) and Friscia et al. (2008)
articles also provided background and better understanding of the La Brea Tar Pits fossil
deposits. The O’Keefe et al. (2009) article in particular was used as a model to create
radiocarbon dating data tables for the geodatabase developed in this study. The Stock and Harris

14
(1992) book listed the common species found at the La Brea Tar Pits and was used as a source
for common names of fossils that were requested by the museum staff to be added as a field to
the geodatabase. Lastly, the Shaw (1982) article contributed to this study by providing a detailed
description of the excavation process, including an explanation of how each excavation site is
gridded and how the x, y, and z in situ position of fossils is measured.
2.1.3 Fossil Collection Background
Between March 2013 and June 2014, the author was a volunteer at the Page Museum at the La
Brea Tar Pits and thus had the advantage of talking to museum staff members with firsthand
information and accounts of fossil excavation and past salvage projects every working day.
From 1913 to present, a standard 1x1-meter grid pattern delineated by string is used to measure
fossil locations when excavating fossils at the La Brea Tar Pits (Shaw 1982, Figure 5).

Figure 5 Example string laid out in 1x1 m grid at Pit 91 excavation site (Shaw 1982)

15
The grid system is set up with string stretched across the fossil site, forming a meter-by-meter
grid pattern. This grid pattern is used to record the x, y, and z location of each fossil, but
accuracy is a challenge due to improper placement of the string or string loosening over time.
Individual fossil’s dimensions are measured with rulers and recorded with pencil and paper,
which contributes many additional potential sources of human error to the fossil collection
process.
2.2 Review of Existing Museum Databases and Applications
In Section 2.2.1 several existing museum and paleontological database schemas are presented.
Section 2.2.2 provides a succinct literature review of the primary sources considered in
preparation for designing the web GIS application. Existing applications found at the time of this
study were reviewed in terms of their user interface, platform, and functionality. Due to the lack
of fossil site web mapping applications discovered in this background research effort,
applications created by museums for other subject areas were reviewed.
2.2.1 Existing Database Review
The first database reviewed was the KE EMu database, the software used by the Page Museum
to catalogue their fossil specimen collection. KE EMu is a popular museum collections software
used by many museums around the world (KE Software 2015b). The database has an easy to use
form-based interface that is used to enter information about the specimen, such as the bone type,
part, description, and taxonomical classification, and also relative location information such as
deposit name and grid location and measurements (Figure 6).

16

Figure 6 KE EMu taxonomy module (KE Software 2015c)
The author reviewed the use of the database software by the museum staff and found that
the three primary modules used were the taxonomy, site, and fossil element modules. The site
information collected is not georeferenced in the form of coordinates. The location information
collected is the name of the deposit and the x, y, and z (depth of the fossil in situ, from surface)
in reference to the origin of the excavation grid. According to online documentation, the KE
EMu database can collect coordinates and display them as a report using a connection to the
desktop ArcExplorer software but the museum does not use this module. Collecting coordinates
is not part of their current workflow, and moreover, coordinate locations were not documented
for the historical excavation sites until the author of this study digitized a paper survey map of
the 1913-1915 excavation sites. However, the ArcExplorer capabilities as outlined in the KE
Software documentation are for small-scale use cases such as displaying sites on a global scale

17
(KE Software 2015d, Figure 7). In this study, the author’s database and web application design
are for the large scale needs of the La Brea Tar Pits study area.

Figure 7 KE EMu ArcExplorer report (KE Software 2015d)
The next database to be reviewed was the database of the MioMap application
(University of California 2015, Carrasco et al 2005). The database structure of the MioMap
database is similar to the Page Museum KE EMu database structure in the inclusion of an age
table, also in the requirements of the geodatabase discussed in Chapter 3. Fossil age data is
included in the geodatabase structure developed as part of this study.
The structure of the MioMap database consists of a Locality table which is linked to an
Age/Deposit table, Absolute Age table, Faunal table, Synonymy table, and Reference table.
There are interrelationships between the Faunal table and Synonymy table connected by a Taxon
ID. Also there is a relationship between the Reference table and the Electronic Bibliography
(Figure 8).

18

Figure 8 MioMap data structure (University of California 2015)
The attributes of the Locality table (Figure 8) include coordinates, altitude and precision
information. The database structure includes data on township location as per a USGS
quandrangle map encompassing the site. Reviewing this data structure was helpful but the same
attributes did not translate well to this project’s geodatabase design, because the fossil locations
are recorded at a different scale than those from the La Brea Tar Pits. Also, in the Faunal table
(Figure 8) each fossil specimen is only recorded as a species and does not include information
about the actual bone. In the context of this thesis, a species is defined as an individual animal of
the most specific taxonomical classification. For example, a saber-tooth cat (Smilodon fatalis) is
of the genus “Smilodon” and at a more specific level, classified as the species “fatalis” (Stock
and Harris 1992). In the La Brea Tar Pits deposits, whole specimens are not often found so
bones are recorded in the KE Emu database as “elements” and not recorded as the entire
individual (i.e., skeleton), as the MioMap database structure suggests.

19
The second database reviewed is the Fossilworks Paleobiology Database (Behrensmeyer
et al 2013). The structure of the database consists of six tables: (1) references, (2) taxonomic
names, (3) taxonomic synonymies and classifications, (4) primary collection data, (5) taxonomic
occurrences, and (6) re-identifications of occurrences. Tables are linked via Record ID numbers
and additional lookup tables are included in the database structure. This database is also not
designed to search on elements (individual bones) and instead searches the taxon name only.
The geodatabase design developed as part of this thesis addressed gaps found in the other
fossil databases considered, including adding precise location components for individual fossils
(bones). In addition, the other databases do not contain information that would allow detailed
searches, other than by species name. Lastly, the geodatabase is intended to display data at more
precise geographic locations than the databases reviewed in this study, as well as interface or
connect seamlessly with the existing KE EMu database maintained by the Page Museum staff.
2.2.2 Existing Museum Collection Application Platforms and Functionality
An extensive internet search for web GIS mapping applications similar to that envisioned for the
Page Museum found four applications that contained relevant functionality, including the ability
to search for specific fossils, visualize the fossil location on a map, and view related information
to add context such as photos or related data (Smithsonian 2015, National Science Foundation
2015, Harbert 2014, and University of California 2015). A summary of each application and the
relevance to this study is provided in the section.
The first application reviewed was the Smithsonian Institution’s application displaying
the most populated as well as unpopulated places in the world (Smithsonian 2014, Figure 9). The

20
Smithsonian site uses a customized ArcGIS Online storytelling shortlist application
9
template.
The user interface features a numerical guide through different web page panels that describe
each numbered point on the map. The application combines media such as photos with text that
describes each location on the map in detail. Since features such as fossil deposits displayed in
the Tar Pits application are numbered, the storytelling shortlist template could possibly be a good
option for this project.

Figure 9 Is the World Full or Empty (Smithsonian 2015)
Another web GIS application reviewed was VertNet, which is an initiative between
several universities and institutions to create a portal to connect to all museum and university
fossil collections (National Science Foundation 2015, Figure 10). The VertNet application is
developed using the Google Maps API as the web mapping technology. The user interface of
VertNet is straightforward, however the web map visualization is basic and lacks richness in

9
GitHub. 2015. “Shortlist Storytelling Template JS.” Accessed August 29, 2015. https://github. com/Esri/shortlist-
storytelling-template-js/.

21
symbology. The website overall is robust and the information that can be found when searching
for a specific specimen is very helpful for the research community. In comparison, the web GIS
application developed as part of this research is intended to be more focused on the specific
needs of the La Brea Tar Pits and would not need to display information such as language,
institution code, name recorded by, and modified date found in the VertNet application (Figure
10).

Figure 10 VertNet showing specimen from the Royal Ontario Museum (National Science
Foundation 2015)

The third application reviewed, the MioMap: Miocene Mammal Mapping Project, is an
interactive web mapping application similar to the web GIS application developed as part of this
thesis (Carrasco et al 2005, Figure 11). This application employs a table and popup combination,
as outlined in the user requirements for this application developed as part this study (Chapter 3).
It is also interesting to note that this application includes records of fossils reportedly found in
the La Brea Tar Pits, yet only shows nine records and the locations of those records are

22
inaccurate. For example, one location is almost a mile south of the true location of the museum
grounds that contain the pits.

Figure 11 MioMap: Miocene Mammal Mapping Project (University of California 2015)
The four similar web map applications reviewed herein indicate that there may be a gap
in publically available robust fossil web GIS applications. The majority of GIS web applications
found in this research effort display data at very small-scale extents. For example, the VertNet
example and MioMap application web maps are low resolution, at the state and city level. In the
MioMap example, coordinates are not accurate for locations and vary by at least a mile. In
contrast, the La Brea Tar Pits web GIS application is a larger-scale application with data
displayed at a near meter accuracy. For example, the La Brea Tar Pits web GIS application is
designed to allow the user to zoom into locations in the park and with a subset of data,
specifically the data extract from Project 23, have the ability to view fossil x, y, and z orientation
to the centimeter.

23
The background research on the La Brea Tar Pits history, current processes, and
excavation methodology gave the author the necessary knowledge and context to proceed with
the thesis work. Moreover, the review of existing web application and databases helped the
author to identify the current research gaps. The knowledge gained from the research carried out
in this chapter greatly helped to inform the database and web development described next in
Chapter 3 Methodology.

24
CHAPTER 3: METHODOLOGY
This chapter describes the methodology for developing the La Brea Tar Pits geodatabase and
accompanying web GIS application. Section 3.1 provides an overview of the methodology, while
section 3.2 describes the data sources including spatial and non-spatial data intended to interface
with the Page Museum’s KE EMu database. Section 3.3 explains the data processing performed
and geodatabase creation methodology. The functional and nonfunctional requirements as well
as the development approach for the database and web GIS application are described in section
3.4. Lastly, section 3.5 discusses the application development environment, development
process, and requirements review.
3.1 Method Overview
The methodology in this thesis initiated from a final project completed under the direction of Dr.
Jennifer Swift in the SSI MS GIST Program, the SSCI 591 Web GIS course at the University of
Southern California. Additional ideas were garnered from a GIST master’s thesis on the topic of
web application development (Milholland 2014), and from my own experience as a GIS Analyst
at International Medical Corps where I participate as part of the Information Technology
Business Services development team (International Medical Corps 2015).
A general overview of the development of the web GIS application is provided in Figure
12. The first two steps included data collection and geodatabase and web application
requirements gathering. The last requirements gathering step was determining the most
appropriate web GIS software and production environment. For this project, the author chose the
ArcGIS Server GIST Virtual Machine environment (Figure 12, step 2).

25

Figure 12 La Brea Tar Pits web GIS application methodology
The third step in this study was data preparation, in which the data model was refined
based on user requirements and the data extract from KE EMu was transformed into a
geodatabase (Figure 12). In Step 4 the data was prepared for the web GIS application by
publishing the feature classes from the mxd to ArcGIS Server (Figure 12, step 4). Step 5
describes the building of the web application, which began with choosing a web application
template for functionality, look and feel. For the last step (Figure 12, step 6), the author tested
the application and fixed all errors found. Finally, users tested the application for usability and to
confirm that requirements were met. The required research skills that were necessary to complete
this project are described in Appendix A: Required Research Skills.

26
The remainder of this chapter covers the data methodology, spatial database and
application development. Specifically, section 3.2 covers data sources, section 3.3 discusses data
processing and data considerations, section 3.4 describes the web GIS application development
approach and user requirements, and section 3.5 explains the ArcGIS Server and Online
application development process.
3.2 Data Sources
This section describes the data sources used in this project and divides the data into two
categories: spatial and non-spatial. Non-spatial and spatial data source descriptions are provided
in sections 3.2.1 and 3.2.2, respectively. A detailed table of all data sources described in this
section can be found in Appendix B: Data Sources and Description.
3.2.1 Spatial Data
One of the most important input spatial data for this thesis project was a paper survey map on
paper (40 x 70”) that was provided by the Collections Manager, Aisling Farrell, of the George C.
Page Museum of La Brea Discoveries (Figure 13, Noble 1913). The map contains locations for
all excavation sites at the La Brea Tar Pits, from 1914 to present. The paper survey map was
originally made by the Los Angeles County Surveyor in 1914 and was updated in 1983 by Page
Museum staff (Aisling Farrell, per comm, 2013; Noble 1913).

27

Figure 13 Scanned TIFF of original paper survey map of Hancock Park

28
The second critical spatial data input was geologic borehole data, which included latitude
and longitude information. This data came from a USGS report written by Jim Quinn, a geologist
and former Curatorial Assistant at the Page Museum (Quinn et al 2000). Two of the 403
boreholes contain detailed sediment descriptions by elevation and depth (from sea level) as well
as the asphalt content of the sediment by elevation and depth (from sea level).
The third highly significant spatial data input was the Project 23 deposit locations. Project
23 is the name of the newest deposits to be found during the excavation of an underground
parking structure for LACMA (see section 2.1.1 La Brea Tar Pits Background). The latitude and
longitude information for the deposits (crates) were transcribed from the ArchaeoPaleo Resource
Management, Inc. report (Turner 2006).
3.2.2 Non-Spatial Data
The non-spatial data for this research came from two sources: (1) the museum’s KE EMu
database (KE Software 2015a, La Brea Tar Pits Collections KE Emu database 2015) and (2) a
publication by O’Keefe et al (2004). The Page Museum uses KE EMu as their curatorial
relational database that contains records describing the fossil specimen excavated from a La Brea
deposit and the positional information about the specimen. The extract provided by Museum
staff consisted of two tables, named Ecatalogue.csv (408,234 records) and SitSiteR.csv (408,203
records). The Ecatalogue.csv table contained the catalogued specimens’ taxonomical and
osteological information and the SitSiteR.csv contained the specimen location information in
three dimensions.
In addition to the survey map, Museum staff provided an extract from June 2015 of the
entire fossil database as two csv files (Appendix C: KE EMu Data, Table C-1 and Table C-2).
The attributes found in the database extract provided are described in detail in section 3.2.2, and

29
are not inclusive of every attribute that can be found in the KE Emu database. Instead the
attributes included from the Project 23 extract represent those that were chosen by the museum
staff as the attributes they deemed necessary for the geodatabase, specifically for the creation of
summary tables (described in section 3.3 and also found in Appendix E and F).
3.3 Data Background and Processing Methods
The first part of the data processing was to scan and digitize the 1914/1983 paper survey map in
order to bring the paper survey map’s data into a GIS. For this project and, more importantly, to
preserve the map and the history it contains, Aisling Farrell and the author of this study had the
paper map professionally scanned and output in TIFF format at a blueprint shop. Due to the map
being over three decades old, there were many worn areas, smudges, and slight tears. Thus, it
was also important to scan the map before more wear and tear could occur.
The geotechnical report borehole data provided by Jim Quinn et al. (2000) was copied
and reformatted into Microsoft Excel and displayed in ArcMap as NAD 1927 UTM Zone 11N
because the data was collected in that datum. Both USGS borehole data and Esri imagery were
used to georeference the scanned survey map, after projecting the borehole data to WGS 1984
Web Mercator Auxiliary Sphere to match the Esri Imagery Basemap (Figure 14).
Next in ArcMap, contours, survey markers, La Brea Tar Pit excavation sites, exposed
fossil pits, creeks, water bodies (lakes and ponds), and oil and gas vents were manually digitized
from the georeferenced survey map. After digitizing the La Brea Tar Pit locations from the
survey map as polygons, the Feature to Point tool in ArcMap was used to convert the polygons to
points. This was done because some pits appeared so small on the original surveyed map that it
was determined that they should be represented as points. Converting to points made it easier to

30
represent the features as marker symbols that can be easily enlarged, and all pits are thus
uniformly represented.

