The Tijuana Historical Ecology Project

Marianne Jara

California State University, Northridge

May 2013 - August 2013

Dr. Shawna Dark

Center for Geographical Studies, California State

University Northridge

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Table of Contents . . . . . . . . . . . . . . . . . . . … . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pg. 1

Executive Summary . . . . . . . . . . . . . . . . . . . … . . . . . . . . . . . . . . . . . . . . . . . . . . . .Pg. 2

Mission Statements and Role of Both SFEI and CGS . . . . . . . . . . . . . . . Pg. 2

Acknowledgements . . . . . . . . . . . . . . . . . . . … . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pg. 2

Project Approach . . . . . . . . . . . . . . . . . . . … . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pg. 3

Part One –Georeferencing . . . . . . . . . . . . . . . . . . . … . . . . . . . . . . . . . . . . . Pg. 3

Part Two – Creating the GLO . . . . . . . . . . . . . . . . . . . … . . . . . . . . . . . . . . Pg. 6

Phase 1 . . . . . . . . . . . . . . . . . . . … . . . . . . . . . . . . . . . . . . . . . . . . . . . Pg. 7

Phase 2 . . . . . . . . . . . . . . . . . . . … . . . . . . . . . . . . . . . . . . . . . . . . . . . Pg. 8

Project Outcome . . . . . . . . . . . . . . . . . . . … . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pg. 9

Conclusion . . . . . . . . . . . . . . . . . . … . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pg. 9

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The Tijuana Historical Ecology Project - Executive Summary

The San Francisco Estuarine Institute (SFEI) consulted with the Center of Geographical

Studies (CGS) of California State University Northridge to update and create historical and

ecological data using Geographic Information System (GIS). The project called for data to be

updated, make analog information into a cartographically visual product. The original data and

surveys are coming from the General Land Office (GLO). The content for this project included

historical topographic maps and surveys conducted between 1785 until 1910 in the Baja

California and Tijuana region. There was a request for these items to be spatially updated, per

SFEI. This report includes specifics of the Tijuana Historical Ecology Project, a project that

occurred between May through July of 2013. Project managers at the CGS coordinated tasks and

distributed them with the interns. As one of the GIS interns for the Center for Geographical

studies, under an internship grant for Water Resource Institute, my responsibility for this project

was to help create and update spatial information using GIS (Geographic Information System)

based on raw, historical data of surveys conducted in the bordering area of Tijuana, Baja

California.

Mission Statements and Role of Both SFEI and CGS

According to the San Francisco Estuarine Institute (SFEI), their purpose is to, “Help

define environmental problems, advance public debate about them through sound science, and

support consensus-based solutions that improve environmental planning, management, and

policy development.” (www.sfei.org/about). For this project, they contracted the Center for

Geographical Studies in Northridge, CA in order to help create a GIS for an ecological area of

interest (AOI).

The Center of Geographical Studies is housed at California State University, Northridge.

The center’s website concludes that the CGS “is an interdisciplinary research center focused on

applications, education, and innovative solutions to real world problems using Geographic

Information Systems (GIS).” (www.csun.edu/~centergs). The CGS works with institutions such

as the SFEI, by helping create or improvise geographic information.

Acknowledgements

I would like to thank the following members under the Center for Geographical Studies

here at California State University, Northridge. First of all I would like to thank Dr. Shawn Dark

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for approaching me about the Water Resources Institute opportunity that was available, and for

promoting opportunities for under-represented students like myself. The project managers and

research assistants, in no particular order: Danielle Bram and Patricia Pendleton for organizing

these projects, allowing interns and eager students like myself getting hands-on experience in

GIS around our school schedules, and for allowing us the experience that has been very valuable

outside the classroom setting. I would really like to thank Joel Osuna for guiding me and other

interns in this specific project, and for sharing his knowledge with all of us. I would also like to

thank the other two interns, Dominic Kovacs and Andrea Rodriguez for working together as an

efficient and collaborative team towards the project. Working on this project throughout the

summer has been a very rewarding experience, allowing me to strengthen my skills in GIS and

working with a team.