Figure 14 Esri Basemap and boreholes (green circles) used to georeference the survey map
Also, the more recent excavation locations referred to as Project 23 were added to the
GIS map project from an Excel table of latitude and longitude locations extracted from the
ArchaeoPaleo Resource Management, Inc (APRMI) mitigation report (Turner 2006). The data
were added to ArcMap and the x,y coordinates were used to convert the data in the Project 23
table into a feature class. Appendix E: Geodatabase Creation Notes and SQL Queries provides
more information on the geodatabase creation process. In order to simplify the data migration
from the KE EMu database, the digitized La Brea Tar Pits data (known as the Hancock

31
Collection data in the KE EMu database) and the Project 23 data were merged into one feature
class. Additionally, the depth fossil measurements were converted to the survey map
measurements in feet, since the APRMI depth recorded in both meters and feet.
Next, the KE EMu data extract from June 2015 was directly converted into a
geodatabase. Appendix E outlines the process of transforming the database extract csv tables into
a SQL Server 2008 geodatabase. To summarize, two files were imported into SQL Server as flat
files and then transformed into one table. Those fields that had multiple values were parsed into
multiple fields in order to preserve the data.
In order to capture key information from the Specimen Site Catalogue entity without
exposing its records as a feature service (the Page Museum requested that the granular data not
be shown), summary tables were generated from the SpecimenSiteCatalogue table. The summary
tables were calculated using SQL queries in anticipation that in the future the queries can be used
as stored procedures if the KE EMu database is directly linked to a SQL geodatabase instance
within ArcGIS Server. The SQL queries used to create the summary tables can be found in
Appendix F: SQL Queries for Specimen Summary Tables.
A small subset of fossil location data from the KE EMu database captured to the
centimeter level from Project 23, Deposit 1 was provided for this project as well. The data
extract consisted of 59 Panthera atrox specimens and 108 Smilodon fatalis specimens. These
specimens were chosen because while the large majority of specimens at the museum cannot be
attributed to one individual animal, these particular specimens have been deemed by
paleontologists to possibly belong to identifiable individuals (Aisling Farrell, per comm, 2015).
For example, the Panthera atrox specimens are thought to represent one individual while the
Smilodon fatalis specimens are thought to represent at least three individuals. The museum staff

32
considered these to be representative data samples well suited as a guide for development of the
geodatabase schema.
ArcMap and ArcScene were then used to visualize the specimen records of the Smilodon
fatalis and Panthera atrox individuals in 3D, as a preliminary test of the geodatabase design. To
prepare the original data for 3D visualization using ArcScene, the data had to be significantly
transformed in Microsoft Excel by the author. Multiple values found in one cell had to be parsed
using Text to Columns in Microsoft Excel, and the catalogue number or identification number
for a given fossil was replicated in each new record corresponding to each point on a bone at
which measurements were taken (Figure 15). When a fossil is excavated, northing (y- direction),
westing (x-direction) and, below depth (z-direction) measurements from one to three points of
the bone are recorded depending on the type of bone.

Figure 15 Below depth (BD), northing (N), and westing (W) measurements taken for a mammal
femur (Shaw 1982)

33
The measurements are recorded in centimeters using a grid pattern laid out at the excavation site
with an origin that is in the southeast corner of the grid (Figure 16).

Figure 16 Example grid Layout: Project 23 Deposit 1
The westing measurement (x-direction) had to be negated (Appendix D: Fossil Positional
Data for 3d Display, Table D-1 and D-2) to flip the measurement because the axis in ArcMap
origin becomes negative as it moves west. Similarly, the BD (Below Depth) measurement was
negated in order for each fossil to be correctly oriented below the surface in a GIS visualization
(Appendix D: Fossil Positional Data for 3D Display, Table D-2). Also, manual editing had to be
done in ArcMap to account for grid extensions. For instance, a fossil that is found predominantly
in Grid B-1 may have a point that extends into B-2. If the measurements are displayed in
ArcMap without editing the fossil may appear distorted in appearance, in many cases too large.
The main purpose of these 3D visualizations was to demonstrate that fossil positional
data collected in the KE EMu database could be digitally visualized to allow paleontologists at

34
the Page Museum to better understand deposit shapes and the distribution of different specimen
types, to thus inform research questions. In the future, 3D models would allow a museum visitor
to visualize fossil orientations pre-excavation. In the final application, a video of Figure 17 is
displayed as in a link from the application, as an example. The linked web pages containing the
3D renderings were created using Adobe Dreamweaver as simple html pages with video tags
hosted on the USC web server.

Figure 17 Smilodon fatalis long bone specimens classified by ontogenetic age
3.3.1 Specimen Data
A major disclaimer concerning the fossil data received from the Page Museum KE EMu database
is that the data extract is in no way representative of the entirety of the Page Museum’s
collection, nor is it an accurate picture of the true number, type, and distribution of fossils found
in the many pits. Many specimens from early excavations were never entered in the KE EMu
database nor have all specimens been completely recovered from every pit (Aisling Farrell, per

35
comm, 2013). Thus, fossil type aggregate totals for each pit provided for this study are
considered proof of concept purposes only, in order to visualize what information the museum
currently has recorded.
It is important to further define the word specimen to provide additional context.
Specimen does not refer to an individual animal. Specimen refers to any bone, bone fragment,
tooth, claw, etc. from a fossilized animal. Note that specimens also include plants, but for the
scope of this project plants were not included in the GIS application. Also, it is essential to note
that the majority of specimens at the museum cannot be assembled together as an individual. In
fact, specimens on display at the museum are primarily a mix of many different individuals. The
reason that individuals are not found at the Tar Pits is because of the geology and mechanics of
an asphalt fossil locality. As asphalt rose through cracks in the geologic layers below the
museum, they were mixed together and the process damaged most fossils.
3.3.2 Spatial Data Accuracy
It is expected that there may be errors due to the inherent lack of accuracy when rubber-sheeting
the survey map since there were only 54 actual measured latitude longitude measurements
(boreholes) that could used as locational references. In addition, the pits were mapped in 1913
and last updated in 1983 by field survey without the help of today’s sub-meter digital GPS units.
The park has changed greatly over the years since this map was first created, and pits, water
features, elevation, and other attributes have also changed due to land development. Also, there
may be errors in the manual digitization of the map because of the human error endemic to using
a mouse.
The original source of this data is considered high quality in terms of accuracy because
the survey map was created by a professional surveyor, albeit in 1914, and updated in 1983 by

36
professional geologists and paleontologists. Nevertheless, there is always the possibility of
human error inherent with hand drawn maps that must be noted. The depth measurements on the
survey map have an additional margin of error because those data were recorded in 1914 by
fossil excavators working in the inhospitable environment of asphalt pits subjected to frequent
cave-ins, asphalt and water infiltration, and methane exposure (Stock and Harris 1992).
Moreover, the pits measured at that time have been covered by rapid development in the Los
Angeles area. There is now an art museum built on top of ten of the pits, a parking lot and a lake
(Figure 18), as well as the Page Museum covering nine pits. Unfortunately it is not possible to
visually field check the accuracy of the map by surveying those parts of the park grounds.

Figure 18 A parking lot was built over Oil Lake and several pits during the 1970s

37
3.3.2.1 Fitness of Use for Spatial Analysis
The borehole data from the 1970s to 1990s and at depth is considered accurate, though
the condition near the surface has changed over the years due to landscaping and the building
construction discussed above. Predictions made from analyzing the data should be carefully
considered since the pit data dates back to 1914 while the geologic data spans the last 30 years.
As for the distribution of the data, the pit polygons are clustered in the southwest part of
the map extent (Figure 19). The polygons for the fossil pits represent between 25 and 1000
square feet and it is unlikely that the fossils are homogenous within pits. This could affect future
spatial analyses because the distribution of and number of fossils in each pit cannot be analyzed
because of a lack of data. Some pits included in the dataset used in this study may not have had
more than a few fossils in them, while other pits contained thousands. Eventually it would be
advantageous to attempt to estimate the number of fossils in each pit with the help of the Page
Museum staff by reviewing old museum documents from the early 1900’s. Another primary
issue for this dataset is that there is no nonoccurrence data, only occurrence, because the
excavation sites were not chosen systematically. If GIS analysis is carried out using this data in
the future, archival documents from the 1914 survey should be reviewed to determine if any
nonoccurrence pit data is available.
If this data is to be used for spatial analysis in the future the author recommends that the
digital data be re-projected into NAD 1927 State Plane California VII FIPS 0407. Web Mercator,
although projected, is not recommended for spatial analysis in California because area and
distance are distorted as distance from Equator increases. State Plane coordinate systems
preserve area, directions, distance, and shape with minimal distortion so it is the best choice for
spatial analysis in a large-scale mapping project for a 0.25 by 0.35 square mile extent.

38

Figure 19 Clustering of fossil pits and fossil excavation sites

39
3.4 Development Approach and User Requirements for the Web GIS Application
The following sections describe the development approach and the user requirements gathered
from the museum staff for the web GIS application.
3.4.1 Development Approach
An Agile programming approach was used to continuously make changes to the application until
the requirements are met, with the acceptance that requirements may change during the
development process (Deewan and Jain 2012). The geodatabase and web GIS application were
tested for functionality and troubleshooting was accomplished as needed. After the web GIS
application was completed, during Spring and Summer semesters of 2015 museum staff
participated in user acceptance testing (UAT) to ensure that the Page Museum requirements had
been met by the application, detailed in the next sections of this chapter.
3.4.2 User Requirements
To summarize, the primary requirements for this project include the following: 1) the digitized
survey data must be contained within a spatial database (geodatabase), 2) the data must be linked
to specimen information in a web GIS application, 3) summary database tables must aggregate
data by attributes such that many details of each specimen are not available to the general public.
The next section describes the application-specific functional requirements.
3.4.2.1 Functional Requirements
Functional requirements are requirements that are necessary for the desired user experience for
the web GIS application. These describe the minimum requirements of the application in terms
of the user experience. Appendix G: Requirements for the Web GIS Application, Table G-1 lists
and describes the requirements and the viewing mediums recommended.

40
3.4.2.2 Nonfunctional Requirements
Nonfunctional requirements identify how the web application and underlying system should
perform. Appendix G: Requirements for the Web GIS Application, Table G-2 describes the
nonfunctional requirements for each medium (server/web system and web/mobile interface).
3.5 ArcGIS Server and ArcGIS Online Application Development
Section 5.5.1 describes the software environment, section 3.5.2 outlines the steps performed in
the ArcGIS Server environment to create and publish the data as a map service to the web,
section 3.5.3 reviews the steps performed in the ArcGIS Online environment to create the web
GIS application, and lastly a review of the web application to determine if user requirements
were met is provided in section 3.5.4.
3.5.1 Development Environment
Database, server, and web development were performed on a MacBook Air (OS X Yosemite
10.10.4) using a GIST ArcGIS Server virtual machine (VM) running Microsoft Windows Server
2008 R2, ArcGIS for Server 10.2.1, and SQL Server 2008. The html code to create the linked
web pages containing the videos of 3D data was edited with Adobe DreamWeaver on a
MacBook Air desktop and transferred to the USC web server via the FTP application Fetch.
3.5.1.1 Web GIS Software and Environment
The web GIS software environment chosen for this project was Esri ArcGIS Desktop, ArcGIS
Server, Microsoft SQL Server Express, ArcGIS Online, and ArcGIS JavaScript API (Figure 20).

41

Figure 20 Flow Diagram of ArcGIS development process from raw data to web GIS application
3.5.1.2 ArcGIS Server Environment
The development and production environments both reside on a Windows Server 2008 R2 SP1
virtual server. At the time of this study, the server was hosted at the University of Southern
California (USC). Feature services were created in ArcMap 10.2.1 using Microsoft SQL Server
2008 database feature classes.
3.5.1.3 ArcGIS Online Environment
ArcGIS Online (AGOL) provides a convenient technology for customizing the symbology and
basemaps used in web GIS applications easily in a user-friendly environment that does not
require programming. Thus, by using AGOL, a web application developer can quickly customize
the look of REST endpoint feature layers by editing the pop-up text, basemaps, layer order, and
other stylistic elements. The ease of development in ArcGIS Online allows the developer to
quickly move on to more complex customization of the application by speeding up the feature
layer customization process. The ArcGIS Online environment used in this project is the USC
Spatial Sciences Institute (SSI) ArcGIS Online for Organizations.

42
3.5.2 ArcGIS Server Development
On the GIST VM, the feature classes were compiled in ArcMap 10.2.1 for preparation for
publishing using ArcGIS Server 10.2.1. In ArcMap, the feature class fields were given user-
friendly aliases to remove underscores and to better describe what the fields represent by adding
details in parentheses, such as “Depth (in meters)” to alert the user of the units of measure of the
depth field. Also, in ArcMap, the symbology was set using custom made symbols (Figure 21)
created by cartoonist John Pham (John Pham, per comm, 2015).

Figure 21 Custom symbols
Next, the finalized ArcMap map project (mxd) was published as a read-only map service as a
REST endpoint. Appendix H lists the layers that make up the feature services.
3.5.3 ArcGIS Online Development
The REST Service endpoint was added to a new blank web map in ArcGIS Online. The web map
created for this project was shared to the USC ArcGIS Online organization and the web map was
customized based on user requirements.
The “Streets” basemap was chosen to provide a neutral backdrop for the data with some
labeling, yet minimal coloring to compete with the map data. The pop-ups were edited in the
ArcGIS Online web map before publishing as a web application. In the pop-up it should again be

43
noted that specimen counts and most abundant values are based on specimens recorded in the
geodatabase and do not represent the entirety of the collection. The pop-up text was written as a
custom attribute display using text and attributes html tags in the ArcGIS Online pop-up text
editor. For example, the La Brea Fossil Deposit pop-up was edited to display:
“There are a total of "{NumberofSpecimens}" databased specimens in Deposit {locality}
as of June 2015. The top elevation of the deposit is {top_elev_ft} feet and the bottom
elevation is {bottom_elev_ft} feet.
The calibrated age range for this deposit is "{relationships/0/Min_Calibrated_Age}
{relationships/0/Max_Calibrated_Age}”. Note: If Total Number of Specimens is blank ("
"), there are no recorded data for this deposit.”

In the web GIS application, feet was chosen as the unit of measurement since all elevations, pits,
and water depths are recoded in feet on the original survey map, which allows for consistency
within the application for the user.
The web application was built using an ArcGIS Online template and the JavaScript API
version 3. The out-of-the-box WebAppBuilder technology hosted on ArcGIS Online was also
used (Esri 2015c). The color of the overall application was changed from the default blue color
to an orange color similar to the orange used on the Page Museum’s main website. An image of a
saber-toothed cat (Smilodon fatalis) was also added to the interface as well as a link to the Page
Museum website. Additionally, the visible scale of the application was extended to 100 as the
maximum zoom level with 800, 400, and 200 added as zoom levels in between. The zoom level
was extended due to this application being large scale for a very small extent of Hancock Park
(0.25 by 0.35 square mile).
An “About” section was added to explain the web application to the public. In the
“About” section, a URL to the original paper survey map was included to give the application

44
user insight into the development of the geodatabase and web GIS application
10
. Also URLs of
the 3D representations of the Smilodon fatalis and Panthera atrox elements (bones) were added
to display the future potential of mapping fossils to the centimeter level in 3D
11

12
.
Also, queries and charts were added per the user requirements of the web GIS
application. The queries were requested by the museum staff because there are about 145
deposits, so searching the data would otherwise be difficult. As a proof of concept, one may
search for most abundant deposits by deposit, by species, and by element. As this web GIS
application evolves in the future these queries can be added or edited as the museum staff
requires. The queries added include the following: (1) Search by Deposit #, (2) Search for Most
Abundant Deposits, (3) Search for Deposit by Most Abundant Species, and (4) Search for
Deposit by Most Abundant Element (Figure 22 - Figure 25 display the chart configurations).

10
Pham, K.P. 2015a. “Tar Pits Survey Map.” Accessed August 29, 2015. http://www-scf.usc.edu/~kaceyjoh
/LaBreaTarPits/TarPitsSurveyMap.html/.

11
Pham, K.P. 2015b. “P23 Box 1 Fluffy Video.” Accessed August 29, 2015. http://www-scf.usc.edu/~kaceyjoh
/LaBreaTarPits/P23_Box1_Fluffy_video.html/.

12
Pham, K.P. 2015c. “P23 Box 1 Smilodon by Age Video.” Accessed August 29, 2015. http://www-scf.usc.edu/
~kaceyjoh/LaBreaTarPits/P23_Box1_SmilodonbyAge_video.html/.

45

Figure 22 Search by Deposit # Query widget configuration

Figure 23 Search for Most Abundant Deposits Query widget configuration

46

Figure 24 Search for Deposit by Most Abundant Species Query widget configuration

Figure 25 Search for Deposit by Most Abundant Element Query widget configuration

47
The charts were requested to show an added dimension to the data visualization by
showing abundance of deposits and certain taxonomical classes by using pie and column charts.
The charts that were added include: (1) Number of Specimens by Deposit, (2) Total Number of
Mammals, Reptiles, Birds, and Amphibians in Collection (associated with a deposit), (3)
Number of Mammals by Deposit, (4) Number of Reptiles by Deposit, (5) Number of Birds by
Deposit, and (6) Number of Amphibians by Deposit (see Figure 26 - Figure 31 for chart
configurations).

Figure 26 Number of Specimens by Deposit Chart widget configuration

48

Figure 27 Total Number of Mammals, Reptiles, Birds, and Amphibians in Collection (associated
with a deposit) Chart widget configuration

Figure 28 Number of Mammals by Deposit Chart widget configuration

49

Figure 29 Number of Reptiles by Deposit Chart widget configuration

Figure 30 Number of Birds by Deposit Chart widget configuration

50

Figure 31 Number of Amphibians by Deposit Chart widget configuration
3.5.4 Web Application Requirements Review
The following section outlines the web application review results from a meeting that took place
between the author and museum staff on July 13, 2015. The results of the review can be found in
Appendix G: Requirements for the Web GIS Application, Table G-3 and G-4. Additionally, a
review of feedback received and changes requested can be found in Appendix G, Table G-5.
3.6 Chapter Summary
The methodology implemented consisted of data collection, requirements gathering, geodatabase
design, data preparation for entry into the geodatabase and use in the web GIS application
respectively, web GIS application creation, and testing of the final pilot web GIS application.
Steps were repeated as necessary using an Agile approach after museum staff user review. The
results of the final geodatabase and web GIS application created by following the method

51
outlined in this chapter are presented in the next chapter, Chapter 4 Results, including
geodatabase and web GIS application user guides citing typical use cases for the web
application.

52
CHAPTER 4: RESULTS
This chapter describes the results of the development of the La Brea Tar Pits database and web
application. Section 4.1 presents the final geodatabase model, and section 4.2 provides examples
illustrating the web GIS application functionality. Section 4.3 includes user manuals for use and
updating of the web GIS application, and section 4.4 outlines typical use cases for museum staff
for the web GIS application. Lastly, section 4.5 presents a preliminary field data collection
schema.
4.1 Data Model: Geodatabase Diagram and Schema
The structure of the La Brea Tar Pits database is visualized in a geodatabase Entity Relationship
Diagram (ERD) in Figure 32 and Figure 33. The design of the geodatabase ERD is based on all
data fields captured in the KE EMu database, though not all of the original data fields were
captured in the web GIS application pop-ups. To best focus the scope of this project as well as
follow the requirements gathered from the Page Museum staff, only some of the fields and
aggregated versions of the fields were implemented in the geodatabase and thus also in the
resulting web GIS application. A detailed description of the resulting interrelationships between
database entities is provided herein.
The design of the geodatabase ERD was created to follow KE EMu database structure as
closely as possible, to facilitate creating queries to extract, transform, and load records from the
KE EMu database to the La Brea Tar Pits geodatabase as part of a regular maintenance and
updating plan for future work (Figure 32). Thus the geodatabase was built based on an ideal
structure for a “live” geodatabase in which records could be updated to the SQL Server
geodatabase nightly using stored procedures.

53

Figure 32 La Brea Tar Pits geodatabase ERD detailed view

54

The La Brea Tar Pits geodatabase ERD begins with the LaBrea_FossilLocalities point
feature class entity and describes the survey site in which the fossils are found. The
LaBrea_FossilLocalities includes dates recorded for some pits in the HC_PitDates table, and
contains many fossil specimens from the Specimen_Site_Catalogue indicated in Figure 33. The
LaBrea_FossilLocalities feature class is related to both the HC_PitDates entity and the
Specimen_Site_Catalogue entity by the Deposit ID. The Specimen_Site_Catalogue entity
describes the paleontological specimen found at the survey site, using a common attribute key.
The Specimen_Site_Catalogue entity contains all attributes found in the extracted tables from the
KE EMu database. Some specimens contained in the table may have null deposit IDs or IDs that
do not match to a locality in the LaBrea_FossilLocalities entity.
The LaBrea_FossilLocalities table possesses a one-to-many relationship with the
Specimen_Site_Catalogue entity, and the LaBrea_FossilLocalities table has a one-to-one
relationship with the HC_PitDates entity (Figure 33). The SQL query summary table
SumbyDeposit is related in a one-to-many relationship to the Specimen_Site_Catalogue table via
intermediate entity tables. The intermediate tables are related to the Specimen_Site_Catalogue
entity as a many-to-many relationship for all entities except for the SpecCountByDeposit entity,
which is related one-to-one because there is only one total specimen count per deposit. The
many-to-many relationships exist because there are many species per deposit. The
SumbyDeposit table was created via multiple SQL queries to extract totals from the intermediary
tables. Lastly, the SumbyDeposit table was joined one-to-one to the LaBrea_FossilLocalities
entity feature class.

55
The geodatabase ERD also contains the borehole entities from the data collected from the
USGS and the Quinn technical report (Quinn et al 2000, Figure 33). The Boreholes_USGS point
feature class is related to the USGSBoreholesDescription entity in a one-to-many relationship
because each borehole location is related to many depth measurements with associated sediment
descriptions.

Figure 33 Enlarged view of main structure of the La Brea Tar Pits geodatabase ERD showing the
relationships between the point feature classes and tables

56
4.1.1 Geodatabase Tables, Fields, and Properties
Appendix I: La Brea Tar Pits Geodatabase contains tables that describe the fields, attributes, and
relationship classes in the La Brea Tar Pits geodatabase.
4.1.2 Data Model Review
The data model was reviewed and accepted by museum staff on July 13, 2015. As previously
stated, the main requirements were that the new geodatabase be as similar to, or as consistent as
possible with the basic schema of the KE EMu data extract provided in June of 2015. The data
entered into the geodatabase was minimally changed for the purpose of better visualizing in a
GIS. The key requirement that highly detailed information on individual specimens not be
exposed in the geodatabase and hence in the web GIS application required that some data be
summarized was accomplished, and checked and accepted by museum staff. The museum staff’s
major concern was sustainability and the ease of maintenance of the geodatabase in the future
(Aisling Farrell, per comm, 2015). These concerns are addressed in Appendix K in the form of
geodatabase update user guide, and in Chapter 5 Future Work, regarding long term database
development and implementation.
4.1.3 Database Update User Guide
To update the database, in the future it is anticipated that the KE EMu database will be directly
connected to the SQL Server and stored procedures, based on the SQL queries found in
Appendix E and F. It is intended that the queries be set to run as nightly automated updates. Until
a connection is set up between the two databases, a manual update procedure is still necessary.
Appendix K: Geodatabase Update User Guide contains the steps required for performing manual
updates of the new geodatabase from the KE EMu database.

57
4.2 Web Application Screenshots and Functionality
When the user first opens the application, the interface appears as shown in Figure 34. Note that
the application is not public so an ArcGIS.com Organizational account will need to be provided
in order to view the application.

Figure 34 The final version of the La Brea Tar Pits web application as of August 2015
There is a left side panel of widgets that can be clicked on and will automatically expand the side
panel to a full pane. The widgets are follows: About the App, Legend, Table of Contents,
Queries, and Graphs. Within the map frame in the top left corner, are zoom in/out controls,
return to home extent, and find the user’s location buttons. Additionally, in this same left upper
area, the bookmarks and basemaps widgets can be found.
4.3 Web Application User Guides
User guides were developed to help the museum staff use and update the web GIS applications.
Appendix L is a guide for use of the application, which follows a standard user workflow. The
user guide shows the staff how to navigate to the various menus and widgets in the application,

58
with emphasis on how to use the query and chart functionalities. Appendix M: Web GIS
Application Update User Guide contains a guide to assist the staff in the update of the
application when necessary. The guide outlines how to change the web map, the layout, the
colors, and edit the widgets in the web application using the ArcGIS Online Web AppBuilder
interface.
4.4 Use Cases
The anticipated current use cases are for the museum staff, to check the web GIS
application daily to view how the total distribution of different taxonomical classes had changed
based on recent excavations. Also, the web GIS application itself can be used to search for
locations of pits as a simpler way to find current or new pit locations. This is the first map view
of any kind displaying both Project 23 and Hancock Collection pits together on the same map.
The web GIS application also provides an extremely efficient way for the museum staff to
monitor and compare locations across the park. Visually, staff members can easily see trends in
abundance of specific types of fossil deposits that could hopefully lead to answering many
present and future research questions related to paleontology and climate change.
The web GIS application offers a way to view selected collection data spatially that
cannot be achieved by viewing through the medium of the KE EMu database. The new way of
viewing the specimen data can lead to new data curation workflows that allow the museum staff
to catch errors in data entry that may not may have otherwise gone undetected using the interface
of the KE EMu database.
4.5 Field Data Collection Geodatabase Prototype
This section describes additional work that was done to support the thesis project, but has not
been reviewed by the museum staff. The proposed field data collection geodatabase could be

59
created by updating the current geodatabase design described in section 4.1. It is important to
note that this is a draft schema that has not yet been implemented as a backend to a mobile field
data collection application.
4.5.1 Field Data Collection Geodatabase Diagram and Schema
The original design of the geodatabase ERD was created with the help of Cynthia Burrows,
Jennifer Titus, and Dr. Jordan Hastings in SSCI 582 Spatial Databases Spring 2014 course as
part of a final project (Cynthia Burrows, Jennifer Titus, and Dr. Jordan Hastings, per comm,
2014). Cynthia and Jennifer designed a geodatabase ERD for archaeology and the author focused
on paleontology and combined these efforts into an Archaeo-Paleo database design. For this
thesis project, the author then adapted the Archaeo-Paleo database design to be focused on
paleontological use specific to the project requirements of the Page Museum at the La Brea Tar
Pits (Figure 35 – Figure 36).

60

Figure 35 La Brea Tar Pits field data collection geodatabase ERD detailed view featuring all Fossil Element Subtypes

61

Figure 36 Enlarged view of main structure of the La Brea Tar Pits field data collection
geodatabase ERD showing the relationships between attributes and the feature class type of each
entity
Appendix J: La Brea Tar Pits Field Data Collection Geodatabase Table J-1 through Table
J-9 describe the tables, fields, relationship classes, topology, and domains, in the La Brea Tar
Pits field data collection geodatabase. The La Brea Tar Pits field data collection geodatabase
ERD starts with the Surveyor entity. The surveyor is responsible for finding and documenting
the specimens (fossils or fossil elements) that they discover in an assigned grid cell. An
individual surveyor may find one or many specimens. The Fossil Element entity describes the

62
paleontological specimen found at the survey site. The fossil element entity is made up of
subtypes of the most common types of fossil element found for each entity. The Pit Deposit
entity describes the survey site in which the fossils are found. Each Pit Deposit entity contains a
Grid entity, which is the outline of the gridded survey site within the larger Pit Deposit entity.
The Grid entity contains the Grid Cell entity, which is made up of rows and columns. Each grid
has depth levels that are measured in units of 25cm called “spits.” A fossil’s element entity must
be found within one or many grid cells.
The following is a description of the envisioned interrelationships between geodatabase
entities. The surveyor has a one-to-many relationship with itself to account for the supervisor
role in the hierarchy of surveyors. The surveyor is responsible for one-to-many grids and grid
cells, and the surveyor finds one-to-many cultural or natural artifacts. The pit deposit has a one-
to-many relationship with the grid(s) that it contains and also has a one-to-many relationship
with the spit levels that are found within the pit deposit. Lastly, the grid cell has a many-to-many
relationship with the fossils that are found within it bounds. There may be multiple grids that
contain the same fossils and there may be multiple fossils in one grid. If the same fossil exists in
many grids, this is called a grid extension, which can be in the x- and/or y- direction. Grid
extensions are represented in the geodatabase by the attributes Artifact_ID_GridCellExt_X_FK
and Artifact_ID_GridCellExt_Y_FK.
There are no interrelationships between individual fossil specimens in this geodatabase
ERD because in practice, paleontologists at the Page Museum consider each fossil in a database
to be its own entity. If relationships between specimens require study, at present a researcher
may query the KE EMu database for specimens that occur within a certain distance from the

63
fossil specimen in question. The definition of a relationship between artifacts may be different
for each researcher, so it is best for the database to make no fossil relationship assumptions.
4.6 Chapter Summary
In summary, the work produced for this thesis project consisted of a geodatabase, a GIS
web application, and a prototype field data collection geodatabase. The following chapter will
describe how this work can be maintained and also discuss future work and considerations for
this project.