Outside of the location of where the project took place, I would absolutely like to thank

the administrators at the Water Resources Institute for having their lines of communication open,

and for ensuring that interns like myself were being taken care of, specifically Julie Lappin for

being so attentive despite the remoteness of the situation. I am very grateful that WRI was able

to provide this kind of opportunity to students like myself, as it has promoted and encouraged me

to pursue a career in the spatial sciences more.

Project Approach

Part One – Georeferencing

Georeferencing maps into a GIS allows for digital maps to have spatial reference, when

they have not been assigned one. The SFEI provided a total of six historical topographic maps

from the General Land Office, to be overlayed onto an existing ESRI basemap that included the

requested and appropriate projection and spatial reference.

There was criteria implemented in order to appropriately georeference these historical

maps to the best of abilities. Control points were used to determine how to spatially reference

these maps, a process that was determined by using significant features that were likely not

changed from the date of the historical document (historical map) to contemporary times (current

ESRI basemap). These features included latitude/longitude intersections visible in maps,

mountain peaks, major streets intersections, and land grant/township lines. A minimum of ten

control points were required for each map. Each control point determines a residual, and all

points accumulated are determined by an RMS (root mean square) error. This value can range

anywhere from 0 to up 1,000. It is preferred that the RMS error has a lower value, because it

determines a more accurate spatial reference. The lower the residual is at each point, the lower

the RMS Error comes out, therefore the more accurate the georeference comes out to be. Figure

1 shows how the link table displays elements of what is taken in account when georeferencing.

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Figure 1 - Picture of tool interface used to determine georeferencing, link table.

This process did present some challenges, as it was difficult to determine which feature

suited the best point to reference. Major intersections were likely to be altered between those

dates to contemporary times. The scales of the topographic maps were also an issue, not all the

same: some were at 7.5 meters scales, some were 15 meters scales. The difference in scale

definitely made a huge impact the accuracy of spatial referencing, as the larger-scaled maps

covered a large extent. The six topographical maps that were georeferenced were from 1903,

1904, 1930, 1943, 1950, and 1967.

Figure 2 is a full image of one of the topographic maps. This one in particular is of

Cuyamaca, California during 1903, located on the eastern side of San Diego County and is a

bordering region. This particular map shows areas such as the San Ysidro Mountains around the

bordering area, both land grants of Otay, and various valleys throughout. This is one of the six

topographic maps that were georeferenced.

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Figure 2 – Georeferenced map of Cuyamaca, a region east of San Diego, border of California and Mexico.

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Part 2 – Creating the GLO (Phase 1 and 2)

Study area of interest is located between Baja California, along Southern California and

Mexico border, southwestern United States. Surveyors used an analog method to measure

townships: links from chains as units of measurement, used by the General Land Office

surveyors, from one point or section to another. These points, or identification, where surveyors

documented were indicative of the points and data we were to create. The method of

measurement per township was based off of links. Basically, 8,000 links made up one section

line that ended in section corners. Corner sections were made up of 4,000 links. The surveyor

would also input non-uniform points in between based on natural descriptions, set marks, or

spatial limits. Spatial limits included private property, natural features, and political boundaries.

With the help of a project supervisor and two other interns, we were to interpret historical,

handwritten surveys. Another set of interns interpreted the handwritten surveys, labeling this set

of work as Textual Data. The team of interns in which I worked with was assigned to also

interpret the handwritten surveys, along with integrating it into the GIS (labeled as Data Entry at

the CGS). The surveys in which I personally worked with were three, two which were conducted

and documented in 1869, and one in 1881. Surveys were taken avoiding land grants,

concentrating on observing and documenting township areas.

The SFEI and the Center for Geographical studies provided a set of keywords in which

the interns and project managers were to identify and assign to each point based on codes. These

codes and keywords were to be integrated into the data entry of the point system. One of the

most significant identifications was that each point was given a point type. There were four point

types: H (human made feature), N (natural feature), L (line tree feature), and P (section corners,

quarter sections of townships, and meander corners). Other keywords included during the data

entry included codes of species, natural features, tree bearings, plant species, vegetation type and

excerpts based on descriptions of the surveys, or the lack of.