64
CHAPTER 5: DISCUSSION AND CONCLUSIONS
This chapter describes the maintenance considerations and future work for the La Brea Tar Pits
database and web GIS application. The author and her committee chair, Dr. Jennifer Swift met
with Aisling Farrell and Luis Chiappe, Director of the Dinosaur Institute at the Natural History
Museum of Los Angeles County, on August 27
th
2015 (Aisling Farrell and Luis Chiappe, per
comm, 2015) and discussed many of the points that informed this chapter. Section 5.1 describes
the long-term maintenance of the geodatabase and application. Section 5.2 discusses future work
including an envisioned museum exhibit, future field data collection opportunities, and 3D data
visualization potential. Lastly, section 5.3 provides final thoughts about furthering this thesis
work.
5.1 Long-Term Geodatabase and Application Maintenance
At present the application will be hosted on the USC GIST VM. It is anticipated that the Natural
History Museum will host the database and application at some time in the near future. In
Appendix N: Recommendations for Technology Transfer, the author provides recommendations
to Page Museum staff for the transfer of the geodatabase to an ArcGIS environment hosted by
the Natural History Museum. Also, recommendations for the skillset needed to carry out the
work will be similar to the required research skills possessed by the author as described in
Appendix A. The geodatabase, web GIS application and all associated files were backed up to
external media in anticipation of this future transfer.
Procedures written for the Page Museum staff for updating the database and application
are outlined in Chapter 4. The author may facilitate technology and knowledge transfer sessions
to the staff to assist in learning how to maintain the database. The La Brea Tar Pits 1913/1983
map feature classes are static for the foreseeable future. Nevertheless, if the museum uncovers

65
new deposits that need to be added to the paper survey map, the procedure for updating the
feature classes can be found in Chapter 4 as well.
Furthermore, it is anticipated that future USC GIST students will continue working on
this project. It is envisioned that the geodatabase will one day be directly connected live to the
KE EMu museum collections database. For example, a student intern or a consultant to the
museum could set up a database connection and stored procedures to push nightly updates of the
KE EMu database to the geodatabase. If this endeavor happens, the student or consultant can
refer to the SQL queries (as described in Appendix E: Geodatabase Creation Notes and SQL
Queries and Appendix F: SQL Queries for Specimen Summary Tables) to re-generate the tables
in the geodatabase.
5.2 Future Work
This section describes long-term aspirations for the evolution of this project. Section 5.2.1
discusses the possibility of this application evolving into a museum exhibit while section 5.2.2
explains how the geodatabase can be adapted in the future to support field data collection using a
mobile GPS collection device. Finally, section 5.2.3 discusses the potential of 3D GIS
visualization of fossils.
5.2.1 Interactive Museum Exhibit
This thesis project geodatabase design and web GIS application efforts will hopefully one day
evolve into a resource available to visitors of the Page Museum in the form of a hands-on
interactive public exhibit within the museum. This exhibit could have the distinction of being
updated every day to support the museum’s website which states that “discoveries are made
daily” (Page Museum 2015a). It is envisioned that the interactive map will be housed in a wall
with a large touch sensitive monitors to allow visitors to directly query different pit locations to

66
learn about the daily discoveries in the deposits. Also, it is imagined that a museum guest could
search for a pit and view interactive graphs of the most abundant types of fossils found.
5.2.2 Field Data Collection
There is a strong potential for adaptation of the geodatabase into a new schema to support field
data collection at excavation sites in Hancock Park. The geodatabase designed in this thesis
could be adapted to be used with a digital, spatial field data collection tool. For example, the
GPS data collection device could be a mobile phone using the ArcGIS Collector application
13
, or
it could be a more precise GPS collection device with sub-centimeter accuracy to precisely map
the location of fossils discovered, which would support mapping the elements found in a 3D
model (see section 5.2.3 for further discussion).
5.2.3 Three-dimensional (3D) Data Visualization
A small extract of data was used in the example 3D visualization in order to map the x, y, and z
(depth) of fossil elements found in Project 23 Deposit 1 for large cats. The methodology is
described in Chapter 3 and involved significant data preparation after the original data was
obtained from the KE EMu database. Future work will be necessary to develop a SQL query to
automate the transformation of the data fields to convert specimen location data to a format that
can be viewed in 3D in GIS. Additionally, the implementation of a new geodatabase design to
capture the x, y, and locations of elements found during future excavations would make the
visualization of elements in 3D space more easily automated without requiring significant
database transformation and queries.

13
Esri. 2015. “Collector for ArcGIS: Get Started.” Esri. Accessed August 22, 2015.
http://www.esri.com/software/arcgis/collector-for-arcgis/.

67
A benefit of 3D display is that it could enhance the capabilities of the La Brea Tar Pits
web GIS application and possible future museum exhibits by allowing visitors to see how fossil
deposits appear below the surface and to better comprehend the vast collection of fossils
contained in each pit. Currently, the web application contains simple hyperlinks to two 3D
videos of large cat specimens in Project 23, Deposit 1
14

15
.
It is desired by the museum staff that 3D data be incorporated into the web application as
live data in the future. Accordingly, the museum exhibit could be enhanced to allow the visitor to
select a fossil deposit location and zoom into the grid layout of the excavation site. Then the user
could choose to display, for instance, all mammals, predatory birds, or highlight specific species.
It is also hoped that previously scanned bone images could also be rendered within the 3D
visualization so that bones can be more easily identified, rather than viewing dots and lines as in
the current version of the 3D proof of concept visualization. This would allow the museum
visitors to truly see the remarkably abundant assortment of fossils that are found at each
excavation site.
5.3 Next Steps
To continue this project, it is recommended that the museum hire volunteers, student interns or
consultants with the same or similar qualifications as the author. In this way the new geodatabase
could continue to be developed and linked to the KE Emu database, and the 3D visualizations
could be implemented directly from the geodatabase. Lastly, the web GIS application could be
extended to include 3D visualizations as well as live web maps updated daily, which could also
become part of new, interactive, public exhibits at the museum.

14
Pham, K.P. 2015b. “P23 Box 1 Fluffy Video.” Accessed August 29, 2015. http://www-scf.usc.edu/
~kaceyjoh/LaBreaTarPits/P23_Box1_Fluffy_video.html/.

15
Pham, K.P. 2015c. “P23 Box 1 Smilodon by Age Video.” Accessed August 29, 2015. http://www-
scf.usc.edu/~kaceyjoh/LaBreaTarPits/P23_Box1_SmilodonbyAge_video.html/.

68
REFERENCES
Akersten, W. A., Shaw, C. A., and Jefferson, G. T. 1983. “Rancho La Brea: status and future.”
Paleobiology 9 (3): 211-217.

Behrensmeyer, A. K., and A. Turner. 2013. Fossilworks Paleobiology Database. Fossilworks.
http://fossilworks.org.

Carrasco, M.A., Kraatz, B.P., Davis, E.B., and Barnosky, A.D. 2005. “Miocene Mammal
Mapping Project (MIOMAP).” University of California Museum of Paleontology.
http://www.ucmp.berkeley.edu/miomap/

Coltrain, J. B., Harris, J. M., Cerling, T. E., Ehleringer, J. R., Dearing, M.D., Ward, J. 2004.
“Rancho La Brea stable isotope biogeochemistry and its implications for the
palaeoecology of late Pleistocene, coastal southern California.” Palaeogeography,
Palaeoclimatology, Palaeoecology 205: 199-219.

Deewan, C., and Jain, R. 2012. “The Agile Methodology.” International Journal of Computer
Science and Management Studies (3) 12: 26-29.

Friscia, A. R., Van Valkenburgh, B., Spencer, L., and Harris, J. 2008. “Chronology and spatial
distribution of large mammal bones in Pit 91, Rancho La Brea.” Palaios 23: 35-42.

International Medical Corps. 2015. “Who We Are.” Accessed August 29, 2015. https://
internationalmedicalcorps.org/who-we-are/.

Jones, Robert Wynn. 2006. Applied Palaeontology. Cambridge, UK; New York: Cambridge
University Press.

KE Software. 2015a. “Collections Management.” EMu. Accessed March 12, 2015. https://emu.
kesoftware.com/about-emu/overview/collections-management/.

KE Software. 2015b. “Our Clients.” EMu. Accessed March 12, 2015. https://emu.
kesoftware.com/about-emu/our-clients/client-lists/.

KE Software. 2015c. “Taxonomy.” EMu. Accessed March 12, 2015. https://emu.kesoftware.
com/downloads/EMu/documents/Taxonomy/Taxonomy_Letter.pdf/.

KE Software. 2015d. “ArcExplorer.” EMu. Accessed March 12, 2015. https://emu.kesoftware.
com/downloads/EMu/documents/ArcExplorer/ArcExplorer_IE_20100719.pdf/.

Milholland, Nancy Elizabeth. 2014. “Exploring San Francisco’s Treasures: Mashing Up Public
Art, Social Media, And Volunteered Geographic Information To Create A Dynamic
Guide” Master’s Thesis. University of Southern California.

69
Noble, I.B. 1913. “Asphalt Beds in the Rancho La Brea: Located on Lands known as the
‘Hancock Ranch’ Los Angeles County. California, U.S.A.,” survey map, Los Angeles
County Board of Surveyors. Revised 1983 by Akersten W., Tejado-Flores, A., Sells, R.,
and Sells, A.

O’Keefe, F.R., Fet E.V., and Harris, J.M 2009. “Compilation, Calibration, and Synthesis of
Faunal and Floral Radiocarbon Dates, Rancho La Brea, California.” Contributions in
Science 518: 1-15.

Page Museum. 2015a. Natural History Museum. Accessed August 20, 2015.
http://www.tarpits.org/.

Page Museum. 2015b. Natural History Museum. Accessed August 29, 2015.
http://www.tarpits.org/our-story/mission/.

Page Museum. 2015c. Natural History Museum. Accessed August 29, 2015.
http://www.tarpits.org/la-brea-tar-pits/timeline/.

Poole, B. 2005. “Tar Pit Fossils Lie Under the Radar; A high-tech search near the Page Museum
spots subterranean pockets of tar but no blips of bone.“ Los Angeles Times, 2 February:
B1.

Quinn, J.P., Ponti, D.J., Hillhouse, J.W., Powell, C.L. II, McDougall, K., Sarna-Wojcicki, A.M.,
Barron, J.A., and Fleck, R.J. 2000. “Quaternary Chronostratigraphic Constraints on
Deformation and Blind Fault Activity, Northern Los Angeles Basin.” Final Technical
Report 14340HQ-98-GR-00025.

Shaw, C. A. 1982. “Techniques used in Excavation, Preparation, and Curation of Fossils from
Rancho La Brea.” Curator: The Museum Journal (25) 1: 63-71.

Shaw, C. A., and Quinn, J. P. 1986. “Rancho La Brea: A Look at Coastal California's Past.”
California Geology June: 123-133.

Shipman, P. 1981. Life history of a fossil: An introduction to taphonomy and paleoecology.
Cambridge, MA: Harvard University Press.

Spencer, L. M., Van Valkenburgh, B., and Harris, J. M. 2003. “Taphonomic analysis of large
mammals recovered from the Pleistocene Rancho La Brea tar seeps.” Paleobiology 29
(4): 561-575.

Stock, C., and Harris, J. M. 1992. Rancho La Brea: a record of Pleistocene life in California (7th
Edition ed., Vols. Science Series, no. 37). Los Angeles, CA: Natural History Museum of
Los Angeles County.

Turner, R.D. 2006. “Archaeological and Paleontological Monitoring Report” LACMA
Transformation project, Los Angeles, California.

70

University of California. 2015. “MioMap: Miocene Mammal Mapping Project,” University of
California Museum of Paleontology, Interactive map. Assessed March 20, 2015,
http://www.ucmp.berkeley.edu/neomap/use.html/.

Woodard, G. D., and Marcus, L. F. 1973. “Rancho La Brea fossil deposits: a re-evaluation from
stratigraphic and geological evidence.” Journal of Paleontology 47 (1): 54-69.

71
APPENDIX A: REQUIRED RESEARCH SKILLS
This appendix contains an overview of the research skills possessed by the author of this study,
also necessary for those who will continue this work.
Table A-1 Required Research Skills

Proficiency
(novice, intermediate, expert)
Experience/Training
ArcGIS Server Intermediate Web GIS coursework; ArcGIS Server experience
SQL Server Intermediate A SQL Server geodatabase experience and access to SQL
subject matter experts
ArcGIS Desktop Expert Over 5 years experience with ArcGIS Desktop
ArcGIS API for
JavaScript
Novice/Intermediate Review of Lynda.com courses, Esri for Developers ArcGIS
for JavaScript reference guide; ArcGIS WebAppBuilder was
used so JavaScript development was not necessary
HTML & CSS Novice/Intermediate Review of Lynda.com courses, other online resources, access
to subject matter experts
Paleontology Intermediate Working knowledge from volunteering at the Page Museum,
access to subject matter experts

72
APPENDIX B: DATA SOURCES AND DESCRIPTIONS
This appendix contains a table of all datasets used in this thesis including data sources, contents,
preparation, size, representation, attributes, and accuracy.
Table B-1 Data Sources and Descriptions
Dataset Source Contents Preparation Size Represen
tation
Attributes/
Accuracy
(If relevant)
La Brea
Tar Pit
Locations
The dataset
describes the
locations of asphalt
fossil pits (i.e., the
tar pits) digitized
from a paper map of
the park. (Noble
1913)
Historical pit
locations from
1913.

Digitized in
ArcMap and
saved as
feature class
114
rows
Point and
Polygons
Pit Name, Bottom
elevation of bottom
of the pit (ft)
*Accuracy may be
off by 10 + ft due to
digitization error.
La Brea
Tar Pits
Pit Dating
O’Keefe et al.
(2009) Rancho La
Brea Dating.
Contributions in
Science, Number
518 (O’Keefe et al
2009)
Radiometric dates
of pits
Import table
from txt file
and relate to
location
layer by pit
number
43
rows
Table Pit, Age Range
Calibrated, Mean
Age Calibrated,
Carbon Years, St
Dev, Number of
Dates
Project 23
Pit
Locations
ArchaeoPaleo
Resource
Management Inc
report via Page
Museum (Turner
2006)
Project 23 pit
locations from
2006 parking lot
salvage
Display xy
events layer
in ArcMap
and saved as
feature class
23
rows
Point and
Polygons
Location name, lat,
long, fossil count,
fossil types
Water
Bodies
Paper survey map of
park from 1913
(Noble 1913)
Map of the water
body boundaries
digitized from the
survey map.

Digitized in
ArcMap and
saved as
feature class
6 rows Polygon Type (Lake or
Pond), Name, Water
Elevation (feet).
Creeks Paper survey map of
park from 1913
(Noble 1913)
Map of the creeks
digitized from the
survey map.
Digitized in
ArcMap and
saved as
feature class
4 rows Line Name
Oil and
gas vents
Paper survey map of
park from 1913
(Noble 1913)
Map of the oil
and gas vents
digitized from the
survey map.
Digitized in
ArcMap and
saved as
feature class
91
rows
Polygon Name
Contours Paper survey map of
park from 1913
(Noble 1913)
Elevation
contours in feet
(contour interval
= 2 ft)
Digitized in
ArcMap and
saved as
feature class
129
rows
Line Elevation

73
Dataset Source Contents Preparation Size Represen
tation
Attributes/
Accuracy
(If relevant)
Survey
Markers
Paper survey map of
park from 1913
(Noble 1913)
Survey points
from original
1913 survey
using stakes and
lathes, points are
18 ft apart,
elevation range is
154 -180 ft.
Digitized in
ArcMap and
saved as
feature class
568
rows
Point Elevation
Boreholes
/ Core data
USGS Geotechnical
Report (Quinn et al
2000)
Exploratory
boreholes – only
2 boreholes in the
Hancock Park
extent have
sediment
descriptions
Display xy
events layer
in ArcMap
and saved as
feature class
403
rows
Point Boring ID,
Investigator, Boring
Type (General
Geotechnical,
Geotechnical
Standard Penetration
Test (SPT), or
Geologic
Exploration),
Elevation (interval,
meters), Total Depth
(interval, meters),
Latitude, Longitude
Borehole
Sediment
Descriptio
ns
USGS Geotechnical
Report (Quinn et al
2000)
Sediment
descriptions by
depth in meters
for two USGS
boreholes within
Hancock Park
Import table
from txt file
and relate to
location
layer by
Boring ID
80
rows
Table Boring ID, From
depth (m), To Depth
(m), Color,
Description, Asphalt
Content
Fossil
x,y,z
location of
Smilodon
fatalis and
Panthera
atrox in
Box 1 of
Project 23
fossil
localities
Page Museum staff
provided extracts
from their KE EMu
specimen database
The x, y, z
coordinates of
where each
element of S.
fatalis or P. atrox
was found in a pit
to be represented
in 3D
Display x,y
events layer
with z value
in ArcMap
and saved as
feature class,
and edited in
ArcScene
253
rows
(S.
fatalis
); 161
rows
(P.
atrox)
3D Point Specimen Number,
Taxon (S. fatalis or
P. atrox),
Comments, Group
(hindlimb, forelimb,
etc), Element Part
(proximal, distal,
etc), element (fibula,
humerus, etc),
ontogenetic age
(adult, juv, etc),
North (y-direction in
cm), West (x-
direction in cm), BD
(z- direction in cm),
grid (A1, B2, etc),
Level (1,2,3,..)
*Grid surveyed:
accurate to the
centimeter

74
APPENDIX C: KE EMU DATA
This appendix contains the schemas of the KE EMu database extract obtained from the Page
Museum in June 2015.
Table C-1 KE EMu Extract - Ecatalogue
Ecatalogue.csv
Field Name Description Notes
ecatalogue_key
Unique ID for ecatalogue table from KE
EMu extract
PK (Combined two tables- this is PK of second
table)
Spec_No
First part Specimen ID or Catalogue
Number
Letter or # key denoting the catalog system and
collection
Spec_No_NoText
Second part Specimen ID or Catalogue
Number
Taxon
Taxonomical hierarchy (Taxon, Family,
Order, Class)
RLB_Point_Tab Point of bone
RLB_BD_Tab Depth of specimen pre-excavation
RLB_N_Tab Y-direction of specimen pre-excavation
RLB_W_Tab X-direction of specimen pre-excavation
RLB_BD_Orient_Tab Orientation of BD point measured
RLB_N_Orient_Tab Orientation of Y point measured
RLB_W_Orient_Tab Orientation of X point measured
RLB_BD_Ext_Tab Grid that BD measurement extends into
RLB_N_Ext_Tab Grid that Y measurement extends into
RLB_W_Ext_Tab Grid that X measurement extends into
Spec_Side Side of bone
RLB_Element Osteological element (type of bone)
SubElement Sub classification of bone
Part
Identifiable part of bone found to base
measurement on Ex. (Px=proximal, Dt=distal, etc)
Number Number of element For numbered bones such as vertebrae and toes
Frag Is the specimen a fragment?
OntogeneticAge Maturity of specimen
If the specimen be identified as juvenile, or
very juvenile

75
Table C-2 KE EMu Extract - SitSiteR
SitSiteR.csv
Field Name Description Notes
SitSiteRef_Key
Unique ID for SitSiteR table from KE EMu
extract Primary Key
ecatalogue_key
Unique ID for ecatalogue table from KE EMu
extract
Primary Key (Combined two tables- this is
PK of second table)
Site Locational information for the specimen Collection, Deposit, Field No, Grid, Level

76
APPENDIX D: FOSSIL POSITIONAL DATA FOR 3D DISPLAY
This appendix provides an example of a specimen record from the KE EMu database before and
after the author transformed one record to three records and shifted data values to flip the axis
and depth measurements. The acronyms used in the column names are RLB (Rancho La Brea),
BD (Below Depth), N (North), and W (West).

Table D-1 Example of fossil positional data before transforming for 3-dimensional display
Catalog
ue_No
RLB_Poin
t

RLB_
BD
RLBBD_Ext
ension
RLBN RLBW RLBWExte
nsion
RLBCoord
inates_tab
_all
RLBLevel
_all
Catalog
Number
Rancho La
Brea (RLB)
Specimen -
Point
Measured
on Bone
Below
Depth
Measu
rement
Below Depth
Measurement
Spit Level
Extension
Northing Westing Westing
Grid
Extension
Grid (1x1
m)
Spit Level
(25 cm
increments)
P23
16107
Dt Px RT 181
172
174
L7
L7
3.5
4
5.5
1
88.5
90.5
B-2 B-1 8

Table D-2 Example of fossil positional data after transforming for 3-dimensional display
Catalog
ue_No
RLB_
Point
RLB_
BD
RLBBD
_neg
RLBBD
Extensio
n
RLB
N
RLB
W
RLBW
_neg
RLB
WExt
ension
RLBCoordina
tes_tab_all
RLBL
evel_a
ll
P23
16107
Dt 181 -181

3.5 1 -1 B-2 B-1 8
P23
16107
Px 172 -172 L7 4 88.5 -88.5

B-1 8
P23
16107
RT 174 -174 L7 5.5 90.5 -90.5

B-1 8

77
APPENDIX E: GEODATABASE CREATION NOTES AND SQL QUERIES
This appendix documents the data processing steps and SQL queries used to create the
geodatabase described in section 3.3 Data Background and Processing Methods.

Processing in ArcMap:
• Merged P23 points from Archaeo Paleo report with HC Points from Survey map using
ArcMap.
Fields From P23
[OBJECTID]
,[Locality]
,[Latitude_N]
,[Longitude_]
,[UTM_Northi]
,[UTM_Eastin]
,[TopElev_m]
,[BottomElev_m]
,[TopElev_ft]
,[BottomElev_ft]
,[Notes]
,[Field_No]
,[P23_Deposit]
,[Shape]

Fields From HC
[OBJECTID]
,[LocalityID]
,[Name]
,[BottomElev_ft]
,[lat]
,[long]
,[Pit_No]
,[SHAPE]

Notes:
• Added Field [Bottom_Elev_m] used field calculator with expression [Bottom_Elev_m] =
[BottomElev]/3.2808
• Field Calculator Top_Elev_ft = TopElev_m *3.2808
• Add Column called “DataSource”, “DataComments” to capture the difference in
accuracy and data processing between the two tables
• Create SiteCollectionDepost field in point feature class for Join to
SiteCollectionDepositFK in table
• Add “Collection” field to specify whether “Project 23” or “Hancock Collection” in order
to draw by unique values category in ArcMap
• Export final table to SQL as LaBrea_FossilLocalities

78
Processed in MS Excel:
Received two extract tables from Museum staff in June 2015
Ecatalogue.csv (408,234 records)
ecatalogue_key
SpeSpecimenNo
SpeSpecimenNoText
Taxon -> Text to Columns in MS Excel to create (Taxon, Family, Order, Class)
SpeSide
RLBElement
RLBSubElement
RLBElementPart
RLBElementNo
RLBFragment
RLBOntogeneticAge
RLBPoint_tab
RLBBD_tab
RLBN_tab
RLBW_tab
RLBBDOrientation_tab
RLBNOrientation_tab
RLBWOrientation_tab
RLBBDExtension_tab
RLBNExtentsion_tab
RLBWExtension_tab

SitSiteR.csv (408,203 records)
SitSiteREf_Key
Ecataloguekey
Site -> Text to Columns in MS Excel to create ( Site_Collection, Site_Deposit, Site_Field
No, Site_Grid, Site_Level)

Processed in MS SQL:
• Imported ecatalogue.csv to SQL as Specimen_Catalogue and SitSiteR.csv as
Specimen_Site_Deposit
• Parsing values from site_collection and site_deposit, editing, and combining the columns to
create foreign key to match to location feature class
Query 1: Script to create table
USE TarPits_kaceyjoh
SELECT [SitSiteRef_key]
,[ecatalogue_key]
,[Site_Collection]
,[Site_Deposit]
,[Site_FieldNo]
,[Site_Grid]

79
,[Site_Level]
,[ObjectID]
INTO Specimen_Site_Deposit
FROM [TarPits_kaceyjoh].[dbo].[SiteSiteR]
-----------------------------------
ALTER TABLE [TarPits_kaceyjoh].[dbo].[Specimen_Site_Deposit]
ADD collection_short VARCHAR(5)
,deposit_short VARCHAR(50)
,SiteCollectionDepositFK VARCHAR(100)
-----------------------------------

Query 2: find all possible values for Site_Collection
SELECT [Site_Collection]
FROM [TarPits_kaceyjoh].[dbo].[Specimen_Site_Deposit]
Group by Site_Collection

Results:
Site_Collection
Pit 91 Collection
Birds-HC
Project 23 Collection
Shinenkan
Deposit General locality
Deposit 14
Deposit 91
Hancock Collection

Note:
• For purposes of GIS, all values that are not equal to Project 23 Collection will be changed
to HC in order to join to HC feature class. This avoids duplicate, stacked point locations
for collections that come from the same deposits.
USE TarPits_kaceyjoh
Update Specimen_Site_Deposit_new2
Set Collection_Short = 'HC'
Where Site_Collection <> 'Project 23 Collection’

USE TarPits_kaceyjoh
Update Specimen_Site_Deposit_new2
Set Collection_Short = ‘P23’
Where Site_Collection = 'Project 23 Collection’
Query to find all possible values for Site_Deposit
SELECT [Site_Deposit]
FROM [TarPits_kaceyjoh].[dbo].[Specimen_Site_Deposit]
Group by Site_Deposit

80
Notes:
• To insert contents of Site_Deposit into new column and replace deposit with ‘-‘ to result
in “-#”
• Note – Cannot map to pits or boxes by grid number because the same grid number can be
found in multiple deposits. Field number can be matched to deposit, but for purpose of
this project, field number values are not included in the query. The resultant column of
only “deposit –“ values has only 385 nulls. The majority are grid values that cannot be
parsed. With specific update statements, cleaned up the remaining values.
USE TarPits_kaceyjoh
Update Specimen_Site_Deposit
SET deposit_short = REPLACE(site_deposit, 'Deposit', ' - ')
WHERE site_deposit like '%Deposit%'
SELECT * from specimen_site_deposit_new2

USE TarPits_kaceyjoh
Update Specimen_Site_Deposit
SET deposit_short= replace(site_deposit, ' Field No. GJM295 ','- 91')
WHERE site_deposit like '%Field No. GJM295%'

USE TarPits_kaceyjoh
Update Specimen_Site_Deposit
SET deposit_short = replace(site_deposit, ' Field No. RLP914 ','- 91')
WHERE site_deposit like '% Field No. RLP914 %'

USE TarPits_kaceyjoh
Update Specimen_Site_Deposit
SET deposit_short = replace(site_deposit, ' Field No. GJM584 ','- 91')
WHERE site_deposit like '% Field No. GJM584 %'

USE TarPits_kaceyjoh
Update Specimen_Site_Deposit
SET deposit_short = replace(site_deposit, ' Field No. GJM596 ','- 91')
WHERE site_deposit like '% Field No. GJM596 %'

USE TarPits_kaceyjoh
Update Specimen_Site_Deposit
SET deposit _short= replace(site_deposit, ' Field No. GJM550 ','- 91')
WHERE site_deposit like '% Field No. GJM550 %'

USE TarPits_kaceyjoh
Update Specimen_Site_Deposit
SET deposit_short = replace(site_deposit, ' Field No. MJB060607 ','- 14')
WHERE site_deposit like '%Field No. MJB060607%'

USE TarPits_kaceyjoh
Update Specimen_Site_Deposit

81
SET deposit_short = replace(site_deposit, ' Field No. KOK060501 ','- 9’)
WHERE site_deposit like '% Field No. KOK060501%'
• There is no ‘KOK060501’ but there is ‘KOK060510’ which is Deposit 9
• Manually updated P23 box 14 values to ‘P23’ because site_collection value set to
“Deposit 14’ and query to shorten collection name to ‘HC’ or ‘P23’ defaulted non
‘Project 23’ values to “HC’
USE TarPits_kaceyjoh
Update Specimen_Site_Deposit
SET collection_short = 'P23'
WHERE sitsiteref_key = '406880' or sitsiteref_key = '406908' or sitsiteref_key = '406911'
-------------------------------------
Query 3: Script to create collection and deposit FK ID to match to point feature class of Tar
Pit deposits:
Use TarPits_kaceyjoh
update specimen_site_deposit
set SiteCollectionDepositFK= Collection_short+ Deposit_short
-------------------------------------

ArcMap Processing:
Query 4: Creation of Aggregate Tables in ArcMap
Joined Specimen_Catalogue and Specimen_Site_Deposit into
SPECIMEN_SITE_CATALOGUE because tables have almost the same amount of records (-31
nulls). In order to aggregate, the catalogue table required location information so joining by
ecatalogue_key with site_deposit table added the necessary location information.

82
APPENDIX F: SQL QUERIES FOR SPECIMEN SUMMARY TABLES
This appendix contains the queries used to create the geodatabase SQL summary tables
described in section 4.1 Data Model: Geodatabase Diagram and Schema.