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Figure 3 – Township Layout.

As shown in Figure 3 (and can later be seen in Figure 5’s final map), townships are areas

that display equal-area squares, whereas land grants are the oddly-shaped polygons. The General

Land Office surveyors were able to determine the proper equal area of townships, in order to

remain consistency with the spatial method. Land grants were areas that were not accessible

and/or not assigned to survey, as only land that was assigned as townships were to be surveyed.

Phase 1: Creating the GLO Surveys into the GIS

The data that was prepared to work with were of the following: vector data of the study

area boundary, PLSA shapefiles of California, Section and land grant lines, and data tables to

populate data. Symbolization of PLSA shapefiles, section and land grant lines were necessary.

Symbolization of these datasets was important, as it provided referential information while

creating geospatial data. The program ArcGIS, a product of ESRI, was primarily used to create

the historical mapping and survey information into a GIS.

A student-created GLO Data Entry Form (Fig. 4) on ArcMap was a tool used in order to

create, reference, and submit data from the surveys into vector data. The form was an efficient

way to enter all the data the historical surveys provided: dates, page of survey, name of

surveyors, number of links, direction, and point type. This data entry form was used with

linework in order to reference created points, and place them accurately along the lines, based on

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the given measure values. It was vital that we double checked our assigned townships based on

the line identification and the direction in which we were integrating the historical data into the

GIS. Flagged entries and excerpts were also important to note. They would come useful for the

second phase of the project. This data entry form was the tool that helped create the vector points

and populate the attribute information per each point.

Figure 3 - Example of GLO Data Entry tool

Phase 2: Quality Control

After the creation of the GLO was completed; the next phase was for each intern to work

with the quality control of the data interpretation into the GIS. Each intern was to review each

township that they did not work on creating, for any errors. This meant reviewing flagged items,

updating keywords, checking to see if their spatial reference was correct, direction, etc. The

quality control portion also used those very same original survey notes as well as textual data to

ensure accurate data entry.

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Project Outcome

The creation of the historical data into a GIS was solely for the SFEI. Analysis was not

particularly assigned to the Center for Geographical Studies at CSUN to conduct, but the process

involving data entry involved critical thinking. For the six historical georeferenced maps, there

was a need for them to have a spatial reference. The maps were scanned from a physical USGS

map into a digital format (.tiff files, in particular). Adding them into a GIS and giving them the

spatial reference will allow GIS users at SFEI to be able to open them into a GIS with the ability

for the historical maps to be displayed based on their new spatial properties. Items that are not

spatially referenced, for example, are sometimes not useful or can get lost in a GIS interface. It

was important to create a vector dataset from a historical GLO dataset because it was possible to

implement the survey properties from the old surveys into tabular data. The interns at CGS were

responsible to determine the classification of each point based on survey descriptions. Project

managers and SFEI worked together to create certain guidelines and criteria in order to properly

identify each point for the sake of the GLO in GIS form.

This will allow GIS users at SFEI to look up properties of the locations, possibly in

accordance to the ecological study area. This data creation also allowed for the surveys to be

both digitally illustrated and updated into a digital database. It allows for data to be illustrated

visually and displayed in an informative way, something that can help the SFEI understand their

area of interest in another light. SFEI will likely find useful any point information that includes

ecological data.

Conclusion

The Tijuana Historical Ecology Project was intended to help the San Francisco Estuarine

Institute map out the areas where surveyors conducted descriptions of the Tijuana area. The

creation and interpretation of vector point data helps illustrate what surveyors were describing

and analyzing into a GIS. The Center for Geographical Studies made it possible to integrate most

of this historical data into a modern geodatabase. Analyzing the data was not part of the project

tasks for the Center of Geographical Studies to conduct. Instead, it was simply to update the data

for the SFEI themselves to interpret or analyze. Figure 5 is an illustration of a final map that

shows many of the elements that were created to map out area where the historical surveys took

place.

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Figure 4 - Final map.