Query 1: Select Counts of Taxonomic Classifications
/****** Script for Select Count of Taxon per Locality ******/
USE TarPits_kaceyjoh
SELECT SiteCollectionDepositFK, taxon, COUNT(taxon)as NumberofSpecimens
INTO TaxonCountByDeposit
FROM dbo.SPECIMEN_SITE_CATALOGUE
WHERE SiteCollectionDepositFK is NOT NULL
GROUP BY SiteCollectionDepositFK, Taxon
order by SiteCollectionDepositFK ;

/****** Script for Select Count of Class per Locality ******/
USE TarPits_kaceyjoh
SELECT SiteCollectionDepositFK, class, COUNT(class)as NumberofSpecimens
INTO ClassCountByDeposit
FROM dbo.SPECIMEN_SITE_CATALOGUE
WHERE SiteCollectionDepositFK is NOT NULL
GROUP BY SiteCollectionDepositFK, Class
order by SiteCollectionDepositFK ;

/****** Script for Select Count of Taxon by Element per Locality ******/
USE TarPits_kaceyjoh
SELECT SiteCollectionDepositFK, taxon,RLBElement, COUNT(RLBElement)as
countoftaxonbyelement
INTO TaxonByElement
FROM dbo.SPECIMEN_SITE_CATALOGUE
WHERE SiteCollectionDepositFK is NOT NULL
GROUP BY SiteCollectionDepositFK, Taxon,RLBElement
order by SiteCollectionDepositFK ;

/****** Script for Select Count of Specimens per Locality ******/
USE TarPits_kaceyjoh
SELECT SiteCollectionDepositFK, COUNT(ecatalogue_key)as NumberofSpecimens
INTO SpecimenCountByDeposit
FROM dbo.SPECIMEN_SITE_CATALOGUE
WHERE SiteCollectionDepositFK is NOT NULL
GROUP BY SiteCollectionDepositFK
order by SiteCollectionDepositFK ;

Notes:
• Register with gdb in arcmap after table creation (Adds an ObjectID)
• Clean up summary tables to remove nulls

83
• Add values to table and join to site_deposit FC (ie, NumberofSpecimens, Number of
Mammals, Most Abundant Taxon, etc)

/****** Script for Select Number of Mammals per Locality ******/
NumberofMammals,
USE TarPits_kaceyjoh
SELECT [SiteCollectionDepositFK]
,[class]
,[NumberofSpecimens] as NumberofMammals
,[ObjectID]
INTO MammaliaClassCountByDeposit
FROM dbo.ClassCountByDeposit
Where class like '%Mammalia%'

/****** Script for Select Number of Reptiles per Locality ******/
NumberofReptiles,
USE TarPits_kaceyjoh
SELECT [SiteCollectionDepositFK]
,[class]
,[NumberofSpecimens] as NumberofReptiles
,[ObjectID]
INTO ReptiliaClassCountByDeposit
FROM dbo.ClassCountByDeposit
Where class like '%Rep%'

/****** Script for Select Number of Birds per Locality ******/
NumberofBirds,
USE TarPits_kaceyjoh
SELECT [SiteCollectionDepositFK]
,[class]
,[NumberofSpecimens] as NumberofAves
,[ObjectID]
INTO AvesClassCountByDeposit
FROM dbo.ClassCountByDeposit
Where class like '%aves%'

/****** Script for Select Number of Amphibians per Locality ******/
USE TarPits_kaceyjoh
SELECT [SiteCollectionDepositFK]
,[class]
,[NumberofSpecimens] as NumberofAmphibians
,[ObjectID]
INTO AmphibiaClassCountByDeposit
FROM dbo.ClassCountByDeposit
Where class like '%amphi%'

84
/****** Script for Select Number of Insects per Locality ******/
USE TarPits_kaceyjoh
SELECT [SiteCollectionDepositFK]
,[class]
,[NumberofSpecimens] as NumberofInsects
,[ObjectID]
INTO InsectaClassCountByDeposit
FROM dbo.ClassCountByDeposit

/****** Script for Select Most Abundant Taxon per Locality ******/
USE TarPits_kaceyjoh
SELECT sitecollectiondepositfk, taxon, numberofspecimens
INTO MostAbundTaxonByDeposit
FROM TaxonCountByDeposit
WHERE numberofspecimens = (SELECT max(numberofspecimens)
FROM TaxonCountByDeposit as f
WHERE f.sitecollectiondepositfk = TaxonCountByDeposit.sitecollectiondepositfk);
Note:
• Some deposits with low values have repeat highest number of taxon bc taxon count = 1
for several taxons but for demo purposes, those low, repeated values will be ignored.

/****** Script for Select Most Abundant Taxon per Locality ******/
USE TarPits_kaceyjoh
SELECT[sitecollectiondepositfk]
,[taxon]
,[numberofspecimens]
INTO MostAbundTaxonByDepositGreaterthan5
FROM [TarPits_kaceyjoh].[dbo].[MostAbundTaxonByDeposit]
WHERE NumberofSpecimens > 5

/****** Script for Select Most Abundant Element by Taxon per Locality ******/
USE TarPits_kaceyjoh
SELECT sitecollectiondepositfk, taxon, RLBelement, countoftaxonbyelement
INTO MostAbundElementByTaxonByDeposit
FROM TaxonByElement
WHERE countoftaxonbyelement = (SELECT max(countoftaxonbyelement)
FROM TaxonByElement as f
WHERE f.sitecollectiondepositfk = TaxonByElement.sitecollectiondepositfk);

Note:
• some deposits with low values have repeat highest number of taxon bc taxon count = 1
for several taxons but for demo purposes, those low, repeated values will be ignored and
only numberofspecimens > 8 will be included in final table

85

Use TarPits_kaceyjoh
SELECT [sitecollectiondepositfk]
,[taxon]
,[RLBelement]
,[countoftaxonbyelement]
INTO MostAbundTaxonByElementGreaterthan8
FROM [TarPits_kaceyjoh].[dbo].[MostAbundElementByTaxonByDeposit]
WHERE countoftaxonbyelement > 8

/****** Script for Join All Temp Summary Tables Together By DepositID ******/
USE TarPits_kaceyjoh
SELECT MammaliaClassCountByDeposit.SiteCollectionDepositFK
,MammaliaClassCountByDeposit.NumberofMammals
,SpecimenCountbyDeposit.NumberofSpecimens
,SpecimenCountbyDeposit.SiteCollectionDepositFK as DepositID
INTO Temp1
FROM MammaliaClassCountByDeposit
RIGHT JOIN SpecimenCountbyDeposit
ON
MammaliaClassCountByDeposit.SiteCollectionDepositFK=SpecimenCountbyDeposit.SiteColle
ctionDepositFK

USE TarPits_kaceyjoh
SELECT ReptiliaClassCountByDeposit.SiteCollectionDepositFK
,Temp1.NumberofSpecimens
,Temp1.NumberofMammals
,ReptiliaClassCountByDeposit.NumberofReptiles
,Temp1.DepositID
INTO Temp2
FROM ReptiliaClassCountByDeposit
RIGHT JOIN TEMP1
ON ReptiliaClassCountbyDeposit.SiteCollectionDepositFK=Temp1.DepositID

USE TarPits_kaceyjoh
SELECT AvesClassCountByDeposit.SiteCollectionDepositFK
,Temp2.NumberofSpecimens
,Temp2.NumberofMammals
,Temp2.NumberofReptiles
,AvesClassCountByDeposit.NumberofAves as NumberofBirds
,Temp2.DepositID
INTO Temp3
FROM AvesClassCountByDeposit
RIGHT JOIN TEMP2
ON AvesClassCountByDeposit.SiteCollectionDepositFK=Temp2.DepositID

86
USE TarPits_kaceyjoh
SELECT AmphibiaClassCountByDeposit.SiteCollectionDepositFK
,Temp3.NumberofSpecimens
,Temp3.NumberofMammals
,Temp3.NumberofReptiles
,Temp3.NumberofBirds
,AmphibiaClassCountByDeposit.NumberofAmphibians
,Temp3.DepositID
INTO Temp4
FROM AmphibiaClassCountByDeposit
RIGHT JOIN TEMP3
ON AmphibiaClassCountByDeposit.SiteCollectionDepositFK=Temp3. DepositID

USE TarPits_kaceyjoh
SELECT InsectaClassCountByDeposit.SiteCollectionDepositFK
,Temp4.NumberofSpecimens
,Temp4.NumberofMammals
,Temp4.NumberofReptiles
,Temp4.NumberofBirds
,Temp4.NumberofAmphibians
,InsectaClassCountByDeposit.NumberofInsects
,Temp4.DepositID
INTO Temp5
FROM InsectaClassCountByDeposit
RIGHT JOIN TEMP4
ON InsectaClassCountByDeposit.SiteCollectionDepositFK=Temp4.DepositID
Order by NumberofInsects

USE TarPits_kaceyjoh
SELECT MostAbundTaxonByDepositGreaterthan5.SiteCollectionDepositFK
,Temp5.NumberofSpecimens
,Temp5.NumberofMammals
,Temp5.NumberofReptiles
,Temp5.NumberofBirds
,Temp5.NumberofAmphibians
,MostAbundTaxonByDepositGreaterthan5.Taxon as MostAbundTaxonWithGreaterthan5
,Temp5.DepositID
INTO Temp6
FROM MostAbundTaxonByDepositGreaterthan5
RIGHT JOIN TEMP5
ON MostAbundTaxonByDepositGreaterthan5.SiteCollectionDepositFK=Temp5.DepositID

USE TarPits_kaceyjoh
SELECT MostAbundTaxonByElementGreaterthan8.SiteCollectionDepositFK
,Temp6.NumberofSpecimens
,Temp6.NumberofMammals

87
,Temp6.NumberofReptiles
,Temp6.NumberofBirds
,Temp6.NumberofAmphibians
,Temp6.MostAbundTaxonWithGreaterthan5
,MostAbundTaxonByElementGreaterthan8.RLBelement as
MostAbundElementGreaterthan8
, MostAbundTaxonByElementGreaterthan8.Taxon as TaxonOfMostAbundElement
,Temp6.DepositID
INTO Temp7
FROM MostAbundTaxonByElementGreaterthan8
RIGHT JOIN TEMP6
ON MostAbundTaxonByElementGreaterthan8.SiteCollectionDepositFK=Temp6.DepositID

Query 2: JOIN TO LABREA_FOSSILLOCALITIES FC

USE TarPits_kaceyjoh
SELECT
LABREA_FOSSILLOCALITIES.OBJECTID
,LABREA_FOSSILLOCALITIES.SiteCollectionDeposit_PK
,LABREA_FOSSILLOCALITIES.Collection
,LABREA_FOSSILLOCALITIES.PitNo
,LABREA_FOSSILLOCALITIES.Name
,LABREA_FOSSILLOCALITIES.Locality
,LABREA_FOSSILLOCALITIES.Field_No
,LABREA_FOSSILLOCALITIES.Notes as P23Notes
,LABREA_FOSSILLOCALITIES.Bottom_Elev_m
,LABREA_FOSSILLOCALITIES.Bottom_Elev_ft
,LABREA_FOSSILLOCALITIES.TopElev_m
,LABREA_FOSSILLOCALITIES.Top_Elev_ft
,LABREA_FOSSILLOCALITIES.DataSource
,LABREA_FOSSILLOCALITIES.DataComments
,LABREA_FOSSILLOCALITIES.Shape
,Temp7.NumberofSpecimens
,Temp7.NumberofMammals
,Temp7.NumberofReptiles
,Temp7.NumberofBirds
,Temp7.NumberofAmphibians
,Temp7.MostAbundTaxonWithGreaterthan5
,Temp7. TaxonOfMostAbundElement
,Temp7.MostAbundElementGreaterthan8
,Temp7.DepositID
INTO LABREA_FOSSILLOCALITIES_SUMS_JOIN
FROM Temp7
RIGHT JOIN LABREA_FOSSILLOCALITIES
ON Temp7.DepositID=LABREA_FOSSILLOCALITIES.SiteCollectionDeposit_PK

88
ArcMap Processing:
• Added Columns for common names of taxon and elements
o MostAbundTaxGreat5CommonName
o MostAbundElemtGreat8CommonName
• Use field calculator to enter common names from Stock and Harris 1992
• Calculate common name for taxons and element

89
APPENDIX G: REQUIREMENTS FOR THE WEB GIS APPLICATION
This appendix lists and describes the recommended requirements and viewing mediums for this
project. Tables G-1 and G-2 detail the functional and nonfunctional requirements for each
medium (server/web system and web/mobile interface), respectively. Tables G-3 and G-4
provide the subsequent review the functional and nonfunctional requirements. Additionally, a
review of feedback received and changes requested can be found in Table G-5.

Table G-1 Functional Requirements
Requirement Description Viewing mediums
Web Mobile
Navigation The user will have the ability to pan and zoom the map X X
Search The user will have the ability to search by pit location, fossil type,
date, abundance
X X
View detail The user will have the ability to view further information about the
map contents and map functionality
X X
Show/hide layers The user will have the ability to show or hide layers using check
boxes
X X
Change basemap The user will have the ability to switch between the Esri Light
Gray, Esri Dark Gray, Esri Satellite, Esri Topographic, Esri World
Street Map, or a custom hillshade DEM created from the Page
Museum survey map from 1913
X X
Show/hide table The user will have he ability to show or hide feature tables X
Feature pop-ups The user will have the ability to click or touch the map to view
feature pop-ups
X X
Link to tarpits.org The user will have the ability to navigate to the Page Museum
home page for more information about the La Brea Tar Pits
X X
Charts The user will have the ability to view charts based on the number
of specimens found in the fossils deposits
X X
Source: Table adapted from Milholland (2014)
Table G-2 Nonfunctional Requirements
Requirement Description

Mediums
Server/Web
System
Web/Mobile
Interface
Display

The display extent will be zoomed to the Page Museum grounds at
Hancock Park in Los Angeles, CA
X
Individual specimen record will not be exposed as a feature service;
Data shall only be used in aggregate form via summary tables
X X
Customize
template for La
Brea Tar Pits
The template’s design will reflect the subject of paleontology X
The web application will be hosted on the USC GIST ArcGIS Server
environment
X
Security Users will have read-only access to feature layers X X
Administrator will have the ability to add, delete, or edit features X X
Source: Table adapted from Milholland (2014)

90
Table G-3 Functional Requirements Review
Source: Table adapted from Milholland (2014)
Table G-4 Nonfunctional Requirements Review
Source: Table adapted from Milholland (2014)

Requirement
met? Requirement Description
Viewing mediums
Web Mobile
Yes; as of
7/13/2015 Navigation The user will have the ability to pan and zoom the map X X
Yes; as of
8/10/2015 Search/ Query
The user will have the ability to search by pit location,
fossil type, date, abundance X X
Yes; as of
7/13/2015 View detail
The user will have the ability to view further information
about the map contents and map functionality X X
Yes; as of
7/13/2015 Show/hide layers
The user will have the ability to show or hide layers using
check boxes X X
Yes; as of
8/10/2015 Change basemap
The user will have the ability to switch between all
available Esri basemaps X X
Yes; as of
7/13/2015 Show/hide table
The user will have he ability to show or hide feature
tables X
Yes; as of
7/13/2015 Feature pop-ups
The user will have the ability to click or touch the map to
view feature pop-ups X X
Yes; as of
7/13/2015
Link to
tarpits.org
The user will have the ability to navigate to the Page
Museum home page for more information about the La
Brea Tar Pits X X
Yes; as of
8/10/2015 Charts
The user will have the ability to view charts based on the
number of specimens found in the fossils deposits X X
Requirement
met? Requirement Description
Mediums
Server/We
b System
Web/Mobile
Interface
Yes; as of
7/13/2015
Display
The display extent will be zoomed to the Page
Museum grounds at Hancock Park in Los
Angeles, CA X
Yes; as of
8/10/2015
Individual specimen record will not be exposed
as a feature service; Data shall only be used in
aggregate form via summary tables X X
Yes; as of
7/13/2015 Customize
template for La
Brea Tar Pits
The template’s design will reflect the subject of
paleontology X
Yes; as of
7/13/2015
The web application will be hosted on the USC
GIST ArcGIS Server environment X
Yes; as of
7/13/2015
Security
Users will have read-only access to feature layers X
X

Yes; as of
7/13/2015
Administrator will have the ability to add, delete,
or edit features X X

91
Table G-5 Feedback and Changes Requests - July 13, 2015 Review
Number Feedback/Change requested Fixed as of Date
1 Troubleshoot why video is not working in linked html pages from About
widget
8/10/2015
2 Confirm pop-up text – some attributes may not be necessary or do not make
sense in the context of the application
8/10/2015
3 Add basemaps selection widget 8/10/2015
4 Add source for O’Keefe and Quinn papers to About widget 8/10/2015
5 Make oil/gas vents feature brighter and easier to see on map 8/10/2015
6 Do not use mean age for pit dates – use calibrated age range instead 8/10/2015
7 Pit 10 should have dates associated with it 8/10/2015
8 Pit 3 and 4 are incorrectly showing no specimens associated with them 8/10/2015

92
APPENDIX H: WEB APPLICATION GIS SERVICES
The following table lists the layers that make up the feature services in the web GIS application
developed as part of this thesis work.

Service Layer ID Capabilities Database Feature Class/Table
Name
Description
La Brea Fossil
Localities
0 Select TarPits_kaceyjoh.DBO.LABR
EA_FOSSILLOCALITIES
The merged HC and P23 deposit
locations joined to the SumByDeposit
table (see Tables 4 & 7)
Boreholes w/
Sediment
Descriptions
1 Select TarPits_kaceyjoh.DBO.BORE
HOLES_USGS
Boreholes within the park with relate to
sediment description table
Boreholes 2 Select TarPits_kaceyjoh.DBO.BORE
HOLES
Boreholes within and surrounding the
park without sediment descriptions
Survey
Markers
3 Select TarPits_kaceyjoh.DBO.SURV
EYMARKERS
Survey markers digitized from the paper
survey map
Contours 4 Select TarPits_kaceyjoh.DBO.CONT
OURS
Contours digitized from the paper
survey map
Creek Center 5 Select TarPits_kaceyjoh.DBO.CREE
K_CENTER
Creeks digitized from the paper survey
map
Pit 6 Select TarPits_kaceyjoh.DBO.PIT Pit polygons digitized from the paper
survey map
Excavation
Site
7 Select TarPits_kaceyjoh.DBO.EXCA
VATION_SITE
Excavations site polygons digitized
from the paper survey map
Water Body 8 Select TarPits_kaceyjoh.DBO.WATE
R_BODY
Water bodies digitized from the paper
survey map
Gas / Oil
Vents
9 Select TarPits_kaceyjoh.DBO.GAS_
OIL_VENTS
Gas and oil vents digitized from the
paper survey map
Hancock
Collection Pit
Dates
10 Select TarPits_kaceyjoh.DBO.HC_PI
TDATES
Pit Dates Table for HC Deposits related
to LABREA_FOSSILLOCALITIES
feature service
USGS
Boreholes
Description
11 Select TarPits_kaceyjoh.DBO.USGS
BOREHOLESDESCRIPTION
USGS Borehole sediment descriptions
related to BOREHOLES_USGS feature
service
Source: Table adapted from Milholland (2014)

93
APPENDIX I: LA BREA TAR PITS GEODATABASE
The following tables (Table I-1 through Table I-7) describe the tables, fields, and relationship
classes in the La Brea Tar Pits geodatabase.

Table I-1 Relationship Class
RELATIONSHIP CLASS
Feature Class/Attribute Origin Table/Attribute Destination Relationship
LaBrea_FossilLocalities_Sum_Join/SiteCollectionDepositPK Grid_Cell_ID One-to-one
Boreholes_USGS/ Boring_ID USGSBoreholesDescription/Boring_ID One-to- many

Table I-2 LaBrea_FossilLocalities Entity

Table I-3 HC_PitDates
HANCOCK COLLECTION (HC) PIT DATES
Field Name Description Type Not Null Unique Notes
DepositID Deposit ID Text NotNull Unique Primary Key
NumberofDates Number of Dated Specimens Long Integer Null O’Keefe et al 2009
Max_Calibrated_Age Calibrated YBP min Long Integer Null O’Keefe et al 2009
Min_Calibrated_Age Calibrated YBP min Long Integer Null O’Keefe et al 2009
Source Supervisor Employee ID Foreign Key Text Null O’Keefe et al 2009
LA BREA FOSSIL LOCALITIES – Point Feature Class
Field Name Description Type Not Null Unique Notes
ObjectID Unique ID Object
ID

Not Null Unique Primary Key
SiteCollectionDeposit
PK
Pit / Deposit ID Text Null Unique Foreign Key
Pit_Name Pit Deposit Name Text Null Noble 1913
P23_Locality P23 Locality Name Text Null Turner 2006
Top_Elev_ft Top Elevation of P23 Locality
(calculated from meters)
Double Null Turner 2006
Bottom_Elev_ft Bottom Elevation of HC an P23
Deposits (calculated from meters)
Double Null Noble 1913, Turner
2006
Top_Elev_m Top Elevation of P23 Deposits Double Null Turner 2006
Bottom_Elev_m Bottom Elevation of HC (calculated
from feet) and P23 Deposits
Double Null Noble 2013, Turner
2006
DataSource Source of data Text Null Citations
DataComments Data creation notes Text Null

94
Table I-4 Specimen_Site_Catalogue Entity
SPECIMEN SITE CATALOGUE
Field Name Description Type Not Null Unique Notes
SitSiteRef_Key Unique ID for SitSiteR table
from KE EMu extract
Doub
le
NotNull Unique Primary Key
ecatalogue_key Unique ID for ecatalogue
table from KE EMu extract
Doub
le
NotNull Unique Primary Key (Combined two
tables- this is PK of second
table)
Site_Collection Collection that specimen
belongs to
Text Null From SitSiteR Table: Site ->
Text to Columns in MS Excel
to create (Site_Collection,
Site_Deposit, Site_FieldNo,
Site_Grid, Site_Level)
Site_Deposit Deposit ID Text Null *See notes for “Collection”
Site_FieldNo Filed Locality ID Text Null *See notes for “Collection”
Site_Grid Grid (Letter-Number) Text Null *See notes for “Collection”
Site_Level Levels (25 cm intervals) Text Null *See notes for “Collection”
Spec_No First part Specimen ID or
Catalogue Number
Text Null Letter or number key denoting
the catalog system and
collection
Spec_No_NoText Second part Specimen ID or
Catalogue Number
Doub
le
Null
Class Taxonomical hierarchy Text Null From ecatalogue table: Taxon
-> Text to Columns in MS
Excel to create (Taxon,
Family, Order, Class)
Order Taxonomical hierarchy Text Null *See notes for “Class”
Family Taxonomical hierarchy Text Null *See notes for “Class”
Taxon Taxonomical hierarchy
(Genus species)
Text Null *See notes for “Class”
RLB_Point_Tab Point of bone Text Null
RLB_BD_Tab Depth of specimen pre-
excavation
Text Null
RLB_N_Tab Y-direction of specimen pre-
excavation
Text Null
RLB_W_Tab X-direction of specimen pre-
excavation
Text Null
RLB_BD_Orient_Tab Orientation of BD point
measured
Text Null
RLB_N_Orient_Tab Orientation of Y point
measured
Text Null
RLB_W_Orient_Tab Orientation of X point
measured
Text Null
RLB_BD_Ext_Tab Grid that BD measurement
extends into
Text Null
RLB_N_Ext_Tab Grid that Y measurement
extends into
Text Null
RLB_W_Ext_Tab Grid that X measurement
extends into
Text Null
Spec_Side Side of bone Text Null
RLB_Element Osteological element Text Null type of bone

95
SPECIMEN SITE CATALOGUE
Field Name Description Type Not Null Unique Notes
SubElement Sub classification of bone Text Null
Part Identifiable part of bone
found to base measurement
on
Text Null Px=proximal, Dt=distal, etc
Number Number of element Text Null For numbered bones such as
vertebrae and toes
Frag Is the specimen a fragment? Text Null
OntogeneticAge Maturity of specimen Text Null If the specimen be identified
as juvenile, or very juvenile
SiteCollectionDepositF
K
Deposit ID Text Not Null Unique Foreign Key

Table I-5 SumbyDeposit (Joined to LaBrea_FossilLocalities Feature Class)
SUMMARY BY DEPOSIT
Field Name Description Type Not Null Unique Notes
Deposit_ID Deposit ID Text NotNull Unique Primary
Key
NumberofSpecimens Total count of specimens in
deposit
Long Integer Null
NumberofMammals Total count of mammals in
deposit
Long Integer Null
NumberofReptiles Total count of reptiles in deposit Long Integer Null
NumberofBirds Total count of birds in deposit Long Integer Null
NumberofAmphibians Total count of amphibians in
deposit
Long Integer Null
MostAbundTaxonWithG
reaterthan5
Most Abundant Species with > 5
Count
Text Null
MostAbundTaxGreat5_C
ommonName
Common Name of Most
Abundant Species with > 5
Count
Text Null
MostAbundElementGrea
terthan8
Most Abundant Element with >
8 Count
Text Null
MostAbundElemtGreat8
CommonName
Common Name of Most
Abundant Element with > 8
Count
Text Null
TaxonOfMostAbundEle
ment
Species w/ Most Abundant
Element with > 8 Count
Text Null
TaxonOfMostAbundEle
mentCommonNa
Common Name of Species w/
Most Abundant Element with >
8 Count
Text Null

96
Table I-6 Boreholes_USGS Entity
USGS Boreholes – Point Feature Class
Field Name Description Type Not Null Unique Notes
Boring_ID Boring ID Text NotNull Unique Primary Key
Lat Double NotNull Quinn et al 2000
Long Double NotNull Quinn et al 2000

Table I-7 USGSBoreholesDescription Entity
UGS Borehole Descriptions - Table
Field Name Description Type Not Null Unique Notes
Boring_ID Boring ID Text NotNull Unique Primary Key
From_m From depth in meters Double Null Quinn et al 2000
To_m To depth in meters Double Null Quinn et al 2000
Color Color of sediment Text Null Quinn et al 2000
Description Description of sediment Text Null Quinn et al 2000
Asphalt_Content Asphalt content Text Null Quinn et al 2000

97
APPENDIX J: LA BREA TAR PITS FIELD DATA COLLECTION GEODATABASE
The following tables (Table J-1 through Table J-9) describe the tables, fields, relationship
classes, topology, and domains, in the La Brea Tar Pits field data collection geodatabase
developed as part of this thesis work.

Table J-1 Topology
TOPOLOGY
Rule Feature Class/Attribute Description of Relationship
Must not overlap Grid Cell Grid cells must not overlap

Table J-2 Domains
DOMAINS
Domain Name Domain Type Domain Definitions Description
Element Coded Values rib, femur, radius, scapula, sacrum,
thoracic vertebra, cervical vertebra, tibia
C/, frontal, maxilla, metatarsal, vertebra,
...
Osteological element (type of bone)
Point Coded Values Px, Dt, Tub, FC, MC, LC ,RT, AC. PC,
NS, ...
Identifiable part of bone found to base
measurement on (Px=proximal, Dt=distal,
etc), e.g., the proximal end of the
humerus bone was found at location x,y
at depth z
Spit Coded Values 1, 2, 3, 4, ... , 9 Levels (25 cm intervals)

Table J-3 Relationship Class
RELATIONSHIP CLASS
Feature Class/Attribute Origin Feature Class/Attribute Destination Relationship
Specimen_ID Grid_Cell_ID One-to-many
Grid_Cell_ID Grid_ID One-to-one
Grid_Cell_ID Spit_ID One-to-many
Deposit_ID Gird_ID One-to-many
Employee_ID Specimen_ID One-to-many

98

Table J-4 Surveyor Entity
SURVEYOR
Field Name Description Type Not Null Unique Notes
EmployeeID Employee ID Text NotNull Unique Primary Key
Surveyor_Name Surveyor Name Text NotNull
Role Role Text NotNull
Supervisor Supervisor Text NotNull
SupervisorEmployeeID_FK Supervisor Employee ID
Foreign Key
Text Null
Surveyor_GridID_FK Surveyor Grid ID
Foreign Key
Text Null Unique Foreign Key
Surveyor_GridCellID_FK Surveyor Grid Cell ID
Foreign Key
Text Null Unique Foreign Key
Surveyor_ArtifactID_FK Surveyor Artifact ID
Foreign Key
Text Null Unique Foreign Key

Table J-5 Spit Entity
SPIT
Field Name Description Type Not Null Unique Notes
Spit_ID Strata ID Text NotNull Unique Primary Key
Spit Level (1,2,3,…) Strata Number Short
Integer
NotNull
Spit_Depth Strata Depth Short
Integer
NotNull
SpitID_GridCellID_FK Strata Type Text NotNull

Table J-6 Pit Deposit Entity
Pit_Deposit
Field Name Description Type Not Null Unique Notes
Deposit_ID Pit / Deposit ID Text NotNull Unique Primary Key
Deposit_Name Pit / Deposit Name Text NotNull
Deposit_Location Pit / Deposit Location Text NotNull
Latitude Latitude Long Integer Null
Longitude Longitude Long Integer Null
DepositID_Spit_ID_FK Deposit ID Spit ID
Foreign Key
Text Null Unique Foreign Key

99
Table J-7 Grid Entity
GRID
Field Name Description Type Not Null Unique Notes
Grid_ID Grid ID Text NotNull Unique Primary Key
No_Rows Number of Rows Short Integer NotNull
No_Columns Number of Columns Short Integer NotNull
Depth_at_Bottom Depth at Bottom Short Integer NotNull
GridID_DepositID_FK Grid ID Deposit ID Foreign
Key
Text Null Unique Foreign Key

Table J-8 Grid Cell Entity
GRID_CELL
Field Name Description Type Not Null Unique Notes
Grid_Cell_ID Grid Cell ID Text NotNull Unique Primary Key
Row_No Row Number Short
Integer
NotNull
Column_No Column Number Short
Integer
NotNull
GridCellID_GridID_FK Grid Cell ID Grid ID Foreign
Key
Text Null Unique Foreign Key

Table J-9 Fossil Element Entity
FOSSIL ELEMENT – (MAMMAL, BIRD, REPTILE, INSECT, AMPHIBIAN)
Field Name Description Type Not Null Unique Notes
Specimen_ID Artifact ID Text NotNull Unique Primary Key
Genus
Species Species Text NotNull
Element Bone Type Text NotNull
Point
Ontogenetic_Age Ontogenetic Age Text NotNull
Remarks Condition Text Null
Date_Found Date Found Date NotNull
Date_Catalogued Date Archived Date Null
Photograph Photograph BLOB Null
X (null allowed) X Long Integer Null
Y (null allowed) Y Long Integer Null
Z (null allowed) Z Long Integer Null
ArtifactID_GridID_FK Artifact ID and Grid ID
Foreign Key
Short Integer Null Unique Foreign Key
ArtifactID_GridCellD_FK Artifact ID and Grid Cell
ID Foreign Key
Short Integer Null Unique Foreign Key
ArtifactID_GridCellExt_X_FK Artifact ID and Grid Cell
ID Foreign Key
Short Integer Null Unique Foreign Key
ArtifactID_GridCellExt_Y_FK Artifact ID and Grid Cell
ID Foreign Key
Short Integer Null Unique Foreign Key

100
APPENDIX K: GEODATABASE UPDATE USER GUIDE
The following guide provides an outline of the steps necessary to manually update the La Brea
Tar Pits geodatabase created as part of this thesis work.
1. Use “Text to Column” in Microsoft Excel to parse the values in the records or fields and
columns by space. It is intended that this task will be updated to a SQL script in the future.
Microsoft Excel was used in the early stages in the development of the database while the
author gained experience in SQL query writing.
2. Import the Ecatalogue.csv as Specimen_Catalogue and SitSiteR.csv as
Specimen_Site_Deposit in SQL Server Management Studio as flat files.
3. Using the SQL queries in Appendix E Query 3, parse values from Site_Collection and
Site_Deposit in the Specimen_Site_Deposit, which will combine the columns to create a
foreign key to match the same attribute in the location feature class.
4. Insert contents of Site_Deposit into new column and replace deposit with ‘-‘ to result in “-#”.
Note that it is not possible to map pits or crates by grid number because the same grid
number can be found in multiple deposits. For example, the resultant column of only “deposit
–“ values has 385 nulls. The majority are grid values that cannot be parsed. With specific
update statements, the remaining values can be cleaned (see Appendix E).
5. In ArcMap join Specimen_Catalogue and Specimen_Site_Deposit into one table called
Specimen_Site_Catalogue. This prepares the table and adds all necessary fields needed to
perform the SQL summary queries.
6. In SQL Server Management Studio, follow the queries found in Appendix F to create the
temporary summary tables. Then join all temp summary tables together by Deposit ID.
Lastly, join to the LaBrea_FossilLocalities feature class.

101
7. Two procedures may be used to update the 1913/1983 survey map feature classes.
a. The first method is to copy to the feature class as a new name and edit the feature
class in ArcMap. A copy must be used because ArcGIS Server locks the original
published feature class. Then, for instance, a point can be copied and moved to the
desired location.
b. The second method is to add a table with the same schema as the feature class and
include the latitude and longitude in separate columns corresponding to the location
where each data (fossil or other) was originally found. Then, right click the table and
choose “display XY data” and save the resultant layer as a feature class and merge the
new feature class with a copy of the old feature class.

102
APPENDIX L: WEB GIS APPLICATION USER GUIDE
The following guide is designed to help the museum staff as well as other users navigate the web
GIS application and follow a typical user workflow.

1. The first step to using the application is to click on the information symbol in the left
panel to open the “About the Application” widget.
a. This widget provides a summary of the application, including data sources, data
methodology, fitness for research use, and links to the pilot project (example) dataset,
3D videos of Project 23 Box 1 Panthera atrox and Smilodon fatalis specimens.
2. Open the Legend widget to view the data layers and identifying symbology to become
familiar with the data displayed.
3. Open the Layer List widget to view all layers and have the ability to turn layers off and
on.
a. To expand the layers click on the small arrow next to the name of the map service
“La Brea Tar Pits June 2015 Database Extract” (Figure L-1).

Figure L-1 How to expand the Layer List
b. The layer names will now be displayed and you can turn them off and on by check
and unchecking the checkboxes next to the layer names (Figure L-2).

103

Figure L-2 The Layer List after expanding in Step 3a
4. Next, navigate the map by clicking and dragging the mouse to pan and use the + and – tool
to zoom in and out of the map, and the home tool to return to the original extent of the
map. Choose an area to zoom into (Figure L-3) and explore by double clicking on the map to
zoom in or by using the zoom icon

Figure L-3 Example zoomed in area of the map focusing on a cluster of pits

104
5. Click on icons on the map in order to open pop-up windows to display detailed information
about the points on the map (Figure L-4).
a. The orange and red polygon layers represent the perimeter of each pit and excavation
site as designated in the 1913 survey map. The orange and red areas have pop-ups
that contain the bottom elevation as recorded on the 1913 survey map (Figure L-4).

Figure L-4 Example pop-up contents for a pit perimeter showing the bottom elevation recorded
on the 1913 survey map
b. If several icons are layered on top of each other, all will be selected when clicking on
that location on the map. To scroll through to the next pop-up to see the desired map
feature, click the arrow pointing to the right on the top right of the pop-up (Figure
L-5).

Figure L-5 How to scroll to the next layer’s pop-up when several map layers are selected at once

105
c. Figure L-6 shows an example of the information contained in the pop-up for a fossil
deposit.

Figure L-6 Example pop-up contents for Hancock Collection deposit 3 (HC - 3)
d. Scroll down to see the complete contents of the pop-up (Figure L-7). Note that a
graph is also included that shows the distribution of taxonomical classes in the
selected deposit.
a. b.
Figure L-7 Example pop-up contents for Hancock Collection deposit 3 (HC - 3) in detail shown
the top of the pop-up contents (a) and the bottom of the pop-up contents (b)

106
6. In the pop-ups, the blue text “Show Related Records” can be clicked to open an attribute
table immediately below the map view, which will show radiometric dating for some HC pit
locations, and borehole sediment descriptions for select boreholes (Figure L-8).

Figure L-8 Example radiometric dating table appears after clicking “Show Related Records” for
Pit 3
a. Fields can be sorted in the attribute table by clicking the column names once to sort
ascending and twice to sort descending (Figure L-9).
a
b
Figure L-9 Sorting of Pit Dates by “Number of Dates” ascending (a) and descending (b) order

107
b. Clicking the down arrow will close the attribute table (Figure L-10).

Figure L-10 Closing the attribute table
7. To further explore the map’s capabilities, open the Query widget (Figure L-11). The
Query widget contains pre-defined queries that allow the user to:
a. search the map for a particular deposit,
b. search for the most abundant deposits,
c. search for a deposit by the most abundant species, and
d. search for a deposit by the most abundant fossil element.

Figure L-11 Queries
8. For example, to search for deposit # 4, click on “Search by Deposit” and click on the drop
down menu to see a list of all deposits. Or type a leading character into the field according to
the gray “hint” below the box that describes the character format of deposit numbers (HC - #
or P23 - #) (Figure L-12).

108

Figure L-12 Query example
9. Next, click “apply” and the query searches for deposit 4. When the results are returned, the
deposit will be highlighted in blue.
a. Click on the result or a pop-up that appears next to the highlighted deposit and click
on “Attributes from related table: Hancock Collection Pit Dates” to view radiometric
dates (note: this table will not appear for Project 23 deposits) (Figure L-13).

Figure L-13 Example query results and pop-up display results

109
10. The following is an overview of how to use the chart widget. First, choose the Charts widget
icon to explore pre-defined charts set up for the La Brea Tar Pits application.
a. The available charts are:
i. Number of Specimens by Deposit,
ii. Total Number of Mammals, Reptiles, Birds, and Amphibians by Deposit,
iii. Number of Mammals by Deposit,
iv. Number of Reptiles by Deposit,
v. Number of Birds by Deposit, and
vi. Number of Amphibians by Deposit.
11. Next, choose a chart from the list in the left side panel after opening the Chart Widget by
clicking the name of the chart or the arrow (Figure L-14).

Figure L-14 List of available charts
12. Then, choose “Apply” to generate the chart. After the chart is generated, all selected data
points in the chart will be highlighted on the map as yellow circles. The charts are
interactive; Click on the chart to view a specific chart data point, which will be panned to and
highlighted on the map as a red square outline (Figure L-15).

110

Figure L-15 Example Number of Specimens by Deposit chart with Deposit HC – 16 highlighted
in the chart and on the map

111
APPENDIX M: WEB GIS APPLICATION UPDATE USER GUIDE
The following guide is designed to help the museum staff to update the web GIS application
developed as part of this thesis work.

1. Open the web mapping application item “La Brea Tar Pits Map” in ArcGIS Online.
2. Click the down arrow in the blue box labeled “Open” and choose “Edit Application” and
the Web AppBuilder for ArcGIS editing interface will open.
a. The layout can be changed to several different themes and i.e. the color can be
changed within the Theme tab (Figure M-1).

Figure M-1 Web AppBuilder editing interface Theme Tab
3. By choosing the Map tab, options are presented that allow the user to choose a different
web map, change the extent of the map and customize the visible scales (Figure M-2).

112

Figure M-2 Web AppBuilder editing interface Map Tab
4. Within the Widget tab, all widgets can be edited and new widgets can be added (Figure
M-3).

Figure M-3 Web AppBuilder editing interface Widget Tab

113
5. For more information, please see the Web AppBuilder for ArcGIS user guide (Esri
2015e).
6. To view original widget settings, see Chapter 3, section 3.5.3 ArcGIS Online
Development.
7. Lastly, to change the logo, title, or to change/add URLs to the map, open the Attribute tab
(Figure M-4).

Figure M-4 Web AppBuilder editing interface Attribute Tab
For additional information, the most current version of Web AppBuilder online help
documentation should be accessed on the Esri.com website.

114
APPENDIX N: RECOMMENDATIONS FOR TECHNOLOGY TRANSFER
This appendix describes the process for the Page Museum to host this database and application in
their environment.

1. A server machine running at least Windows Server 2008 R2
2. Installation of ArcGIS Server 10.2.1 or later on the server machine
3. SQL Server installation on the ArcGIS Server machine and SQL Server Native Client
installation
4. ArcGIS Desktop installed on at least one machine
5. Backup media of mxds and SQL Server geodatabase will be provided by the author
6. These files should be transferred to the server machine
7. Restore SQL database using SQL Server Management Studio
* If museum staff desire a “live” connection from the SQL Server geodatabase to the KE
EMu database, a person with the experience listed in Appendix A will be required in order to
set up a connection between the two databases. It is recommended that this individual use the
SQL queries found in Appendix F: SQL Queries for Specimen Summary Tables, to create
stored procedures in SQL Server Management Studio, for example to run nightly updates
from the KE EMu database to update the La Brea Tar Pits geodatabase.

Abstract (if available)

Abstract The occurrence of asphaltic fossil localities within and surrounding the Page Museum at the La Brea Tar Pits in Los Angeles, California is extensive and has been recorded for decades as non-spatial data collected in a non-spatial database. The motivation for this project stemmed from the author’s time as a volunteer at the Page Museum over the course of one year. The Page museum staff requested an efficient way to cartographically display fossil data to assist staff with visualizing the taphonomy of fossils. At the time of this study, this thesis is the first GIS project that the Page Museum had ever supported for mapping of fossils. Most current literature describing fossil-related web GIS applications reports data displayed at small-scales, and exact locations of fossils are not generally provided through the applications. The main objectives of this thesis project were to design and implement a fossil excavation spatial database, digitally curate data that previously only existed in paper form, display fossil data in an interactive web GIS application, and develop a framework to support spatial analysis and live data feeds of fossil data in the future. As part of this thesis project, known fossil localities were digitized from a La Brea Tar Pits survey map maintained since 1913. The fossil specimen location records from the museum’s existing database were then joined to those newly digitized features to support the development of the spatial database of existing fossil localities within the park. The fossil features contained in the spatial database were then published to the web through the web GIS application also developed as part of thesis research, as a proof of concept intended to guide future Page Museum GIS projects. Visualizing the location of fossils is intended to help better communicate the paleontology of the La Brea Tar Pits to the museum staff, and eventually to the general public. Lastly, it is anticipated that this web GIS application will contribute to the current literature on documentation and visualization of extensive fossil deposits.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

3D fossil visualization and mapping of the La Brea Tar Pits, Los Angeles, California

PDF

A user study of GIS infused genealogy with dynamic thematic representation and spatiotemporal control

PDF

GIS-aided production of equipment locator maps for Metro rail maintenance and support

PDF

Design and implementation of an enterprise spatial raster management system

PDF

Social media canvassing using Twitter and Web GIS to aid in solving crime

PDF

Applying GIS to landscape irrigation systems: a case study of the Music Academy of the West campus in Montecito, CA

PDF

Generating trail conditions using user contributed data through a web application

PDF

Wake County District Overlay: an online electoral data visualization application

PDF

Semi-automated visualization of spatial information in unstructured text

PDF

The role of GIS in asset management: County of Kauai Department of Parks and Recreation a need for an asset management program

PDF

Visualizing email response data to improve marketing campaigns

PDF

Address points and a maser address file: improving efficiency in the city of Chino

PDF

A Web GIS application for airport pavement management

PDF

Geospatial web application development to access irrigation asset data: Veterans Affairs Palo Alto Health Care System

PDF

Comparative 3D geographic web server development: visualizing point clouds in the web

PDF

A unified geodatabase design for sinkhole inventories in the United States

PDF

Cal ToxTrack: a full stack Web GIS for mapping pollution in California

PDF

Finding your best-fit neighborhood: a Web GIS application for online residential property searches for Anchorage, Alaska

PDF

Assessing the impact of a web-based GIS application to promote earthquake preparation on the University of Southern California University Park Campus

PDF

Precipitation triggered landslide risk assessment and relative risk modeling using cached and real-time data

Asset Metadata

Creator Pham, Kacey Johnson (author)

Core Title GIS data curation and Web map application for La Brea Tar Pits fossil occurrences in Los Angeles, California

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Publication Date 09/18/2015

Defense Date 08/31/2015

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

Tag asphaltic fossil,data curation,fossil deposit,geodatabase,geographic information system,GIS,La Brea Tar Pits,Los Angeles,OAI-PMH Harvest,Page Museum,paleontology,Web map application

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Swift, Jennifer (committee chair), Chiang, Yao-Yi (committee member), Kemp, Karen (committee member)

Creator Email kaceyjoh@usc.edu,kaceyjpham@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-185662

Unique identifier UC11274486

Identifier etd-PhamKaceyJ-3936.pdf (filename),usctheses-c40-185662 (legacy record id)

Legacy Identifier etd-PhamKaceyJ-3936.pdf

Dmrecord 185662

Document Type Thesis

Format application/pdf (imt)

Rights Pham, Kacey Johnson

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